JNDE - March 2018

March 2018 Volume 16 Issue 12

D IMAGING & INFRARED IMAGING & INFRARED IMAGING & LICATIONS

IT’S APPLICATIONS IT’S APPLICATIONS D IMAGING &

LICATIONS

INFRARED IMAGING &

D IMAGING & PLICATIO

ING & ONS

ING & TIONS

NFRAR T’S AP

GING & TIONS

FRARED IMAGING & INFRARED IMAGING & IT’S APPLICATIONS IT’S APPLICATIONS

INFR IT’S

MARCH 2018 Volume 16 - Issue 12

17 - Vibrothermography for Detection of Disbonds in Composite Plates Based on Viscoelastic Loss - A Statistical Approach to Subsurface Defect Detection and Evaluation for Frequency Modulated Thermal Wave Imaging - Application of Electro-thermal Approach for Non-Destructive Evaluation of Materials and Structures

by Infrared Thermography - An Aperiodic Digitized Frequency Modulated Thermal Wave Imaging for Testing and Evaluation of Carbon Fibre Reinforced Polymer Materials

51

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LETTERS

3

PRESIDENT At the onset, I thank members for honouring me with the important position as the President of our Society. ISNT has come a long way & made considerable progress in the areas of Training, Certification, Technical Publication, Skill Development, International Interaction & Membership Development.

During my tenure, we will put in our best efforts to build our ISNT working team & take our society to greater heights.

Last December, we completed yet another successful National Conference & annual NDE event. The NDE has been inviting International participation & is now of a stature of an International Event. Peek into the exclusive event highlights put together on Events page.

I welcome our members to share their views & suggestions on how we can serve better. Do write to us & we assure you, your feedback will be carefully actioned.

R.J.PARDIKAR r.j.pardikar@gmail.com

/ CHIEF EDITOR Beginning a brand new year with new beginnings ! Over the past few months, ISNT appointed a new team of Governing Council, focusing further on the smooth & successful functioning of the Society. I once again take this opportunity to welcome President Shri R.J. Pardikar, President Elect Dr. B. Venkatraman, Vice Presidents Shri Diwakar D. Joshi, Dr. Krishnan Balasubramaniam & Shri Jaiteerth Joshi, Hon. Gen. Secretary Shri P. Mohan, Hon. Jt. Secretary Shri Samir K. Choksi & Shri Bikash Ghose, Treasurer Shri K.A. Nerurkar & all elected members of the society for 2018-19.

Meanwhile, this year we have listed topics for each issue of JNDE. The coverage is rooted in a set of defining obsessions: core topics of importance for every ndt professional, from the ‘Industrial Inspection using Optics & Laser’, ‘NDE in Clinical Diagnosis’ to ‘Training & Certification in NDE’, besides the current ‘Infrared Imaging & it’s Applications’ issue in your hand. These are the issues that will energize your need for knowledge and we invite you to obsess about them along with us.

I am pleased & humbled at the overwhelming response we have been receiving from old as well as new advertisers for JNDE. Your continued support encourages us in further uplifting JNDE’s standard & enabling it to be a self sustaining one. I wish to thank you all for extending support, likewise anticipative of your patronage year after year.

We are aiming to expand the scope of JNDE by including topic reviews/discussions, employment opportunities and conference announcements, will appreciate your participation by way of matter contribution which we may feature in the JNDE.

RAJUL PARIKH Managing Editor rajulparikh@eecindia.com

DR. KRISHNAN BALASUBRAMANIAM Chief Editor balas@iitm.ac.in

March 2018

4

LETTERS

Infrared imaging plays a vital role in the field of non-destructive testing and evaluation of various materials. It has gained a wide acceptance in industrial quality control, especially in the community of non-destructive testing and evaluation due to its full-field, remote, safe, fast and quantitative inspection capabilities. Safe, fast and remote screening capabilities makes this technique to widen its applicability not only in non-destructive testing and evaluation community but also to other fields of specializations such as bio-medical, food processing, agriculture etc..

The aim of this special issue is to provide a snap-shot of state-of-the-art research from wide selection of key researchers involved in this area from various esteemed academic and research institutes. Contributors have also been encouraged to submit high standard relevant articles that act as a review of their own work upto the current boundaries of their focused research specialization.

I am sure that the readers will find the Infrared Imaging & it's Applications special issue as highly informative and precious reference. Finally I would like to thank each and individual contributing authors for providing their valuable piece of research work to this special issue. It has been a great pleasure for us to prepare a special issue on Infrared Imaging & it's Applications, and we hope that you will enjoy its reading as much as we enjoyed its preparation.

DR. RAVIBABU MULAVEESALA ravi.iitrpr.ac.in

March 2018

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Pune Chapter Shri. Mandar Vinze (Chairman) Plot No-16, Survey No-17/B Kothrud Industrial Area, Kothrud, Pune - 411038 Ph: 91-83083938151 (M) / 020 25459526 mandarvinze@gmail.com / chairman@isntpune.org.in

March 2018

BENGALURU: DECEMBER 2017 - FEBRUARY 2018 Ÿ 5th January 2018 - Conducted EC meeting. Attended by 10 EC members. Ÿ 5th January 2018 - ISNT Bengaluru chapter conducted following three Technical talks in series. 25 Members attended.

- Topic 1: Fast work approach in NDE Research - An experience of Industry and Research Institute in collaborative effort.

Speaker was Shri.V.Manoharan. GE Global research.

Bangalore.

- Topic 2: Computed tomography (CT) A powerful tool for defect characterization in Aerospace composite liners.

Speaker was Shri.Prasad Thapa. GE Global research.

Bangalore. He received best paper award - NDE 2017 at the NDE Seminar, Chennai.

- Topic 3: Evaluation of stages of fatigue damages in riveted joints with Acoustic Emission approximate entropy approach. Speaker was Shri.Kalyana Sundaram. CSIR-NAL, Bangalore.

Participants at the technical talks

PUNE : SEPTEMBER 2017 - DECEMBER 2017 Pune chapter received Best Chapter Award for the year 2017.

The award was received by the Chairman, ISNT Pune Chapter during inaugural function of NDE-2017 held on 14th Dec 2017 at Chennai. The award was received from the hand of Shri Banwarilal Purohit, Governor of Tamilnadu. Ÿ 22nd Dec 2017 - One day Faculty Development Programme on the topic of "Non-Destructive Testing" was conducted at PVPIT, Budhgaon, Sangli. About 84 faculties from engineering colleges from 5 different states had attended the programme. Ÿ

ISNT Pune Chapter Chairman receiving Best Chapter Award 2017

Shri Chintamani Khade delivering the evening talk

March 2018

The programme was conducted by Shri Mandar Vinze, Shri Sunil Gophan and Shri Kalesh Nerurkar. Ÿ Evening Technical Talk of 2 hours for members on "Leak Testing: Basics and Advances" under Back to Basic Series was conducted at Hotel Kohinoor Executive, Apte Road, Pune. Talk was delivered by Shri Chintamani Khade. Ÿ 3rd Feb 2018 - Interaction meeting of NCB / NGC members from Pune and Pune Chapter EC with President ISNT, Shri R J Pardikar was held at Pune Chapter Office. Ÿ EC meeting of Pune Chapter was conducted on 9th Dec 2017, 13th Jan 2018 and 10th Feb 2018.

Pune Chapter team with the Best Chapter award

Shri. K. Nerurkar delivering talk in the programme

Chairman Elect Shri Sunil Gophan welcoming President ISNT Shri R J Pardikar

Team of NGC/NCB members from Pune and EC members

9

VADODARA : SEPTEMBER 2017 - JANUARY 2018 Ÿ 30th December 2017 - Evening lecture was organized on “Semi Automated and Automated Ultrasonic Examination of Welds in Pressure Vessels and Piping in Lieu of Radiography” by Shri R. Venkatasubramanian, Shri Krutik Shah and Shri Kashyap Bhatt. Ÿ A Presentation on “ARJUN – The digital ultrasonic flaw detector” designed, developed and manufactured by M/s.

Modsonic Instrument Manufacturing Company Pvt. Ltd., Ahmedabad was done by Shri Deepak Parikh and his team. Ÿ Total 46 members and invited guests have attended the evening lecture. All participated members appreciated the evening lecture. Ÿ 23rd and 24th September 2017 - NGC / NCB / Chapter Chairman meet was attended by Shri R. Venkatasubramanian (Hon. Chairman), Shri Prashant Barodia (Hon. Vice Chairman) at Bangalore. Ÿ 15th and 16th December 2017 - Five Executive commitee

members & few other members from Vadodara attended NDE 2017 seminar at Chennai . Ÿ

15 December 2017 - Five Executive Committee members attended the AGM held at Chennai during National Seminar – NDE 2017.

Ÿ

24th December 2017 - ISNT Vadodara Chapter’s Executive Committee Meeting. Fourth Executive Committee Meeting of ISNT Vadodara Chapter held at ISNT Vadodara Chapter office. 06 EC Members have attended the meeting.

Ÿ

October 2017 to 13 February 2018 - Total 11 Life Members and 8 student members have been added as Life Members of ISNT.

Ÿ

27 September 2017 - The GST registration of ISNT Vadodara Chapter was done. The form GSTR-3B were on time filed for the month from September 2017 to December 2017 and GST was paid on time.

Participants at the evening lecture

CHENNAI : JANUARY 2018 - FEBRUARY 2018 Ÿ 8th January 2018 to 13th January 2018 - Visual Testing Level-II (IS 13805 / SNT-TC-1A) course and examination was conducted. Number of candidates attended the course were 4 and examination were 5. Mr. E. Sathya Srinivasan was the course director and Mr. J. Shanmugam was the examiner.

HYDERABAD : JANUARY 2018 - FEBRUARY 2018 Ÿ 2nd January to 6th January 2018 - ISNT LT Level I Course and Exam was conducted during (18 candidates) Ÿ 10th January 2018 - “Recent advances in NDT” a technical talk delivered by Shri M.Venkata Reddy, Secy. ISNT for

A presentation on “ARJUN by Shri. Deepak Parikh

7th February 2018 to 17th February 2018 - Surface NDT (MT & PT) Level-II (IS-13805 / SNT-TC-1A) course and examination was held. Number of candidates attended the course were 12 and examination was 13. Mr. M.S. Viswanathan was the course director and Mr. P. Anandan was the examiner. Ÿ 20th January 2018 - EC Meeting was held. Ÿ

officers of MIDHANI. Ÿ

7th January 2018 - EC Meeting was conducted

Ÿ

22nd January to 2nd February 2018 - ISNT RT Level II Course and Exam was conducted. (13 candidates)

KOTA : JANUARY 2018 - MARCH 2018 Ÿ 22nd JANUARY - 27th JANUARY 2018 - Radiography Film Interpretation Level-II

March 2018

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MANAGER & HEAD OFFICE IN-CHARGE In ISNT since 02-01-2012.

Has vast knowledge and experience in Accounts, Taxation & Administration Matters. Managing day to day functioning of Head Ofce, matters connected with statutory compliance, co-ordinates with ISNT chapters and ISNT Chapters and ISNT Ofce bearers, arrangements for Meetings, guiding HO Staff in their respective area of work.

March 2018

ACCOUNTS OFFICER In ISNT since 1-11 2010.

In-charge of work connected with Accounts, Preparation of MIS, payment of statutory dues maintenance of accounting records, preparation of monthly income & expenditure statement and matters connected with ISNT membership.

ADMIN OFFICER In ISNT since 23-3-2011.

In-charge of work connected with Training and Certication conducted

by NCB-ISNT as per the instruction of NCB Ofce Bearers. Takes care of general correspondences and other administration matters of Head Ofce.

Assisting accounts ofcer in regular matters.

OFFICE ASSISTANT In ISNT since 4-8-2014.

Coordinates with JNDE Executive, correspondence with subscribers and other institution in connection with JNDE journal.

Assisting accounts incharge in matters connected with preparation of vouchers, receipts, tabulation of service tax, data entry in tally and other work assigned from time to time.

JNDE EXECUTIVE In ISNT since 17-8-2015.

In-charge of corresponding & coordinating with Editors / Authors / Chapters for write ups & managing contents. Sourcing advertisers & handling advertisements. Invoicing, coordinating payments & JNDE related matter with Head Ofce. Laying out, designing nal cover page, editing, proof reading, taking journal for nal print.

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Vibrothermography for Detection of Disbonds in Composite Plates Based on Viscoelastic Loss *Xingwang Guo and Liang Zhu Beihang University, 37 Xue Yuan Rd. Haidian District, Beijing 100191, China *Email: xingwangguo@buaa.edu.cn ABSTRACT The vibrothermography (VT) of disbonds in composite plates by using viscoelastic heating was studied. The inuence of the excitation frequency on the detectability of the disbonds was analyzed.

The temperature response of the plate during VT was analyzed by the nite element method (FEM) based on the viscoelastic loss modeling. It is concluded that the viscoelastic loss of the nonmetal material in the composite plate is the leading factor of the local temperature increase in disbond areas during VT, different disbond has different frequency selectivity.

