, 44:79 | Cite as

Probing defects in friction stir welding process using temperature profile

  • B Das
  • S Pal
  • S BagEmail author


Detection of defects in friction stir welding process is a challenging task since most of the defects are internal or subsurface. An attempt has been made to explore the methodologies based on temperature signal for possible detection of defects in friction stir welding process using different tool profiles. The presence of defect is affected by temperature diffusion and is better reflected by the change of temperature over time. Temperature signals are acquired using thermocouples inserted in specific locations on advancing and retreating side of the welds. The rate of change of temperature and wavelet-analysis-based indicator computed from temperature signal against each experiment reveal appreciable difference for defective and defect-free welds. Threshold values are computed that clearly set a boundary for classifying the defective welds from defect-free welds. The proposed approaches can effectively reduce the post-processing steps essential for other non-destructive evaluation of the welds and can work as a first level of safeguard in the identification of defects during friction stir welding process. The methodologies can be extended towards monitoring of the process in industrial applications.


Defect detection temperature profile temperature gradient wavelet analysis monitoring 



The authors gratefully acknowledge the financial support provided by SERB (Science and Engineering Research Board), India (Grant No. SERB/F/2767/2012-13), to carry out this research work.


  1. 1.
    Kim Y G, Fujii H, Tsumura T, et al 2006 Three defect types in friction stir welding of aluminium die casting alloy. Mater. Sci. Eng. A 415: 250–254CrossRefGoogle Scholar
  2. 2.
    Boldsaikhan E, Corwin E M, Logar A M and Arbegast W J 2011 The use of neural network and discrete Fourier transform for real-time evaluation of friction stir welding. Appl. Soft Comput. 11: 4839–4846CrossRefGoogle Scholar
  3. 3.
    Saravanan T, Lahiri B B, Arunmuthu K, et al 2014 Non-destructive evaluation of friction stir welded joints by X-ray radiography and infrared thermography. Proc. Eng. 86: 469–475CrossRefGoogle Scholar
  4. 4.
    Chen H B, Yan K and Lin T 2006 The investigation of typical welding defects for 5456 aluminium alloy friction stir welds. Mater. Sci. Eng. A 433: 64–69CrossRefGoogle Scholar
  5. 5.
    Rosado L S, Santos T G, Piedade M, et al 2010 Advanced technique for non-destructive testing of friction stir welding of metals. Measurement 43: 1021–1030CrossRefGoogle Scholar
  6. 6.
    Iwaki S, Okada T, Eguchi N, Tanaka S, Namba K and Oiwa N 2006 Imperfections in friction stir welded zones and their precision non-destructive testing. Studies on characteristics of friction stir welded joints in structural thin aluminium alloys. Weld. Int. 20: 197–205CrossRefGoogle Scholar
  7. 7.
    Cosmi F, Cristofori A and Mancini L 2005 Preliminary investigation by synchrotron radiation of cracks and defects in AA FSW samples. In: Proceedings of the 11th International Conference on Fracture, Paper No. 5450, Turin, ItalyGoogle Scholar
  8. 8.
    Moles M, Lamarre A and Cancre F 2002 Utilization of state-of-the-art phased array inspection technology for the evaluation of friction stir welds. In: Proceedings of NDE 2002: Predict, Assure, Improve, Chennai, IndiaGoogle Scholar
  9. 9.
    Moles M, Lamarre A and Dupuis O 2004 Complete inspection of friction stir welds in aluminium using ultrasonic and eddy currents arrays. In: Proceedings of 16th WCNDT 2004 – World Conference on NDT, Montreal Canada, Paper No 84Google Scholar
  10. 10.