1.0

INTRODUCTION

V

ibrothermography, also known as ultrasonic thermography and thermosonics, is a relatively new version of IRT which is well known for its defect selective, dark-eld defect detection capability, and capability of detecting kiss defects. The physics governing the heat generation process in vibrating defects is still a topic of active discussion. Xiaoyan Han studied the VT of metals using the FEM, and proved that the heat generation in crack is friction heat [1], Robert Montanini etc. studied the relationship between viscoelastic heat and vibration mode shape by experiment on a sample with resin lled in the defects, found that the viscoelastic heat is closely connected to the mode shape and veried that the viscoelastic heat is the leading factor [2]. The excitation frequency is also an important factor in VT, and the most recently development of VT is the local defect resonance (LDR) theory. I. Solodov and G.Busse found that the excitation with the LDR frequency can pump energy directly into the defected areas, leading to a higher temperature increment compared with non-LDR frequency under the same excitation amplitude [3,4,5].

In order to understand the thermomechanics of the VT of metal-nonmetal bonded composite plates, the VT experiment and thermo-mechanical coupling eld simulation of composite plates containing disbond defects were performed, focusing on the viscoelastic loss and the inuence of excitation frequency on the thermal effect at disbond areas.

2.

THEORETICAL BASIS

2.1 VIBRATION OF PLATES According to the classical plate theory, for an isotropic plate under a transverse base excitation Wb(t) as shown in Fig.1, the differential equation of motion is given as [6]:

(1)

where, w is the displacement of the middle surface of the plate, ρ is density, D is the exural rigidity, E is Young’s modulus, μ is Poisson’s ratio, h is the thickness of the plate. Eq.(1) can be solved when it is combined with the necessary boundary and initial conditions that is omitted here.

Fi g . 1 B o u n d a r y conditions, F represents free b o u n d a r y condition, C represents clamped boundary condition

2.2 Transient Conduction of Heat For the transient conduction of heat in solids with internal heat source, the temperature obeys Eq.(2), (2) where ki (i=x,y,z) is the thermal conductivity in the i direction, and it has Kx= ky= kz=k for a isotropic plate, c is specic heat, q is the power density of the heat source. According to the viscoelastic loss theory, q can be estimated as the timeaveraged volumetric energy dissipated over one cycle of harmonic loading, as given by Eq.(3) [7], (3) where, ν0 is the strain energy density. Eq.(2) can be solved when it is combined with the necessary boundary and initial conditions that is omitted here.

3.

EXPERIMENT

3.1 EXPERIMENTAL SETUP AND SAMPLES An ultrasonic IR thermographic system developed by Beihang University, China, was used. The system includes an IR camera, an ultrasound source and a computer with specialized March 2018

16

software for image acquisition and processing.

The ultrasonic device is equipped with three sets of ultrasonic piezoelectric transducers driven by an ultrasound source in order to stimulate the samples in a relatively large frequency range. The three transducers, denoted as A, B, and C respectively, were used respectively. The nominal power of transducer A, B, and C is 100W, 100W, and 50W respectively.

The reference samples are two adhesive bonded composite plates. Each one consists of an aluminum alloy plate and a cork plate. The thickness of the cork plate is 2 mm for sample 1, and 1 mm for sample 2. Eight pre-made disbonds were created by drilling at bottom holes with a depth of 0.1 mm on the aluminum alloy plate before bonding. The design of the samples and the xture used in the experiment are shown in Fig.2. A plastic gasket was adopted to couple the ultrasonic wave into the sample.

(a)

(b)

Fig.2 Schemes of the sample (a) and its xture (b) used in the experiment

3.2 EXPERIMENTAL RESULTS The excitation duration was 10 seconds, the frame rate was 1Hz, and an image sequence of 60 frames was recorded in each time. The excess temperature images were obtained by subtracting the ambient temperature image from subsequent images in the analyzed sequence. It can be seen from Fig.3 that the defects are obvious, and the thermal mode at the nondefected areas is also clear in Fig.3(c). The thermal mode phenomenon on the non-defected area cannot be explained by the friction heat view [8]. The temperature difference evolutions at each disbonds in the case of Fig.3(c) are shown in Fig.4. The best applied frequency of a transducer can be found in the experimental curves of the average temperature differences of the defects vs.the excitation frequency of the two samples excited by the same transducer, as shown in Fig.5(a),(b). The average temperature difference of the defect is dened as the difference between the average temperature on the defect area and the average temperature on a reference area selected in non-defect region. The best applied frequency of transducer A, B and C is about 21.5 kHz, 26.5 kHz and 39 kHz respectively as shown in Fig.5.

(a) 17 kHz

(b) 20 kHz

(c) 21.5 kHz

(d) 24 kHz

Fig.3 Typical excess temperature images at time t=10s using transducer A acting on sample 1 D=15mm D=8mm D=5mm D=10mm

10

°

Temperature difference/ C

12

8 6 4 2 0 0

10

March 2018

20

30 Time/s

40

50

Fi g . 4 : T h e t e m p e r a t u r e difference evolutions at defects in the case of Fig.3(c) (21.5 kHz excitation by transducer A)

Fig.5 Experimental results showing the max average temperature differences of the defects vs. excitation frequency

All the eight defects can be seen under the 21.5 kHz excitation, this may be attributed to that the output power is larger at the resonance frequency of the transducer. However, this reason cannot explain the observed fact that different excitation frequency has different thermal effect to a certain defect. To understand the relationship between the heat generation at disbonds and the excitation frequency, the numerical simulation by using the FEM was conducted.

4.

NUMERICAL SIMULATION

4.1 BOUNDARY AND INITIAL CONDITIONS To solve the coupled equation, the related boundary conditions and the initial condition were set as the followings.

(1) On the place contacted with the transducer, the amplitude of the base displacement excitation was 2µm, i.e.

Wb(t)=2x10-6 sin (2p ft) (4) (2) The initial condition of the heat conduction was T|t=0=298.15K; (3) The convective boundary condition on the outer surfaces of the specimen was kÑT=hr(T0-T) (5) The convective heat transfer coefcient of the aluminum alloy and the cork were regarded as equal, hr=5W/(m2•K).

4.2 THERMO-MECHANICAL COUPLING FIELD SIMULATION The thermo-mechanical coupling eld simulation was conducted by using the Comsol software with the solid mechanics and heat transfer module [9-10]. The temperature response was calculated in time dependent analysis. Mostly, the calculation time under each excitation frequency was set to 12s, which was enough to include the temperature changes of interest. The tetrahedral meshing was adopted and the maximum meshing size was one tenth of the wavelength of the elastic wave in cork. The material properties were taken as in Table 1.

Loss [11] Specific Thermal Young's Poisson’s Material modulus ratio Density factor heat conducivity -3 E/Pa m h r/kg·m ) c /(J·kg-1K-1) k /(W·m-1K-1) 10 Aluminium 7.0×10

Cork Air

3.92×108 6×10

-3

0.33

2696

5×10-5

900

138

0.46

470

0.01

1790

0.08

0

1.174

0

1013

0.02624

Table.1 Material properties

17

The temperature distribution at time 10s under excitation frequency 21.5kHz of sample 1 is shown in Fig.6(a), and the temperature difference evolutions at the four defects in the upper row are shown in Fig.6(b). The behavior that the temperature difference changes with time in Fig.6(b) is similar to the experimental result in Fig.4, the thermal effect at disbond areas is also similar to the experimental one, and this illustrates the correctness of the simulation. (a)

(b)

(2) The viscoelastic loss of the nonmetal material in the composite plate is the leading factor of the local temperature increase in disbond areas during VT. (3) Different disbond has different frequency selectivity. For an unknown defect, in order to obtain a better detection, the excitation frequency should be adjustable in a large range. The super-harmonic or sub-harmonic excitations due to the nonlinear coupling and support conditions are helpful for the defect detection.

ACKNOWLEDGMENTS This research was supported by the National Natural Science Foundation of China under the projects: 61571028, U1433122.

Fig.6 Simulation results for sample 1: (a) the temperature distribution at time 10s under 21.5 kHz excitation; (b) the temperature difference evolutions of the four defect areas in the upper row

It should be noticed that, the temperatures at the disbonds with diameter 8mm and 5mm are lower in Fig.6 compared with Fig.3(c). Some super-harmonic or sub-harmonic excitations may exist in the experiment due to the undemanding ultrasound coupling and support conditions as indicated in some references [12-14]. if so, multi-frequency elastic waves will be produced when the ultrasonic wave interacts with the sample. To simplify the analysis, only the single frequency excitation was considered here. The simulation temperature elds under the excitation at the super-harmonic and subharmonic frequencies of 21.5 kHz for sample 1 are shown in Fig.7. It can be seen that the frequency 32250Hz has special contribution to the thermal effect on the disbond of 8 mm, while the frequency 43000Hz has contribution to the disbonds of 15 mm and 10 mm, and the frequency 64500Hz has contribution to the disbonds of 8 mm, 5 mm and 10 mm. So the excitation frequency has a great inuence on the local heat generation, different disbond has different frequency selectivity.

(a) 32250 Hz

(b) 43000 Hz

( c) 64500 Hz

Fig.7 Simulation temperature elds at time 10s under the excitation at the super-harmonic and sub-harmonic frequencies of 21.5 kHz for sample 1 5.

CONCLUSIONS (1) VT is a powerful tool to detect disbonds in composite plates, and the defect size and depth are no long the main constraint on the detectability.

REFERENCES [1] Han X, Islam M, Newaz G. Finite element modeling of the heating of cracks during sonic infrared imaging. Journal of Applied Physics, 2006; 99(7): 1-7.

[2] Montanini R, Freni F. Correlation between vibrational mode shapes and viscoelastic heat generation in vibrothermography. NDT&E International, 2013; (58): 43–48.

[3] I.Solodov, M.Rahammer, D. Derusova, G.Busse.

Highly-efcient and noncontact vibro-thermography via local defect resonance. Quantitative Infrared Thermography Journal, 2015; 12(1):1-14.

[4] I.Solodov, J.Bai, S.Bekgulyan, G.Busse. A local defect resonance to enhance acoustic wave-defect interaction in ultrasonic nondestructive evaluation. Applied Physics Letters, 2011;99(21):211911(1-4).

[5] I.Solodov, J.Bai, S.Bekgulyan, G.Busse. Resonant ultrasound spectroscopy of defects: Case study of atbottomed holes. Journal of Applied Physics, 2013;113, 223512.

[6] Cao Z.Y. Vibration theory of plates and shells. Beijing: China Railway Press,1989.

[7] Miller J K, Woods D C, Rhoads J F. Thermal and mechanical response of particulate composite plates under inertial excitation. Journal of Applied Physics,2014;116(24):925.

[8 ] C. Homma, M. Rothenfusser, J.Baumann, J. Shannon.

Study of the Heat Generation Mechanism in Acoustic Thermography. AIP Conference Proceedings. 2006; 820:. 566573.

[9] COMSOL Reference Manual: Heat generation in a vibrating structure. 2012,1-8.

[10] R.Ganesan, A. Muthupandian. Simulation of heat generation from vibration in COMSOL Multiphysics. Excerpt from the Proceedings of the COMSOL Conference 2010 India.

India, Tamilnadu, Chennai, 2010.

[11] G.J.Chang. Viscoelastic damping materials. Beijing: National Defense Industry Press, 2012, 10, 9-38.

[12] I.Solodov, B.A.Korshak. Instability, Chaos, and “Memory” in Acoustic-Wave–Crack Interaction. 2001; 88(1),13 [13] Li Z H, Wi Y X, Long Z L. Nonlinearity harmonic at contact interface in ultrasonic transducers bonding system.

Journal of Vibration and Shock, 2010; 29(09): 138-141+248.

[14] Solodov I, Wackerl J, Peiderer K, Busse G. Nonlinear self-modulation and subharmonic acoustic spectroscopy for damage detection and location. Applied Physics Letters, 2004;(84): 5386-5388.

March 2018

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A Statistical Approach to Subsurface Defect Detection and Evaluation for Frequency Modulated Thermal Wave Imaging Javed Ahmad1,2, Aparna Akula1,2, Ripul Ghosh1,2, Satish Kumar2, Ravibabu Mulaveesala3* and H K Sardana1,2 1Academy of Scientic and Innovative Research (AcSIR), Chennai, Tamil Nadu - 600113, India 2.CSIR-Central Scientic Instruments Organisation, Chandigarh - 160030, India 3.Indian Institute of Technology Ropar, Nangal Road, Rupnagar, Panjab - 140001, India *Email: ravibabucareiitd@yahoo.co.in ABSTRACT Infrared thermography (IRT) techniques are extensively used for characterization of various structures and materials. In this paper, application of principal component analysis (PCA) is proposed for defect detection on the obtained thermal data from frequency modulated thermal wave imaging (FMTWI) and entitled as principal component frequency modulated thermal wave imaging (PC-FMTWI). PCA processes, infrared image sequences to distinguish between sound and defect regions and reduce redundancy by projecting the thermographic data from its original space into Eigen space. PC-FMTWI detects defects by enhancing the contrast between sound and defect regions, thereby allowing retrieval of shape and size. Further, Canny edge detector-based approach is used for extracting edges so as to quantify the size and shape characteristics of defects. Obtained results highlights the robustness of the PC-FMTWI method and its immunity to noise by analysing over a range of signal to noise ratio (SNR) levels.

1.

INTRODUCTION

O

ver the past few years, subsurface defect detection in structures becomes a highly researched area in various industries such as aerospace, civil, electrical, mechanical, manufacturing industries etc. It’s unavoidable to the defects and damages that occurs during either fabrication or the lifetime of structures or components which can be a cause for the system failure in the future. Therefore, nondestructive testing and evaluation (NDT&E) techniques are highly useful for inspection of a variety of materials, structures and components during manufacturing, fabrication and inservice process to avoid such type of system failures and to increase product integrity, reliability of structures or components. Among all NDT techniques, there is a signicant increase in the use of infrared thermography (IRT) as a tool in the eld of NDT&E for subsurface defect detection in various materials, structures and components. IRT is widely accepted by the NDT&E community because of its special advantages such as one of the most prominent, safe, whole-eld, fast monitoring capabilities, non-contact, non-invasive technique.

Infrared thermography is basically a surface temperature variation measurement technique, broadly classied into two approaches: active and passive thermography. Passive thermography measures the thermal radiation emitted from the object using a thermal detector and doesn’t require any external heat sources, whereas, active thermography needs an external heat source in order to create signicant temperature March 2018

difference between the defected and sound region in the specimen under the inspection. Active infrared thermography is one of the NDT&E techniques used to detect aws underlying the structures. There are three most prominent optically excited active thermography techniques: pulse thermography (PT), Lock-in thermography (LIT) and pulse phase thermography (PPT) each one has their advantages and limitations [1, 2].

In PT, a short duration of high peak power thermal energy is used to stimulate the specimen to detect the defects with enough test sensitivity and resolution [3]. Thermal wave front propagates through the specimen surface by heat diffusion and surface temperature changes with time recorded by thermal camera. PT is widely accepted by industrial application because of its simplicity in application and fast in implementation. The disadvantages of PT are that it requires high energy, heat source and it gets affected by emissivity variations, surface geometry variation, reections from the environment and uneven heating.

By contrast, in LIT, specimen surface is stimulated with low peak power periodically modulated thermal heat source and the temporal response at specimen surface is recorded by thermal camera under the periodical sinusoidal heating [4]. The entire energy from the excitation source is delivered onto the specimen surface in single frequency, so the depth resolution capability is xed by thermal wavelength.

However, mono-frequency sinusoidal thermal excitation wave is used in LIT, which may not be accomplished to probe the defects situated at various depths and also need to perform repetitive experiments with various frequencies to cover an entire depths range, which makes LIT as time-consuming

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technique. Phase image is extracted from thermal image sequence by taking Fourier’s transform (FT) of each temporal pixel. The main advantages of using the LIT, is that the obtained phase image is insensitive to local emissivity variations and non-uniform heating.

Another active thermographic technique is pulsed phase thermography (PPT), experimentally similar to PT, but the only difference is that data acquired by PT are processed in a different manner by transforming the acquired thermal image sequence from time domain to the frequency spectrum domain.

In PPT, in order to extract the broad spectrum of several thermal frequency components present in PT, the Fourier transform is performed on each temporal pixel of thermograms. The resulting phase images have the benets of being less affective by emissivity variations, surface geometry, uneven heating and environmental reections same as phase image obtained by LIT. To overcome these limitations associated with conventional thermography techniques (high peak power, long-time experiments, depth of penetration), a relatively new thermographic technique based on non-stationary thermal excitation signal entitled as frequency modulated thermal wave imaging (FMTWI) is used to perform experimentation to acquire thermographic data [5, 6]. FMTWI solves the problems related to conventional thermography such as high peak powers and long experimentation time of PT and LIT, respectively. In FMTWI, the specimen is excited with not only mono-frequency (for example, in LIT) or with all frequencies (for example, in PT), but with predened linearly up-chirp thermal wave, whose frequencies ranges varies from lower to higher. The bandwidth of frequencies is decided by some of the specimen properties like sample thickness, thermal conductivity, thermal diffusivity and on aw depth. FMTWI uses relatively low peak power and single experiment to cover all the depths range of the defects from shallower to the deeper one. The acquired data are a set of three-dimension IR image sequence which contains valuable information both in time and space domain.

There are various processing approaches used to probe subsurface defect, most are based on phase change and time delay of captured thermal response. While there is an improvement in all the processing techniques for qualitative analysis, but there is no method which can process the sequence of infrared images statistically to investigate for the variation occurs between defected and sound region in order to enhance the thermal contrast among them. The present work describes a methodology, based on a statistical approach principal component analysis (PCA) that has been used for subsurface defect detection to yield high thermal contrast of structural defects in the mild steel sample [7].

2.

METHODOLOGY An application of PCA technique in the eld of thermal nondestructive evaluation (TNDE) is known as principal component thermography (PCT) [8]. It has a number of applications in several elds such as data compression, dimension reduction, face recognition, computer vision etc. The PCT has been proposed to process the IR image sequence to emphasize the defects contrast and depth characterization [9]. PCT can be used as a statistical approach for the recognition of structural defects. In this paper, a combined application of PCA and FMTWI, PC-FMTWI, is proposed in order to probe subsurface

aws situated at different depths and to estimate their diameter. This technique provides an outline for contrast improvement between defect and sound area. A recorded sequence of IR thermal images contains the valuable information both in time and space. PC-FMTWI projects the obtained thermographic data from its original domain to its Eigen domain and thereafter, pattern in data is identied by increasing variance and reducing covariance. This is achieved by the use of empirical orthogonal functions (EOF) method which builds an arrangement of orthogonal statistical modes that highlight the inconstancy of spatial and time domains inside the IR images and likewise give the most grounded projection to the information.

Singular value decomposition (SVD), is an statistical algorithm which can be used to extract of the transient and spatial variations related to thermal contrast associated with the structural defects from thermographic data matrix in very efcient way [10]. The SVD is similar to the PCA method which identies the strongest pattern in the data and organizes them in descending order along which the data show the maximum variation. The underlying hypothesis to be validated is that variation of information would be more in the defected area as compared to the sound area, thereby better contrast of defects would be available in the rst few principal components (PC).

In order to perform SVD algorithm on a video sequence, rst the 3D thermographic data must be structured into data matrix in a manner such that the time variation occurs column-wise and spatial variation occurs row-wise, as illustrated in Figure 1. SVD is a statistical tool similar as PCA but there is an additional advantage of using SVD that it simultaneously gives the PC in both temporal and spatial domain.

Figure 1 : Preparation of 2D data matrix

Figure 2 : Flowchart of implementation of PC-FMTWI using SVD algorithm

The SVD can be decomposed, the normalized data matrix A, size M x N (M >N), into three matrices U, S & V. A=USVT where, orthogonal matrix U of size M x N contains spatial information in the orthogonal space. The column of orthogonal matrix U signies the EOF, which depicts the spatial inconstancy of the actual data set. The diagonal matrix S of size N x N March 2018

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contains singular values elements of data matrix A. The singular elements of the matrix S are the eigenvalues for the relating eigenvectors of the vector matrix V. The rows of matrix VT,of size N x M associated with time characteristic behavior of each orthogonal function called as PC vectors. The orthogonal matrix U contain a set of EOF. The principal EOF implies the most vital inconstancy of the data and similarly the second EOF holds second most imperative inconstancy, and so forth. The essential few EOF is used to modify the information to lessen the redundancy of the actual data set.

3.

EXPERIMENTATION The experimental setup of FMTWI for the data acquisition is shown in Figure 3. The experiment has been performed on a 1 cm thick mild steel sample contains six articially created circular holes located at various depths 0.12 cm, 0.137 cm, 0.197 cm, 0.213 cm, 0.232 cm and 0.342 cm respectively from the plain side of the sample, diameter of each defects are 1 cm, illustrated in Figure 4 (a). A linear frequency modulated thermal wave, frequencies varied from 0.01 Hz to 0.1 Hz in a time duration of 100 seconds is used to heat the sample with the help of two halogen light sources, each of power 1 KW. Thermal wave front propagates through the specimen by heat diffusion through conduction process, when encountered with defects it acts as resistance and increases the surface temperature.

Transient thermal behavior over the sample surface is recorded through the thermal camera at frequency of 20 frame per second. A preprocessing step is required by removing the temporal mean of each pixel to minimize thermal effects [11]. In order to detect the defects underlying structure, further PCA data processing approach is applied to the rst order temporal mean removed recorded thermal image sequence.

Figure 3: Experimental setup for FMTWI 7000 50

6500 100 6000 150

............ (1) whereas, is the temperature distribution on the sample surface in the direction of heat ow x, at time t, is the thermal diffusivity of the material, is the material thermal conductivity, is the

material density, is the specic heat at a constant temperature.

Instantaneous temperature {T(x,t)} is given by explaining the above equation with limiting conditions x=0 to d of FMTWI:

........(2) where, T0 [K] initial change in the temperature by heat ux, f0 [Hz], is the initial frequency, B[Hz] is the bandwidth and t[sec] is the excitation span.

The thermal diffusion length (mFM) for FMTWI is explained by equation: ........... (3) where, is the rate of change of frequency, which controls the frequency variation w.r.t time. Thermal diffusion length changes with change in slope of frequency, gives measure of the various depth in the sample. The thermal wavelength ( ) for FMTWI is given by. ........... (4) In FMTWI, thermal diffusion length and thermal wavelength both changes with change in the excitation frequency while performing the experiment, which promote in probing the defects situated at various depths.

4. RESULTS AND DISCUSSION As it can be seen in the raw thermographic image, no defects are visible, shown in Figure 4 (b). The acquired frames have been processed in spatial domain using statistical data processing approache. Results obtained from PC-FMTWI method is shown in Figure 5 (a). The obtained image corresponds to 3rd EOF after applying PC-FMTWI to the entire 2000 image sequence. It is interesting to see that after applying the PC-FMTWI resulting 3rd EOF highlights, all six defects at various depths. PC-FMTWI improves the contrast of the defected area in EOF images. Image processing tools such as median lter is used to reconstruct the 3rd EOF and visualize the subsurface defects with better contrast as shown in Figure 5 (b).

EOF images clearly demonstrate the ability of PC-FMTWI to identify deeper defects in the sample with adequate depth resolution.

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Theory - Frequency modulated thermal heat energy propagate through the sample by conduction and hence thermal response at the sample surface changes with time. Thermal response at surface can be explained by Fourier’s one-dimensional heat equation [12]: March 2018

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Defects’ SNRs, PC-FMTWI using SVD have been calculated.

Figure 6, It follows that the EOF image obtained from PC-

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FMTWI technique signicantly increases input values of the SNR (In cases of SNR of the input images is higher than that in the EOF images are evidently related with the particularities of the selection of defect and sound region and do not reect the general tendency because there is no correlation between defected and sound region).

Table 1: Estimated diameter

Figure 6: SNR of the input image and 3rd EOF Image obtained from PC-FMTWI method

The diameter of the defects has also been calculated as shown in the owchart (Figure 7). Each individual defect is cropped and analyzed by PC-FMTWI. The essential EOF holds defects information is taken from the output of SVD algorithm which contains some amount of noise. The noise in the image is rst removed by applying a median lter and then the Canny edge detector is applied to the ltered image to extract the edge of the defect. Using PC-FMTWI method, shape of the defects can easily be extracted by applying Canny edge detector straightforwardly.

Figure 7: Flowchart for shape extraction and diameter calculation

To analyse the robustness of PC-FMTWI towards quantifying defect shape and size, pixel length of the detected defect has been computed. In this experiment, it is well known that the defects are circular and diameter of each defect is 1 cm. The estimated diameter of the defects is tabulated in Table 1.

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CONCLUSION This paper demonstrates the applicability of frequency modulated thermal wave imaging along with principal component analysis for detection of subsurface defects in the mild steel sample. Further attempts were made to calculate the actual diameters of the subsurface defects.

REFERENCES [1] X. Maldague and S. Marinetti, "Pulse phase infrared thermography," Journal of applied physics, vol. 79, pp. 2694-2698, 1996.

[2] C. Meola, G. M. Carlomagno, A. Squillace, and A. Vitiello, "Nondestructive evaluation of aerospace materials with lock-in thermography," Engineering Failure Analysis, vol. 13, pp. 380-388, 2006.

[3] C. Ibarra-Castanedo, M. Genest, P. Servais, X. P. Maldague, and A. Bendada, "Qualitative and quantitative assessment of aerospace structures by pulsed thermography," Nondestructive Testing and Evaluation, vol. 22, pp. 199-215, 2007.

[4] M. Choi, K. Kang, J. Park, W. Kim, and K. Kim, "Quantitative determination of a subsurface defect of reference specimen by lock-in infrared thermography," Ndt & E International, vol. 41, pp. 119-124, 2008.

[5] R. Mulaveesala and S. Tuli, "Implementation of frequencymodulated thermal wave imaging for non-destructive sub-surface defect detection," Insight-Non-Destructive Testing and Condition Monitoring, vol. 47, pp. 206-208, 2005.

[6] V. Ghali and R. Mulaveesala, "Frequency modulated thermal wave imaging techniques for non-destructive testing," Insight-NonDestructive Testing and Condition Monitoring, vol. 52, pp. 475-480, 2010.

[7] D. Griefahn, J. Wollnack, and W. Hintze, "Principal component analysis for fast and automated thermographic inspection of internal structures in sandwich parts," Journal of Sensors and Sensor Systems, vol. 3, p. 105, 2014.

[8] S. Marinetti, E. Grinzato, P. G. Bison, E. Bozzi, M. Chimenti, G. Pieri, et al., "Statistical analysis of IR thermographic sequences by PCA," Infrared Physics & Technology, vol. 46, pp. 85-91, 2004.

[9] N. Rajic, "Principal component thermography for aw contrast enhancement and aw depth characterisation in composite structures," Composite Structures, vol. 58, pp. 521-528, 2002.

[10] J. Dumoulin, L. Ibos, C. Ibarra-Castanedo, A. Mazioud, M. Marchetti, X. Maldague, et al., "Active infrared thermography applied to defect detection and characterization on asphalt pavement samples: comparison between experiments and numerical simulations," Journal of modern optics, vol. 57, pp. 1759-1769, 2010.

[11] R. Mulaveesala, S. S. B. Panda, R. N. Mude, and M. Amarnath, "Non-destructive evaluation of concrete structures by non-stationary thermal wave imaging," Progress In Electromagnetics Research Letters, vol. 32, pp. 39-48, 2012.

[12] R. Mulaveesala and S. Tuli, "Theory of frequency modulated thermal wave imaging for nondestructive subsurface defect detection," Applied Physics Letters, vol. 89, p. 191913, 2006.

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Application of Electro-thermal Approach for NonDe str ucti v e Ev a l ua ti o n o f M a te r i a l s a n d Structures by Infrared Thermography Priyanka Jena, Humaid Mohammed, Sagarika Kumar and Rajesh Gupta* Department of Energy Science and Engineering, Indian Institute of Technology Bombay, Mumbai, India *E-mail ID: rajeshgupta@iitb.ac.in ABSTRACT Active infrared thermography is a fast, safe, non-contact technique for non-destructive evaluation of materials and structures. In this technique, the temperature evolution over the surface is used for detection and characterization of defects in the object. To simplify the analyses and solutions of active infrared thermography problems, electro-thermal modelling and simulation approach has been exploited. The approach is based on the analogy between the electrical and thermal quantities. This paper highlights the state-of-the-art research performed using the electro-thermal approach of active infrared thermography for investigation and characterization of materials and structures, and to nd their direct (optimum parameter for defect detection) and inverse solutions (defect and material parameters). Different electro-thermal models have been presented while considering their applicability and advantages for various applications of active infrared thermography investigations. The approach was found to be suitable in defect investigation for a variety of materials and structures. This paper opens a wide horizon of electro-thermal approach to solve active infrared thermography problems, using electrical circuit analysis methods and signal processing techniques, which are common in the eld of electrical engineering.

1.0

INTRODUCTION

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nfrared thermography (IRT) technique has emerged as a popular non-destructive evaluation (NDE) technique in the last few decades due to its adaptable, multi-faced nature and the rapid development in the infrared imaging technology.

The major advantage of this technique is its fast and noncontact nature and its applicability for large area investigation [1]. IRT has a variety of applications covering all elds of science and engineering. There are many reported applications for condition monitoring of civil structures [2]–[4], evaluation of fatigue damages in materials [5], [6], inspection of machineries [7], [8], welded joint inspection [9]–[11], monitoring of circuit boards [12], [13] and also for evaluation of chemical and biological processes [14]–[16].

Infrared thermography can be classied as passive and active thermography based on the mode of operation. In passive thermography, surface temperature variation of the test object is observed under its natural state i.e. without application of any external thermal stimulus and the abnormal or non-uniform temperature prole indicates a potential problem. This mode of IRT covers a wide variety of applications including detection of moisture, liquid level in enclosures and inspection of thermal insulation, electrical components, leakages, sources of heat loss, and also bio-medical diagnostics [1], [17]–[22].

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In active thermography mode, an external stimulus in the form of heat is given to create temperature contrast for detection and characterization of surface and sub-surface defects in materials and structure. Active infrared thermography is relatively recent addition in the eld of NDE. This technique can be further classied into pulse, step and lock-in thermography based on the excitation waveform. Every technique has its own advantages and limitations [1]. In pulse thermography, a high energy pulse is impinged upon the specimen for a very short duration, which allows fast detection but is generally limited for shallow defect investigation. In step thermography, a constant low energy thermal excitation is used for longer period for detection and characterization of defects. It is a slow technique, but can be used for detection of deeper sub-surface defects. In lock-in thermography (LIT), a periodic thermal excitation is used for defect detection and characterization. In this technique, the oscillating part of thermal signal is evaluated over the surface.

The amplitude and phase information obtained from this technique is used to evaluate sub-surface defects. This technique provides a high signal to noise ratio (SNR) and a good spatial resolution. It can be used to nd deeper defects however it is also a slow technique. Besides these broad categories, IRT has also been recently classied on the basis of thermal image processing such as pulse phase thermography [23], frequency modulated thermal wave imaging [24] and by the way of exciting thermal stimulation, such as vibrothermography [1].

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Infrared thermography technique provides either temporal temperature evolution or stationary temperature prole at the surface of the test object. In order to nd the solutions from the analysis of a thermal response for different applications in active infrared thermography, modeling and simulation is generally required. It is especially indispensable when it is difcult to replicate environmental conditions due to unavailability of resources and time constraint. The solution of active infrared thermography can be broadly categorised as direct and inverse solutions. The direct solutions of active infrared thermography provide the temperature evolution at surface of the object. These solutions play an important role in understanding the mechanism of formation and investigation of temperature elds, created by various types of defects and thermal stimulations. It also gives an understanding of the interdependence of variable parameters and helps to optimize experimental parameters for better defect investigation. In contrast to direct solutions, the domain of inverse solutions includes extraction of information about the defect area, defect depth and thermal properties of material from the surface temperature evolution.

Active infrared thermography technique is based on the heat conduction principle. Hence, solutions to its problems can be obtained by analytical or numerical method of heat transfer [25], [26]. However, pure analytical solutions are available for very simple cases (generally 1-D, simple geometry, isotropic material, etc.). Whereas, numerical solutions are generally required in more practical situations (which are generally not simple cases), and require more computation time and programming skills. Although, there are few dedicated software’s available, which can solve heat transfer problems, but they are only exible enough to dene simple input excitation waveforms and process temperature evolution.

To overcome these limitations of analytical and numerical methods, analogies can be adopted from other eld of science and engineering to reduce complexities. In this context, some different approaches including electro-thermal approach, neural network, fuzzy logic, genetic algorithm has been introduced [27]–[30]. The propagation of heat inside the material is analogous to current ow in electrical equivalent circuit of heat conduction. The transformation of a heat transfer problem into its electrical equivalent can reduce the complexity of the problem. The analysis based on electrical modeling of heat transfer is popular and is extensively used for investigation of thermal behavior of electronic devices [31]. Following this analogy, R. Gupta et al. introduced this approach to obtain the solutions of active infrared thermography problems by channelizing the electro-thermal approach. By using this approach of modelling active infrared thermography, optimum heating parameters required to view a sub-surface defect in a material can be estimated [32], [33]. The analogy can be further used to derive inverse solutions by exploiting the electrothermal approach of modelling active infrared thermography using various circuit analysis methods [34] and signal processing techniques [24], [35] existing in eld of electrical engineering. Also, similar approach can be used for deriving direct and inverse solutions to different problem such as defect characterization and estimation of material properties.

This paper highlights the work related to application of electrothermal approach in the eld of active infrared thermography on different materials and structures. It covers the basic aspects of electrical-thermal analogy, and illustrates the different types of electro-thermal models available for the purpose of active infrared thermography solutions. In the later section, various applications of direct and inverse solutions using electrothermal modeling of active infrared thermography problems have been elucidated.

2. ELECTRO-THERMAL APPROACH OF ACTIVE INFRARED THERMOGRAPHY This section presents the correspondence between thermal and electrical quantities by correlating the differential equations governing the one-dimensional (1-D) heat conduction and voltage variation in lossy transmission line. The 1-D heat conduction equation is given by [25] ......... (1) Where, ρ, c and k are the mass density, specic heat and thermal conductivity of the material respectively. The above equation can also be written as: .......... (2) Where C’ is the heat capacity of a small element of thickness Δd and area of cross-section A. Similarly, the differential equation describing voltage V variation in a lossy transmission having electrical resistance per unit length Rl and electrical capacitance per unit length Cl, with negligible inductance and leakage, is given by [36] .......... (3) For a small transmission line element having length Δd, area of cross-section A, electrical conductivity σe and electrical capacitance C, the terms Rl and Cl can be written as (1/σeA) and (C/Δd) respectively, after substituting these values of Rl and Cl in Eq. (3), it can be rewritten as: ........... (4) By comparing Eq. (2) and Eq. (4), the correlation between thermal and electrical quantities can be written as: T↔V ↔ , k ↔ σe , C' ↔ C ........... (5) Heat ow in a material depends on the temperature gradient, similar to current ow due to voltage difference. In this process, thermal resistance is similar to electrical resistance, and the heat capacity of the material is similar to charge stored in a capacitor. This equivalence can be used to electrically model a material of thickness Δd, area of cross-section A, thermal conductivity k, density ρ and specic heat c into its equivalent resistance Rth and capacitance C using the above equivalence between thermal and electrical quantities [31][1]: .......... (6) .......... (7) Similarly, for convective and radiative heat transfer, the thermal resistance can be modelled as Rconv and Rrad respectively [1]: March 2018

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......... (8) ......... (9) Where, A, hc and hr are the surface area, convective and radiative heat transfer coefcient respectively. These analogies are applicable in multi-dimensional, stationary and transient regimes and can be utilised for making electro-thermal model of active infrared thermography technique. In the electrothermal model, the electrical equivalent of external stimulus is a driving source which can either be a current or voltage source.

Generally, constant current source is used analogous to heat ux, to model incident heat energy [33]. The boundary condition in the electro-thermal model can be incorporated by using Rconv, Rrad which represent convective and radiative heat transfer boundary or a diode to represent insulated boundary.

There are basically two approaches of constructing an electro-thermal model based on the type of application viz. distributed and lumped. In distributed modelling, the object is divided into small elements. Whereas, in a lumped model the object is modelled as a single element. The application of distributed model and lumped model depends upon the type of analysis and desired accuracy of the results. This exibility in electro-thermal modelling makes it suitable for application in active infrared thermography problems. Based on the application, the object under active infrared thermography investigation, can be electrically modelled either in one-(1-D), two (2-D) or three-dimension (3-D) representing heat conduction in 1-D, 2-D, or 3-D respectively. In a 1-D model, ow of heat is modelled along a single direction of the object and is considered negligible in other two directions, as shown in Fig. 1. In this case, electro-thermal model is constructed by dividing the object into small elements with corresponding resistance (R) and capacitance (C) values, as given in Eq. (6) and (7).

Figure 1: One-dimensional equivalent electrical model of heat conduction for active infrared thermography

Whereas, in a 3-D model, the heat conduction in the object is considered in all three directions. It can be modelled by dividing the object into small identical cuboid (element), as shown in Fig. 2. Where, each cuboid is represented by its electrically equivalent resistance and capacitance (using Eq. 6 and Eq. 7).

The appropriate interconnection of all the cuboids, forms a 3-D RC model of the object. This type of modelling can be used to model thermally anisotropic as well as multi-layered objects and, also caters to model lateral diffusion of heat.

The above mentioned electro-thermal models can be solved by commonly available electrical circuit simulators, to obtain the direct solutions.

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Figure 2: Three-dimensional equivalent electrical model of heat conduction

Inverse solutions can be derived by simplied electro-thermal models, with the help of network analysis techniques, which are commonly used in the eld of electrical engineering [20].

This type of analysis approach has been used by Gupta and Tuli [15] to estimate defect parameters. Also, signal processing technique can be applied for estimation of inverse solution from the temperature response obtained by the active infrared thermography investigation. For this purpose, thermal response in time domain can be simplied by converting it in a frequency domain. Following this approach, Mulaveesala et al.

[24], [35], [37] have used signal processing on the thermal signals for defect parameter estimation.

3. APPLICATIONS OF ELECTRO -THERMAL APPROACH FOR NON-DESTRUCTIVE EVALUATION OF MATERIALS AND STRUCTURES In this section, an overview of the applications including direct and inverse solutions of active infrared thermography problems using electro-thermal approach are given for different materials and structures.

3 . 1 . D I R E C T S O LU T I O N S O F A C T I V E I N F R A R E D THERMOGRAPHY The temperature evolution obtained from active infrared thermography and electro-thermal modelling and simulation can be instrumental to understand the inuence of different heating parameters on the thermal prole, in addition to the preliminary role of defect detection. This further facilitates the determination of the optimum heat source characteristics for enhanced visibility. In the following sub-sections the applications of electro-thermal modelling and the methodology for acquiring direct solutions has been illustrated.

3.1.1. Investigation of materials In this subsection, some fundamental work done based on the direct solution of common engineering materials like mild steel and silicon, using electro-thermal modelling of active infrared thermography, have been presented. This approach can be extended to other materials by modifying the electro-thermal model according to the dimensions, thermal properties and the boundary conditions of the material. Defect detection in a material is dependent on factors like; defect location, defect size, resolution of thermal camera and heat source characteristics (shape of waveform, ux intensity and duration).

The defect location, size and the resolution of thermal camera are xed, hence, the factors that can be varied and optimized for better detection of defects are the heat source characteristics. Optimization of heat source facilitates maximum thermal contrast, with minimum temperature rise in a short time. The electro-thermal modelling can be advantageous in such a situation because it enables estimation

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of optimum characteristics like heat ux intensity, heating duration and shape of heating waveform before performing the experiments.

1) Estimation of optimum heat ux intensity: Optimum heat ux intensity is the minimum heat ux necessary to view the sub-surface defect in a material for a given heating duration and thermal camera resolution. In electro-thermal analogy, incident heat ux is modelled as a current source.

Gupta and Tuli [33] used this analogy in a distributed electrothermal model to determine the lower limit of heat ux required for defect detection in a mild steel sample. This type of modelling can help to nd the minimum heat ux requirement, for a given resolution of thermal camera and heating duration.

2) Estimation of optimum heating duration: Optimum heating duration in case of step heating is the minimum heating time in which the thermal contrast of the material reaches its saturation or maximum value with minimum temperature rise of material [32]. The factors which inuence the estimation of optimum heating time are; type of heat source, material and resolution of thermal camera. This application of optimization of heating time duration is of practical signicance in situations where excess temperature rise is a concern. In this direction, Gupta and Tuli [32] used electro-thermal approach for modelling a silicon diaphragm sample, which is a temperature sensitive material. The model could successfully estimate the minimum heating duration necessary for better defect detection.

3) Estimation of minimum detectable thickness: The magnitude of thermal contrast due to a defect in material is dependent on the defect depth, defect thickness, heat source characteristics and resolution of thermal camera. The minimum thickness of detectable defect can be estimated if the defect depth is known for a given experimental setup i.e. heat source characteristics and resolution of thermal camera are known.

3.1.2. Investigation of structures The applications of electro-thermal approach are not limited to materials but can also be extended to complex multi-layered samples like composite, coated materials etc. [32], [33]. In this sub-section, the application of electro-thermal approach is illustrated for use on a photovoltaic (PV) module, which is a multi-layered structure having silicon solar cells sandwiched between glass and polymer back-sheet with the help of an adhesive encapsulant. Even though the packaging and structure of a PV module is made to protect it from external environment conditions, it is still vulnerable to degradation under harsh outdoor condition. The commonly observed degradation and defects in a PV module are; shunts (cause heating due to high current ow in shunts), corrosion (cause heating due to joule heating at regions of high resistance), delamination (create temperature contrast due to non-uniform heat ow in presence of sub-surface air void) and discoloration of encapsulant (non-uniform heating due to reduction in intensity of heat ux). Different IRT techniques stimulate thermal response in the PV module due to the above mentioned reasons, which can be viewed as thermal contrast on the module surface. This knowledge can be exploited using electro-

thermal modelling for better detection and characterisation of defects arising in PV modules. Recently, Sinha et al. [38], [39] used this approach to study the delamination degradation occurring in PV module. Delamination is a form of structural degradation which occurs due to reduced adhesion strength between the different layers of a module, as shown in Fig. 3a.

Its equivalent 1-D model (Fig. 3b), which has been extended to 3-D model as shown before in Fig. 2. Sinha et al. [38] used a 3-D RC model of a PV module with delamination defect between cell and encapsulant, undergoing investigation by step heating.

Figure 3: (a) Cross-sectional schematic of PV module with delamination (b) 1-D electro-thermal model of a PV module with delamination

The model could detect the delamination and aided in optimization of different parameters i.e. heat ux intensity and heating duration and could also estimate minimum detectable delamination thickness by an experimental setup.

In addition to step heating, Sinha and Gupta [39] modelled different waveforms viz. rectangular, half sine and short pulse as a current source with the same electro-thermal model as earlier reported by Sinha et al. [38] to show their respective effects on the detection and characterisation of delamination. The advantages and limitations of different waveform have been studied in detail for characterization of delamination in PV module.

Another commonly observed defect in PV modules is shunt in the solar cells. Shunts are the low resistance paths formed in a solar cell (due to disruption of the p-n junction). The presence of shunts increases the cell temperature and decrease the overall cell performance. In this regard, Somasundaran et al. [40] investigated the performance loss in a cell due to the thermal mismatch caused by the presence of shunts. A RC model of a PV module was developed with each layer i.e. glass, EVA (encapsulant), silicon solar cell, tedlar (back-sheet) (Fig. 4a) modelled according to their respective thermal properties with a shunt current obtained using distributed diode modelling, as shown in Fig. 4b. The model could give the temperature rise due to presence of shunts. The increase in temperature was also used to estimate the loss in electrical performance like output power, ll factor and module open circuit voltage.

Figure 4: (a) Crosssection of PV module (b) Electro-thermal model of PV module with shunt (a)

(b)

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Shunts in the solar cell are commonly inspected through lock-in thermography (LIT) technique due to its advantage of high signal to noise ratio (SNR). The quantitative evaluation using LIT is done with an assumption that the sample is in steady state condition, whereas in the actual situation the sample reaches the steady state condition only after an initial heating phase. If this initial heating phase is not considered in the calculation of lock-in signal it leads to a temperature drift error in the results. This is especially signicant where fast implementation of LIT is a requirement. Gupta and Breitenstein [41] estimated the error caused by this temperature drift. For this purpose, a solar cell with a shunt was modelled using a 2-D RC network with a current source, as shown in Fig. 5

(a) (b) Figure 5: (a) Schematic of solar cell with shunt subjected to irradiation (b) 2-D electro-thermal model of a solar cell with shunt

3.2. INVERSE SOLUTIONS OF ACTIVE INFR ARED THERMOGRAPHY The estimation of defect parameters and material properties is another important aspect of NDE after the defect detection.

However, estimating these parameters from a thermal prole is not straightforward and needs an inverse approach. In this section, the applications where, electro-thermal approach has been exploited for obtaining the inverse solutions of active infrared thermography have been illustrated.

3.2.1. Investigation of materials The following sub-section illustrates different electro-thermal models used for estimating defect depth, defect area and material thermal properties using various active infrared thermography techniques.

1) Estimation of defect depth The defect depth is a feature which cannot be obtained directly from the temperature prole. A detailed analysis and inverse treatment of the temperature prole obtained through modelling and simulation of different active infrared thermography techniques can be helpful in estimating defect depth.

In this context, Gupta and Tuli [34] investigated the trend of temperature rise over the surface of semi-innite and nite thickness material subjected to step heating, for estimation of defect depth. For this purpose, a distributed RC model of a semi-innite material which is similar to an innite RC ladder with a current source at the heating end (front surface) was made, as shown in Fig. 6. The voltage at this end is the surface temperature of the semi-innite material. Its expression has been obtained by the Laplace transform technique of network analysis, and is given as: March 2018

.......... (10)

Figure 6: Distributed RC model for a semi-innite material

The thermal equivalent of Eq. 10 can be obtained by using Eq. 6 and 7 and the expression for surface temperature rise of a semiinnite material is given as: .......... (11) The above equation indicates that when a semi-innite material is subjected to step heating on one side, the surface temperature at the heating end increases in proportion to square root of time (√t), as shown in Fig. 7a.

In case a material of nite thickness (d) is subjected to step heating, temperature initially increases in proportion to square root of time (√t ) but drifts away from this trend, when heat is reected to the front surface from the back, as shown in Fig. 7a. This is termed as thermal transit time and it is proportional to d2/α , where α is the material thermal diffusivity [42][43].

(a) (b) Figure 7: (a) Temperature rise (T) vs square root of time (√t) over a sample surface subjected to step heating (b) Temperature evolution (T vs t) over the sample surface subjected to step heating

Gupta and Tuli [34] utilised the above mentioned behaviour to estimate the defect depth in a material. The schematic of the sample with its cross-sectional view is shown in Fig. 8a. The dependency of thermal transit time on the thickness (d) and thermal diffusivity (α) of the material was used to derive an expression for defect depth (dd), which is expressed as: ......... (12)

Where, ttd and ttnd are the thermal transit time of the defect and non-defect location as shown in Fig. 7a. The derived expression was independent of material thermal properties and incident heat ux, which is advantageous in many applications where material under investigation is not known.

In addition to above mentioned work, Tuli and Mulaveesala [24] have used frequency modulated thermal wave imaging (FMTWI), for defect depth estimation using RC network model. In this method, sample was subjected to a frequency modulated thermal wave which is a time varying aperiodic heating method. The output response of this model

27

underwent a signal processing treatment called pulse compression which resulted in a compressed signal with a peak. The time at occurrence of the compressed peak of pulse compressed signal was used as a measure to estimate defect (a) depth.

This equation is independent of material properties and heat source intensity, which can be obtained from the observed temperature evolution curves. This type of solution is very advantageous in practical situation where material under investigation and incident heat ux is not known.

2) Defect Area: The defect area, like defect depth cannot be directly known as it requires greater analysis of the temperature data. There are many analysis methods based on contrast images for estimation of defect area like full width at half-maximum (FWHM), maximum gradient etc. [1], [44]. The major limitation of these methods is their requirement of complete defected region temperature data.

To overcome this limitation, Gupta and Tuli [34] proposed a method in which temperature data at only two points (defected and non-defected region) was required for the defect area estimation. The schematic of the sample (with a square defect) is shown, with its cross-sectional and front view in Fig. 8a and 8b. A step heating technique was utilized as a heat source. In this type of heating technique, the temperature contrast over the sample increases exponentially, and saturates after some time, which has been represented by the term ΔTsat, as shown in Fig. 7b. The saturation of temperature contrast (ΔTsat) at later stages indicates a lumped behaviour of the material. This behaviour was utilised to estimate the defect area of a material by modelling it as a 2-D RC lumped model with a current source, as shown in Fig. 8c.

3) Estimation of material thermal diffusivity: The thermal property characterization of material is another important facet of NDE. There are already many established methods of estimating thermal properties like ash method, hot wire method, transient plane source method etc. [45], [46].

In this direction, Gupta et al. [47] have also contributed by formulating a transient method for estimation of thermal diffusivity using an electro-thermal approach. A distributed RC model (with constant voltage source) was used to model a material subjected to constant temperature at one surface. The model was analysed using Elmore delay, which is an extensively used concept in digital electronics as an approximation of the delay in information transfer through interconnecting wires. [48].

The charging curve of a capacitor in a RC circuit with constant voltage has a characteristic point known as time constant. The time constant (or also called as delay time, τ) is a point on the voltage response curve which indicates time required for the capacitor to charge 63% of the input voltage.

For a single RC element in a circuit, delay time (τ) is given by: τ = CR ......... (14) Whereas, for a cascaded N-stage RC network as shown in Fig. 9a, the expression for delay time (τ) can be written as Eq. 15 using Elmore delay [48]: .......... (15)

(a)

(b)

( c)

Figure 8: Schematic of sample with sub-surface defect (Ad): (a) side-view of sample (b) front-view of sample (parallel to the heated surface) (c) Lumped RC model of the sample

The RC model was analysed by using Laplace transform technique of circuit analysis. A simplied expression for defect area (Ad) was obtained in terms of the saturation value of temperature contrast (ΔTsat ), for a sample of thickness (d) with a thermal transit time at defected location (ttd) and a thermal transit time at non-defected location (ttnd). Two parameters 'a' and 'b' were obtained from the temperature evolution plots.

Where, the parameter 'a' is the slope of the T vs Öt when initial temperature evolution over defected and non-defected regions increases at same constant rate, from Fig.7a. Whereas, parameter 'b' is the slope of the T vs t over non-defected region (when the corresponding value of temperature contrast attains saturation i.e. at ΔTsat , from Fig. 7b.

Using the above parameters, the simplied expression for defect area is given by:

......... (13)

(a)

(b)

Figure 9: (a) Distributed RC model of a material subjected to constant temperature (cross-section shown perpendicular to heated surface) (b) Temperature evolution at the backside of the sample

Since, the R and C are identical, can be written as: .... (16) Now for a completely distributed response, ∆d → 0, N → ∞ and N∆d = d, and hence the expression of τ gets reduced to: .... (17) Where, thickness (d), resistance per unit thickness (Rl) and capacitance per unit thickness (Cl).

Using the analogy dened in Eq. 6 and 7, Eq.17 can be converted into its thermal equivalent using the expression for material thermal diffusivity as a function of material thickness (d) and thermal time constant (τT). This thermal time constant (analogous to electrical time constant) can be dened as the time required for the temperature response to reach 63% of the temperature at the front surface of material (Fig. 9b). The March 2018

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relation can be expressed as: ......... (18) This method facilitates faster estimation of thermal diffusivity as it is obtained at transient (non-steady state) condition. The temperature in this technique can be measured by infrared thermography.

3.2.2. Investigation of structures Direct solutions can also be used to nd inverse solution by curve tting. Sinha et al. [38] used this approach to estimate delamination thickness in a PV module from the direct solution obtained from a 3-D electro-thermal model as shown in Fig. 2.This approach of obtaining inverse solution was easy in case of delamination defect in PV module structure, since the depth of the delamination is xed. However, this approach to nd inverse solutions by tting direct solutions is not easy, where defect depth is also variable.

Another important application where inverse approach has been utilized is in estimation of scale thickness in boilers. Deposition of scales on the internal wall of boiler deters heat transfer leading to failure of boiler. Awasthi et al. [37] built a RC model with controlled voltage source to model a boiler with scaling undergoing pulse thermography. The resultant thermal response of boiler wall was analysed at half power point i.e. where temperature is 0.707 times of maximum and estimated the slope at that point. Similar analysis was done for a boiler without a scaling. The comparison between the results obtained for boiler with scaling and without scaling was used to determine the relative thickness variation due to scale deposition in boiler wall.

4. CONCLUSIONS This work presented the use of electro-thermal approach to acquire the solutions of active infrared thermography problems, for non-destructive evaluation of materials and structures. An overview of the various electro-thermal models for active infrared thermography problems used in procurement of direct and inverse solutions for different applications, from the thermal response of the object, have been illustrated. The application of direct solutions to understand the inuence of different heating parameters (heat ux intensity, heating duration and shape of heating waveform) has been presented by this approach, which facilitates the determination of the optimum heat source characteristics for enhanced visibility. The use of electro-thermal approach was also found to be useful for detection and characterization of a variety of defects in a multi-layered structure (like solar photovoltaic module) ranging from electrical to structural defects. Estimation of defect parameters and material properties from thermal proles requires inverse approach. In this context, various applications of inverse solutions to nd defect characteristics such as defect area, depth, and thickness and material properties such as diffusivity have been illustrated. To obtain analytical expressions of these parameters using inverse solution, complex resistancecapacitance (RC) circuits based on various discussed models have been solved by network analysis, delay-time analysis, and signal processing techniques, which are well established methods in the eld of electrical engineering. The presented approach is not limited to particular defect and can be used to model other types of defects through equivalent electrical March 2018

circuits. Also, electro-thermal approach can be implemented for other types of active infrared thermography techniques for obtaining direct and inverse solutions.

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An Aperiodic Digitized Frequency Modulated Thermal Wave Imaging for Testing and Evaluation of Carbon Fibre Reinforced Polymer Materials Geetika Dua, Vanita Arora and Ravibabu Mulaveesala* InfraRed Imaging Laborotory, Department of Electrical Engineering, Indian Institute of Technology Ropar, Nangal Road, Rupnagar, Punjab, INDIA 140001 TEL: +91 1881 242118 FAX: +91 1881 223395 *Email: ravibabucareiitd@yahoo.co.in ABSTRACT

Carbon ber reinforced polymer composites are widely used materials in various industries due to their low weight and improved mechanical properties. However, these materials are subject to impact, fatigue and other damages. With their increased use, there is a tremendous need to develop efcient non-destructive testing methods for inspecting these materials. Infrared thermography is a safe non-destructive testing technique that has a fast inspection rate. In this paper, an aperiodic infrared thermographic technique named as digitized frequency modulated thermal wave imaging with appropriate post-processing method is proposed to detect the defects present in carbon ber reinforced polymer composite materials.

1.0

INTRODUCTION

H

igh strength to weight ratio, excellent corrosion resistance and withstanding capabilities for high impact loads make the usage of ber reinforced composite materials in a wide variety of applications. Among various reinforced polymer composites, Carbon Fiber Reinforced Polymer (CFRP) is extensively used in various industries such as aeronautical, aerospace, automotive, civil, mechanical and naval due to its high mechanical stiffness, low density, and good chemical stability[1-9]. CFRP is composed of bonding carbon ber laments to a polymer matrix. The quality of the material is required to be good enough to meet the requirements of the aforementioned applications. However, invisible defects such as ber breaks, voids, cracking, delaminations and interfacial debonding may be developed in these materials either during their manufacturing process or their in-service. The introduced defects can signicantly reduce the strength and degrade the mechanical performance of structural components, therefore, it is essential to inspect the materials for defect detection and characterization in order to avoid catastrophic failure. Much attention has to be paid in order to develop preferably a remote and whole-eld NonDestructive Testing and Evaluation (NDT&E) technique for the detection and evaluation of defects.

Among various NDT&E techniques, InfraRed Thermography (IRT) has gained wide acceptance and plays a crucial role due to its merits such as non-contact, whole-eld, safe and fast inspection capabilities for characterization of various materials irrespective of their electrical, optical and magnetic properties[10-18]. IRT involves the mapping of the temperature over the test specimen for characterizing its surface and sub-surface defects. IRT detects the infrared radiation emanating over the test material, March 2018

converts it into a temperature map which is named as a thermogram [1]. IRT can be applied in two ways; passive and active. The passive approach is used when the defective regions in the test material provide enough thermal contrast with respect to the background. However, due to the limited depth of detection and inability in providing the quantitative assessment regarding the sub-surface defects in the test material, limits its applicability for NDT&E applications. On the contrary, to detect defects located deep inside the test material with enough thermal contrast, active thermography is preferred. This paper is mainly focused on the applications of active IRT for testing and evaluation of composites.

Among various active thermographic techniques, recently proposed aperiodic thermal wave imaging methods gained wide acceptance in NDT&E research community due to their higher test resolution and sensitivity. This paper highlights the capability of one of the aperiodic thermographic technique, i.e. Digitized Frequency Modulated Thermal Wave Imaging (DFMTWI) technique in visualizing defects present in Carbon Fiber Reinforced Polymer (CFRP) sample. Better test resolution and sensitivity can be achieved with DFMTWI technique by probing more energy into the test sample, introducing harmonics along with the desired band of fundamental frequency components. This leads to an improved depth resolution for near surface defects by exhibiting better pulse compression properties (compressed to a narrow duration) due to increased probing bandwidth of incident thermal excitation.

This paper presents applicability of the DFMTWI technique along with widely used post-processing techniques for testing and evaluation of CFRP material for identifying subsurface defects such as blind holes and inclusions [10-18].

31

THEORY

A. DIFFUSION OF MODULATED THERMAL WAVES Theoretical analysis of surface temperature evolution as a result of the incident thermal waves is carried out by chosing the one dimensional heat equation to a homogeneous, isotropic and semi-innite media, in the absence of any heat sink or source. The one dimensional heat equation is given by: ......... (1) where T (x', t') is the temperature response over the surface, α is the thermal diffusivity, x' is the spatial coordinate in the direction of heat ow and t' indicates the time.

The expression for Digitized Frequency Modulated (DFM) heat ux is given as: ......... (2) where In this, fo indicates the initial frequency, B is the bandwidth and τ indicates the duration of excitation. In solving equation (1) for DFM heat ux (equation (2), the thermal diffusion length μ can be obtained as follows: ......... (3)

Where Ref(t)is the chosen reference temperature data. Cross correlation of the delayed thermal proles with the chosen reference results in sinc shaped compressed pulse which can contribute for contrast in Cross Correlation (CC) image at any chosen instant of time. This temperature contrast over the test material discriminate different defects present in it.

3.0 RESULTS AND DISCUSSION For illustrating the feasibility of the proposed DFMTWI approach with appropriate post-processing methods, a CFRP specimen is considered. The CFRP sample schematic of 82 mm length, 151 mm breadth, with thickness of 4.2 mm is as shown in Figure.1. It contains 5 circular at-bottom holes (a, b, c, d, e) of varying diameters as subsurface defects which are placed at a depth of 2.16 mm from the front end of the test sample. The defects a, b and c are adhered with a metallic backing of coppercovered steel in order to study the effect of backing material on defect detection, where as defects e and f are considered as 82 voids.

151

2.0

Figure.1. Schematic of the experimental CFRP sample

Where, B. Frequency domain analysis In frequency domain analysis, the captured thermal response over the sample is analyzed using Discrete Fourier Transform (DFT) to extract the phase and magnitude information. One dimensional DFT is computed for each pixel on the mean removed temperature distribution data (Tmean removed (x, y, t)) (where x, y are the spatial coordinates and t is the index of image sequence) in the eld of view as[1]:

The dimensional details of the defects are as given in Table.1. (mm)

Table.1: Diameter of subsurface defects located in CFRP sample

(4) Where k is the bin number, N is the total number of frames, Re (T (x, y, k)) and Img (T (x, y, k)) are the real and imaginary parts of the T(x, y, k) respectively.

The magnitude images are then reconstructed from the computed real and imaginary components as[1]: ......... (5)

The test specimen is allowed to undergo a known controlled digitized frequency modulated thermal stimulation sweeping its fundamental frequency from 0.01 Hz to 0.1 Hz in 100 s duration. The infrared camera captures the thermal response over the sample surface as a sequence of images at every 0.05 s time intervals for duration of 100 s during active heating. The experimental setup used to conduct the experiment is as shown in Figure.2.

Further, the phase images are reconstructed by using the real and imaginary components as[1]: ......... (6) C. Pulse compression Correlation based pulse compression is carried by cross correlation of the mean removed captured temperature response with the chosen reference temperature response as below[12,15,17]: .......... (7)

Figure.2. Schematic of infrared imaging system used for experimentation March 2018

32

Further, the mean raise in captured temperature distribution is removed using an appropriate t. The chosen raw thermal image obtained from the mean removed temperature distribution is a shown in Figure.3.

Figure.3. Mean remove thermal image

The obtained mean removed thermal prole is processed using conventional frequency domain analysis and recently introduced time domain based pulse compression approach as described in the theory section. The resultant images obtained after postprocessing are as shown in Figure.4 and Figure.5. Figure.4.(a) shows the frequency domain magnitude image obtained at a frequency of 0.02 Hz and Figure.4.(b) shows the frequency domain phase image obtained at a frequency of 0.03 Hz.

Figure.4. Resultant images with frequency domain analysis (a). magnitude image obtained at 0.02 Hz (b). phase image obtained at 0.03 Hz

Figure.5 shows the corresponding pulse compression image obtained at a time instant of 45 s

Results obtained clearly shows detection capabilities of the proposed DFMTWI for the defects with different lateral dimensions and of different material properties.

4.0 CONCLUSION The proposed work highlights the capability of the DFMTWI technique to visualize different defects present in the CFRP sample. Results clearly indicates that the pulse compression image is superior to that of frequency domain approaches.

5.0

REFERENCES

[1] Maldague, X.(2001) Theory and Practice of Infrared Technology for Nondestructive Testing, p. 72. (Wiley, New York).

[2] Vavilov, V., Maldague, X., Dufort, B., Robitaille, F., Picard, J. Thermal nondestructive testing of carbon epoxy composites: detailed analysis and data processing(1993) NDT and E International, 26 (2), pp. 85-95.

March 2018

[3] Henneke II, Edmund G., Reifsnider, Kenneth L., Stinchcomb, Wayne W. Thermography - An NDI method for damage detection.(1979) Journal of Metals, 31 (9), pp. 11-15.

[4] Yang, R., He, Y. Optically and non-optically excited thermography for composites: A review (2016) Infrared Physics and Technology, 75, pp. 26-50.

[5] He, Y., Yang, R., Zhang, H., Zhou, D., Wang, G.Volume or inside heating thermography using electromagnetic excitation for advanced composite materials(2017) International Journal of Thermal Sciences, 111, pp. 41-49.

[6] Silipigni, G., Burrascano, P., Hutchins, D.A., Laureti, S., Petrucci, R., Senni, L., Torre, L., Ricci, M. Optimization of the pulsecompression technique applied to the infrared thermography nondestructive evaluation(2017) NDT and E International, 87, pp.

100-110.

[7] Gong, J., Liu, J., Qin, L., Wang, Y. Investigation of carbon ber reinforced polymer (CFRP) sheet with subsurface defects inspection using thermal-wave radar imaging (TWRI) based on the multi-transform technique(2014) NDT and E International, 62, pp.

130-136.

[8] Liu, J., Liu, X., Wang, Y. Technology of linear frequency modulation infrared thermal-wave imaging for nondestructive testing(2012) Hongwai yu Jiguang Gongcheng/Infrared and Laser Engineering, 41 (6), pp. 1416-1422.

[9] Cao, M.S., Ostachowicz, W., Bai, R.B., Radzie ski, M. Fractal mechanism for characterizing singularity of mode shape for damage detection (2013) Applied Physics Letters, 103 (22), art. no.

221906.

[10] Siddiqui, J.A., Arora, V., Mulaveesala, R., Muniyappa, A.Infrared Thermal Wave Imaging for Nondestructive Testing of Fibre Reinforced Polymers (2015) Experimental Mechanics, 55 (7), pp. 1239-1245.

[11] Dua, G., Mulaveesala, R., Siddique, J.A.Effect of spectral shaping on defect detection in frequency modulated thermal wave imaging(2015) Journal of Optics, 17 (2).

[12] Arora, V., Siddiqui, J.A., Mulaveesala, R., Muniyappa, A.Pulse compression approach to nonstationary infrared thermal wave imaging for nondestructive testing of carbon ber reinforced polymers(2015) IEEE Sensors Journal, 15 (2), art. no. 6936841, pp.

663-664.

[13] Mulaveesala, R., Ghali, V.S., Arora, V. Applications of nonstationary thermal wave imaging methods for characterisation of bre-reinforced plastic materials(2013) Electronics Letters, 49 (2), pp. 118-119.

[14] Mulaveesala, R., Venkata Ghali, S. Coded excitation for infrared non-destructive testing of carbon ber reinforced plastics (2011) Review of Scientic Instruments, 82 (5), art. no. 054902, .

[15] Mulaveesala, R., Ghali, V.S. Cross-correlation-based approach for thermal non-destructive characterisation of carbon bre reinforced plastics(2011) Insight: Non-Destructive Testing and Condition Monitoring, 53 (1), pp. 34-36.

[16] Ghali, V.S., Mulaveesala, R., Takei, M.Frequency-modulated thermal wave imaging for non-destructive testing of carbon berreinforced plastic materials(2011) Measurement Science and Technology, 22 (10), art. no. 104018.

[17] Ghali, V.S., Jonnalagadda, N., Mulaveesala, R.Threedimensional pulse compression for infrared nondestructive testing(2009) IEEE Sensors Journal, 9 (7), pp. 832-833.

[18] Mulaveesala, R., Tuli, S.Theory of frequency modulated thermal wave imaging for nondestructive subsurface defect detection(2006) Applied Physics Letters, 89 (19), art. no. 191913.

34

CONTAMINATION MONITOR (CM-10) The CM-10 is a rugged, versatile and accurate instrument, designed to measure a wide range of radiation contamination.

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FLIR T1030SC - HIGH PERFORMANCE HANDHELD THERMAL IMAGING CAMERA FOR R&D APPLICATIONS The T1030sc is a portable, high-speed, high definition infrared imaging and measurement camera. It’s designed for engineers, researchers, and scientists who need the highest resolution and sensitivity possible in a flexible, battery powered handheld package. Built on 50 years of experience, this camera records full 1024 x 768 resolution images at 30 frames per second. The T1030sc lets you capture lossless HD radiometric imagery at up to 120 Hz which you can view, acquire, analyze, and share in FLIR ResearchIR Max or MathWorks® MATLAB. FEATURES · The T1030sc offers a thermal sensitivity of < 20 mK (NETD), more than twice the industry standard · Wide temperature ranges with calibrations up to 2000°C · FLIR OSX™Precision HDIR Optical System features an ultrasonic drive, ambient temperature drift compensation, and parasitic radiation protection.

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FILM VIEWER FOR VIEWING FILMS IN RADIOGRAPHIC TESTING In the past, the only way of increasing brightness was to increase wattage. Bulbs such as Photo Flood bulbs and Quartz Halogen are bright, but most of their energy is lost in heat light.

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PULSED EDDY CURRENT TESTING (PECT) EQUIPMENT PECT is an advance electromagnetic inspection technology which majorly detects corrosion hidden under insulation in ferrous material. It is an effective method of quantifying defects in multi-layer structure.

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20000

3960

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8000

1440

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EVENTS PAGE

5000

900

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MEMBERSHIP

5000

900

5900

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5000

900

5900

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5000

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8000

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38

NDE 2017 - HIGHLIGHTS 14th to 16th December, 2017 Organised by : ISNT, Chennai Chapter National Conference & Exhibition on Non Destructive Evaluation NDE – 2017 with the theme “SAFETY, RELIABILITY & SUSTAINABILITY” was successfully conducted during 14-16 Dec, 2017 at Chennai Trade Centre, Chennai.

Pre-conference Tutorials were organized on 12th and 13th December, 2017 at Hotel GRT Grand, Chennai on four major topics viz. NDE in Automobile Industries, NDE in Aerospace Industries, NDE in concrete & infrastructure and NDE in Oil & Gas Industries. These topics were handled by eminent faculties.

About 105 candidates attended the Pre-conference Tutorials. Kit containing course material was distributed at the beginning and certificate of participation was also distributed.

The Conference began on 14 Dec, 2017 on an auspicious note with lighting of lamp followed by invocation song Thamizh Thaai Vazhthu. Prof. Krishnan Balasubramanian, chairman of the conference welcomed the chief guest and the gathering. Sri D.J.Varde, National President, ISNT briefed the activities of ISNT during the yester years. Our chief guest honourable governor of Tamilnadu Thiru. Banwarilal Purohit delivered the inaugural address. During his inaugural speech the chief guest appreciated ISNT for their services rendered in the field of technical education and in spreading the knowledge on NDT&E. Thiru. Umakanth Anand, senior vice president, Reliance Industries Ltd. was our guest of honour who inaugurated the exhibition. Thiru. T.S.G. Narayannen, technical advisor and secretary – Boiler BoardMinistry of Commerce and Industry, New Delhi was guest of honour to release the souvenir.

Shri Rajul R Parikh, Gen. Sec-ISNT announced the names of awardees for National NDT Awards 2017 and the chief guest presented the awards to the awardees. Shri S. Subramanian, convener of the seminar delivered the vote of thanks and the session ended with the national anthem.

Mr. Arun Kumar Bhaduri, IGCAR, Kalpakkam delivered Prof A K Rao memorial lecture and Mr. Francois Champigny, COFREND (French Society of NDT) delivered Shri V S Jain memorial lecture.

Dr. B.Venkatraman was the Chairperson for the Memorial Lectures.

March 2018

Chief guest lighting the lamp

Guest of honor Shri.Umakanth Anand at the inauguration

Momento presented to guest of honor by President Shri. D.J. Varde

Momento presented to chief guest

by Shri. Krishnan B, NDE chairman

Momento presented to guest of honor Shri Narayannen by Convener

Shri. Umakanth Anand, Senior Vice President, Reliance Industries

Shri. D.J.Varde, National President, ISNT

NDE 2017 Chairman, Shri. Krishnan Balasubramanian

Shri. S.Subramanian, Convener - NDE 2017

Shri. Rajul Parikh, Gen.Sec - ISNT

Chief Guest presenting NDT Awards

NDE 2017 Souvenir

39

Technical committee under the chairmanship of Shri Paritosh P.Nanekar, Chairman and Dr. Prabhu Rajagopal, Co-Chairman put in exemplary work in ensuring large number of authors with technical papers. For identifying the best papers, the sessions’ chairmen were given evaluation sheets prescribing marks for each attribute.

Lifetime Achievement Award presented to Shri. B.K.Shah

Best Chapter Award-Pune Team

Shri. Arun Kumar Bhaduri, delivering Mr. Francois Champigny, COFREND A K Rao memorial lecture delivering V S Jain Memorial lecture

Technical talk

Technical talk

E- Posters

Exhibition stalls

Chief guest visiting NDE stalls

Similarly, for selecting the best posters, a three-member committee was constituted with the assigned responsibility of interactions with the poster presenters. Souvenir was brought out in a defeating manner by Shri T.V. Navanithakrishnan as Chairman, Souvenir Committee.

To carry back home as a memorable, each delegate including invitees were given a kit containing souvenir, CD of the presented papers, program booklet, pad and pen For success of any conference, delegates are the force behind and Shri S.R. Ravindran, Chairman – Registration Committee played a key role in this area of NDE 2017. In a nutshell, NDE 2017 set a new benchmark by attendance of 866 delegates, 142 Oral Presentations, 47 Invited Talks, 2 Memorial Lectures, 2 Plenary talks, 8 parallel sessions (3 sessions on 1st Day and 6 sessions each on 2nd & 3rd Day) and 72 E-Posters (introduced for the first time in our NDE Conference).

Any conference will not be able to function without the financial support of sponsors and supporters. Our list of sponsors and supporters have exceeded the hither to achieved numbers and is as given below. Baker Hughes (GE), Olympus, Reliance Industries Ltd, Powergear Ltd, Blue Star Engineering & Electronics Ltd, TWI Training & Examination Services, FLIR, Lucid Software Ltd, Electronic & Engineering Co. (I). Pvt. Ltd., OSERIX, Eddyfi, Proceq, Vibrant NDT Services Pvt. Ltd., V.J. Imaging Technologies Pvt. Ltd., Indian Oil, Electro-magfield Controls And Services, AES, Modsonic Instruments Manufacturing Company Pvt. Ltd., Verasonics, ACS-Solutions GmbH, Midhani, Technofour, TVS, Bharat Petroleum, AFCONS Infrastructure Limited, IGP Engineers Pvt Ltd, DRDO, DAE, ISRO, Air Bus Boeing, CPCL, BGR Energy & Micro Lab. Support from the sponsors for the mega event NDE 2017 was possible because of the tireless effort put forth by Prof. Krishnan Balasubramaniam, Chairman – NDE 2017, who led the team from the front and motivated them to work with its full potential and focused aim, unmindful of time.

March 2018

40

On 14th December, a “Classical cum Fusion based Dance Programme” by Smt. Sudha Vijayakumar and troup. On 15th December, 2017 evening a Light Music Programme comprising of 25 musicians was organised by Shri Sunder & Party at Hotel Le Royal Meridien, Chennai, well organized by Shri R. Ganesan, Chairman – Events Committee.

The Conference was well supported by 85 stalls booked by the 69 exhibitors from all over the world. Delegation from France and COFREND was the added advantage. Shri R. Vivek, Chairman, Exhibition Committee and Shri P. Anandan, CoChairman coordinated well with the exhibitors and took care of their requirements. The structure of the exhibitions was well designed by M/s. Deko Systems. Shri G. Ramachandran, Chairman, Industrial Relations, NDE 2017 took the efforts in alluring Shri Gautham Roy, Managing Director, CPCL to be the Chief Guest for the Valedictory function on 16 th Dec, 2017. The Chief Guest gave a brief, but inspiring valedictory talk on the journey of NDE in India and gave away prizes to the winners of poster session, oral presentations & exhibition stalls.

Chief guest honored at Valedictory Function

NDE 2017 team with French Delegation

Cultural Show - Fusion Dance Performance

Convener, NDE 2017 wishes to thank Shri V. Pari, Advisor - NDE 2017 for his valuable guidance and advise extended to team NDE 2017.

Everyone left the Chennai Trade Centre, Chennai with sweet memories, looking forward to an exciting NDE 2018 at Mumbai.

Music Programme

A houseful of audience at the Cultural Show

NDE 2017 Team ; President Shri. R.J.Pardikar along with Past President Shri. V.Pari, Chairman NDE 2017-Shri. Krishnan Balasubramanian, Convener NDE 2017-Shri. S.Subramanaian

March 2018

42

NGC/NCB Meetings Calendar 2018 MONTH

MEETING

DAY/DATE OF MEETING

TENTATITVE TIMINGS

NCB Meeting

Saturday, 2.06.2018

2.00 - 5.00 pm

VENUE

June 2018

September 2018

CHENNAI NGC Meeting

Sunday, 03.06.2018

Chapter Chariman Meet

Saturday

NCB Meeting

NGC Meeting

December 2018

NCB Meeting & NGC Meeting

22.09.2018

Sunday, 23.09.2018

10.00 - 3.30 pm

10.00 - 1.00 pm

KOLKATA

2.00 - 5.00 pm

10.00 - 3.30 pm

During NDE 2018

AC Magnetization Yoke

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43

NGC/NCB Office Bearer’s List NATIONAL GOVERNING COUNCIL MEMBER'S LIST (NGC)

PAST PRESIDENTS

EX-OFFICIO MEMBERS

38. Shri V.R. Deenadayalu

50. Dr. M.T. Shyamsunder

39. Shri K. Balaramamoorthy

51. Shri P.P. Nanekar

1. Shri.R.J.Pardikar

9. Shri Nerurkar K.A

40. Shri Ramesh B. Parikh

President, ISNT

Hon. Treasurer - ISNT

41. Shri A. Srinivasulu

Mobile : 9003096843

Mobile : 0'9822525518

42. Shri Dr. Baldev Raj

53. Shri T. Loganathan

r.j.pardikar@gmail.com

pradeepndt@vsnl.net

43. Shri S. I. Sanklecha

54. Dr. Krishnan Balasubramaniam

44. Shri Shri D.M.Mehta

55. Shri V. Pari

2. Dr. B. Venkatraman President - Elect – ISNT Mobile : 9443638974 bvenkat@igcar.gov.in

10. Shri D.J. Varde

45. Shri K. Viswanathan

Immediate Past President - ISNT

46. Shri Dilip P. Takbhate

Mobile : 09821131522

47. Shri K. Thambithurai

djvarde@gmail.com

48. Dr. P. Kalyanasundaram

PERMANENT INVITEES

49. Shri. V.Pari

57. Shri G. Ramachandran

3. Shri Diwakar D. Joshi Vice President, ISNT

52. Shri S. Viswanathan

56. Dr. P. Kalyanasundaram

MEMBERS

Mobile : 9822263475

11.Shri. Anil V. Jain

diwakarj@gmail.com

12.Shri. Anil Kumar Das

NATIONAL CERTIFICATION BOARD MEMBER'S LIST (NCB)

13.Shri. V Deepesh 4. Dr. Krishnan

14.Shri. Dharamveer Singh

Balasubramaniam

15.Shri. Dipankar Goutham

Vice President - ISNT

16.Shri. R.G.Ganesan

Mobile : 9840200369 balas@iitm.ac.in

17.Shri. Gopalakrishnan 18.Shri. S. Hari Krishna

1.

Dr. M.T. Shyamsunder

18. Shri Chintamani Khade

2.

Shri P.P. Nanekar

19. Shri G.V.S. Hemantha Rao

3.

Shri T. Loganathan

20. Shri. Uday B Kale

4.

Shri S. Viswanathan

21. Shri Sadasivan. N

5.

Shri B. K. Shah

22. Shri. Vikas Neeraj 23. Smt. Navita Gupta

19.Shri. G. Levin 5. Shri Jaitheerth Joshi Vice President – ISNT Mobile : 9440049272 joshidrdl@gmail.com 6. Shri P. Mohan Hon. Gen. Secretary - ISNT Mobile : 0'94901 67000 metsonic@sify.com

20.Shri. V Manoharan

REGIONAL CONTROLLER OF

21.Shri. Mukesh Arora 22.Shri. C.K. Muktopadhyay

6.

Shri S.K. Bandyopadhyay

23.Smt. Navita Gupta

7.

Shri S.R. Ravindran

24.Shri. Bhausaheb K Pangare

8.

Shri Jayaprakash Hiremath

25.Shri.Partha Pratim Brahma PATRON

26.Shri.Rajul R. Parikh 27.Shri.Ravibabu Mulaveesala

24. Shri M. Venkata Reddy

EXAMS

9.

Shri K. Balaramamoorthy

EX-OFFICIO MEMBERS 25. Shri R.J.Pardikar 26. Dr. B. Venkatraman 27. Shri Diwakar D. Joshi 28. Dr. Krishnan Balasubramaniam 29. Shri. Jaiteeth Joshi

28.Shri.S.R. Ravindran MEMBERS

30. Shri. P. Mohan

7. Shri Samir K. Choksi

29.Shri.Sadasivan. N 30.Smt.Sangita Kapote

10 Shri. R.B. Bhardwaj

31. Shri Nerurkar K.A

Hon. Jt. Secretary - ISNT

31.Shri.Shashidar Pallaki

11. Shri V. Manoharan

32. Shri. V. Pari

Mobile : 9821011113 Choksiindia@yahoo.co.in

32.Shri.S. Shendkar

12. Shri Avinash U. Sonuwane

33.Shri.S. Subramanian

13. Shri R. Sundar

CHAPTER REPRESENTATION

8. Shri Bikash Ghose

34.Shri.Sunil Gophan

14. Shri Phani Babu

33. Shri Hemant Madhukar

Hon. Jt. Secretary - ISNT

35.Shri.G. Surya Prakash

15. Shri Dilip Gatti

34. Dr. Krishnan Balasubramaniam

Mobile : 9890127524

36.Smt. Umrani K.J

16. Shri ME. K.A Nerurkar

35. Shri M. S. Shendkar

ghose.bikash@hemrl.drdo.in

37.Shri.M.N.V. Viswanath

17. Shri Bikash Ghose

36. Shri G.V.S. Hemantha Rao

March 2018

45

ISNT LEVEL III COURSES SN

Method

Course Duration

Exam Date

Course fees Including 18% GST (INR)

1

Ultrasonic Testing

02/04/2018 to 06/04/2018

08/04/2018

18,880 (16,000+2,880)

Specic – Weld

Do

Specic– Cast/ Wrought 2

Basic

3

Radiographic Testing

4

Specic– Cast/ Wrought

Do

2950 (2500+450)

Magnetic Particle Testing

23/04/2018 to 25/04/2018

Liquid Penetrant Testing

** Visual Testing

11,800 (10,000+1,800)

01/05/2018

13/05/2018

4720 (4000+720)

2950 (2500+450) 18,880 (16,000+2,880)

4720 (4000+720)

Specic – Tubes

Do

2950 (2500+450)

Specic – Rods / Wire

Do

2950 (2500+450)

** Leak Testing

14/05/2018 to 18/05/2018

19/05/2017

2950 (2500+450)

2950 (2500+450)

Do 07/05/2018 to 11/05/2018

2950 (2500+450)

2950 (2500+450) 18,880 (16,000+2,880)

Do

Specic– Cast/ Wrought

4720 (4000+720) 2950 (2500+450)

Do 06/05/2018

2950 (2500+450)

2950 (2500+450) 11,800 (10,000+1,800)

Do

02/05/2018 to 05/05/2018

4720 (4000+720)

2950 (2500+450)

2950 (2500+450)

Do 28/04/2018 to 30/04/2018

Specic – Weld

** Eddy Current Testing

27/04/2018 Do

Specic– Cast/ Wrought

8

4720 (4000+720) 2950 (2500+450)

Specic – Weld

7

4720 (4000+720)

Do

Specic– Cast/ Wrought

6

22/04/2018

2950 (2500+450) 18,880 (16,000+2,880) 18,880 (16,000+2,880)

Specic – Weld

Specic – Weld

5

15/04/2018

4720 (4000+720)

Practical Exam Fees (If applicable) Including 18% GST (INR) 2950 (2500+450)

2950 (2500+450)

Do 09/04/2018 to 13/04/2018 16/04/2018 to 20/04/2018

Exam Fees Including 18% GST (INR)

21,240 (18,000+3,240)

4720 (4000+720)

Specic – Weld

Do

2950 (2500+450)

Specic– Cast/ Wrought

Do

2950 (2500+450)

2950 (2500+450)

2950 (2500+450)

ASNT LEVEL III COURSES Course Duration

Dates

Ultrasonic Testing - UT

5 days

2nd to 5th April 2018

16,000

2,880

18,880

Basic

5 days

9th to 13th April 2018

16,000

2,880

18,880

Radiographic Testing

5 days

16th to 20th April 2018

16,000

2,880

18,880

Magnetic Particle Testing - MT

3 days

23rd to 25th April 2018

10,000

1,800

11,800

Liquid Penetrant Testing - PT

3 days

28th to 30th April 2018

10,000

1,800

11,800

Visual Testing

4 days

2nd to 5th May 2018

16,000

2,880

18,880

Eddy Current

5 days

7th to 11th May 2018

16,000

2,880

18,880

Leak Testing - LT*

5 days

14th to 18th May 2018

18,000

3,240

21,240

Method

Course Fees in INR

Tax @18% in INR

Total in INR

March 2018

46

NDE Patents - Compiled by Dr. Shyamsunder Mandayam, Chairman-National Certication Board, ISNT Continuing our endeavor to provide you updates on NDE and Inspection relate patents, listed below are a few patents from the areas of Infrared Thermography which were issued by USPTO in the last few years. If any of the patents are of interest to you, a complete copy of the patent including claims and drawings may be accessed at http://ep.espacenet.com/ United States Patent 9,857,227 Flash thermography device having moveable arm for inspecting internal turbine components Inventors: Boudin Dustin C, Hatcher Jr. Clifford, Kulkarni Anand A, Licata Kevin Assignee: Siemens Energy Inc, Orlando A ash thermography device for generating an infrared image of a turbine component located inside a turbine. The device includes a ash enclosure having an aperture. A ash source is located in the aperture wherein the ash source generates a light pulse that heats the turbine component. The device also includes an infrared sensor for detecting thermal energy radiated by the turbine component wherein the radiated thermal energy is transmitted through the aperture to the infrared sensor to enable generation of an infrared image of the turbine component. Further, the device includes moveable arm that holds the ash enclosure and infrared sensor wherein the arm is inserted through an opening in the turbine to enable positioning of the ash source and infrared sensor in close proximity to the turbine component.

United States Patent 9,509,923 Continuous infrared thermography monitoring and life management system for heat recovery steam generators Inventors: Pandey Achalesh Kumar, Nirmalan Nirm Velumylum, Venugopal Manoharan, Rogers David Lee, Laen John Brandon, Giametta Lucy Summerville Assignee: General Electric Company (Schenectady, NY) A method for continuously monitoring the working condition of a heat recovery steam generator ("HRSG") using infrared thermography, comprising the steps of identifying target locations inside the HRSG, positioning one or more infrared cameras to continuously monitor and record the temperature at each target location, generating continuous thermographic images corresponding to selected components and locations at each target locations, comparing the continuous thermographic images to corresponding, stored base line images and generating a set of comparative data reports in real time for each target location in order to predict the life span or potential failure of HRSG components.

United States Patent 9,519,844 Infrared thermographic methods for wrinkle characterization in composite structures Inventors: Thompson Jeffrey G, Georgeson Gary E, Holmes Tyler M, Tat Hong Hue Assignee: The Boeing Company (Chicago, IL) Methods for identifying and quantifying wrinkles in a composite structure by processing infrared imaging data. Temperature versus time proles for all pixels in the eld of view of an infrared camera are calculated, enabling thermal signatures to be produced. By comparing the thermal signature of the part under test with the thermal signature of a reference standard representing a similar part having wrinkles of known size and shape, the presence of wrinkles can be detected. The wrinkle wavelength can be determined by measuring the infrared image and applying a transfer function. If the geometric information acquired using infrared thermography does not reach a certain quality factor (for a quality prediction) or is incomplete, an ultrasonic transducer array probe running wrinkle quantication software can be rotated into position and scanned over the wrinkle area. The ultrasonic imaging data can be combined with the infrared imaging data to enable an improved quantication of the wrinkle geometry.

March 2018

United States Patent 9,250,134 Nondestructive testing active thermography system and method for utilizing the same Inventors: Shepard Steven M, Young Timothy, Beemer Maria Frendberg Assignee: Thermal Wave Imaging, Inc. (Ferndale, MI) An assembly including an optically excited infrared nondestructive testing active thermography system is disclosed. The optically excited infrared nondestructive testing active thermography system includes one or more illumination sources, at least one rst reector, at least one second reector and a computing resource. The at least one rst reector is arranged about the one or more illumination sources. The at least one rst reector has a near focal point and a far focal point. The one or more illumination sources is/are positioned at least proximate the near focal point of the at least one rst reector. The at least one second reector is positioned at least proximate the far focal point. The computing resource is communicatively-coupled to a motor that is coupled to the at least one second reector for manipulating the at least one second reector between at least: a rst spatial orientation and a second spatial orientation. At least one of the rst spatial orientation and the second spatial orientation results in the at least one second reector reecting light that originates from the one or more illumination sources. The light is directed toward the at least one second reector as a result of the light being directly propagated from the one or more illumination sources and reected by the at least one rst reector. A method is also disclosed.

United States Patent 8,471,207 Method for the automatic inspection of a welding seam by means of heat ow thermography Inventors: Louban Roman, Dottinger Christoph, Stolz Pete, Bohm Manfred In a method for the automatic inspection of a welding seam using heat ow thermography, a feature vector is established which represents a time course of a detected heat ow. The feature vector is used to determine, from a series of thermal images, a rst characteristic thermal image, which corresponds to a minimum heat ow through an object to be examined, and a second characteristic thermal image, which corresponds to a maximum heat ow through the object, wherein a heat ow directly from an excitation source has already dissipated. A suitable thermal image is used from the series of thermal images to detect and evaluate the welding seam in relation to defects of various defect types, for each defect type, the characteristic thermal images being used as references to determine the respective suitable thermal image.

United States Patent 8,506,159 Method for detecting defect in material and system for the method Inventors: Nakagawa Junichi, Ito Tadayuki , Nishiyama Tetsuo, Doki Masahiro, Saito Kozo Gharaibeh Belal , Chuah Keng Hoo, Salaimeh Ahmad ], Yamamoto Masahiro, Takeuchi Tomoya, Ito Kazufumi , Huang Huaxiong , Bohun Sean C. Assignee: Nippon Steel & Sumitomo Metal Corporation (Tokyo, JP) A defect on the surface or in the surface layer of a moving material can be detected by using a method comprising steps of: heating the surface of the material, obtaining thermal image data of the surface of the material using an infrared thermography camera while the surface of the material is being heated up at the heating step or being cooled down after heating, and detecting the defect by calculating Laplacian with respect to temperature of the surface represented by the thermal image data. When the thermal image data is obtained while the material is being heated up, a heating device and the camera is arranged so that thermal energy emitted from the heating device is reected by the material to come into the camera

47

Crossword Puzzle Infrared Imaging 12 In general, the changes in one wave train caused by another, in thermal scanning & imaging, image luminant contrast; 15 The loss of radiation intensity due to the blocking of a portion of the beam bundle between the focused point on the object plane & the focused point on the image plane 18 The portion of a color display which is related to display intensity, how bright a color is perceived by the eye 19 Indicating reď&#x20AC;&#x201A;ections or a shiny surface 20 The relationship between the brightest intensity on a display & the dimmest 21 essentially refers to the minimum necessary temperature difference required, in order for the difference to be resolved by a thermal imaging camera

Across 5 A hole or opening limits the amount of radiation used by an optical system 7 A thermal photograph generated by scanning an object or scene 8 Any undesired factor that distorts an image 9 The ratio of a target surfaceâ&#x20AC;&#x2122;s radiance to that of a black body at the same temperature, viewed from the same angle and over the same spectral interval

DOWN 1 An object in which the object plane which the system can focus on & analyze 2 The alignment of the sensing axis of a detector to the display axis so that the center of the display screen represents straight ahead to the imager 3 Forward looking Infrared 4 A radiation object whose emissivity is a constant value less than unity (1.0), over a speciď&#x20AC; c spectral range 6 The property of some polar materials, such as ferro electrics, where in the polarization changes with temperature 10 An infrared sensor that operates on the principle that the resistance of certain materials varies with temperature 11 A temperature scale that is directly related to the heat energy within a body 13 The amount that a cooling cycle in a thermal feedback control system cools beyond the set point 14 In thermography, an material is one that does not transmit thermal infrared energy 16 Areas or lines of constant temperature 17 The loss of foement of spot size and halation on a cathode ray tube

Puzzle Solution December 2017 Magnetic Particle Testing

To Advertise in JNDE Contact 022-61503839 or Email isnt.jnde@gmail.com March 2018