    Wu C S, Wang L, Ren W J and Zhang X Y 2014 Plasma arc welding: process, sensing, control and modeling. J. Manuf. Process. 16(1): 74–85CrossRefGoogle Scholar
  11. 11.
    Huang X and Chen S 2006 SVM-based fuzzy modeling for the arc welding process. Mater. Sci. Eng. A 427(1–2): 181–187Google Scholar
  12. 12.
    He K, Li Q and Chen J 2013 An arc stability evaluation approach for SW AC SAW based on Lyapunov exponent of welding current. Measurement 46(1): 272–278CrossRefGoogle Scholar
  13. 13.
    Zhang Z, Chen H, Xu Y, Zhong J, Lv N and Chen S 2015 Multisensor-based real-time quality monitoring by means of feature extraction, selection and modelling for Al alloy in arc welding. Mech. Syst. Signal Process. 60–61: 151–165CrossRefGoogle Scholar
  14. 14.
    Cavaliere P, Campanile G, Panella F, et al 2006 Effect of welding parameters on mechanical and microstructural properties of AA6056 joints produced by friction stir welding. J. Mater. Process. Technol. 180: 263–270Google Scholar
  15. 15.
    Arora K S, Pandey S, Schaper M and Kumar R 2010 Effect of process parameters on friction stir welding of aluminum alloy 2219-T87. Int. J. Adv. Manuf. Technol. 50: 941–952CrossRefGoogle Scholar
  16. 16.
    Yang Y, Kalya P and Landers R G 2008 Automatic gap detection in friction stir butt welding operations. Int. J. Mach. Tool. Manuf. 48: 1161–1169CrossRefGoogle Scholar
  17. 17.
    Chen C, Kovacevic R and Jandgric D 2003 Wavelet transform analysis of acoustic emission in monitoring friction stir welding of 6061 aluminium. Int. J. Mach. Tool. Manuf. 43: 1383–1390CrossRefGoogle Scholar
  18. 18.
    Soundararajan V, Atharifar H and Kovacevic R 2006 Monitoring and processing the acoustic emission signals from the friction stir welding process. Proc. IMechE Part B J. Eng. Manuf. 220: 1673–1685CrossRefGoogle Scholar
  19. 19.
    Mehta M, Chatterjee K and De A 2013 Monitoring torque and traverse force in friction stir welding from input electrical signatures of driving motors. Sci. Technol. Weld. Join. 18: 191–197CrossRefGoogle Scholar
  20. 20.
    Das B, Pal S and Bag S 2014 Monitoring of friction stir welding process through signals acquired during the welding. In: Proceedings of the 5th International and 26th All India Manufacturing, Design and Research Conference (AIMTDR2014), December 12–14, 2014, Assam, IndiaGoogle Scholar
  21. 21.
    Bhat N N, Kumari K and Dutta S 2015 Friction stir weld classification by applying wavelet analysis and support vector machine on weld surface images. J. Manuf. Process. 20(1): 274–281CrossRefGoogle Scholar
  22. 22.
    Das B, Pal S and Bag S 2016 Defect detection in friction stir welding process through characterization of signals by fractal dimension. Manuf. Lett. 7: 6–10CrossRefGoogle Scholar
  23. 23.
    Shrivastava A, Dingler C, Zinn M and Pfefferkorn F E 2015 Physics-based interpretation of tool–workpiece interface temperature signals for detection of defect formation during friction stir welding. Manuf. Lett. 5: 7–11CrossRefGoogle Scholar
  24. 24.
    Khandkar M Z H, Khan J A and Reynolds A P 2003 Prediction of temperature distribution and thermal history during friction stir welding: input torque based model. Sci. Technol. Weld. Join. 8(3): 165–174CrossRefGoogle Scholar
  25. 25.
    Fuller M D, Swaminathan S, Zhilyaev A P and McNelley T R 2007 Microstructural transformations and mechanical properties of cast NiAl bronze: effects of fusion welding and friction stir processing. Mater. Sci. Eng. A 463: 128–137CrossRefGoogle Scholar
  26. 26.
    Shirazi H, Kheirandish S and Safarkhanian M A 2015 Effect of process parameters on the macrostructure and defect formation in friction stir lap welding of AA5456 aluminium alloy. Measurement 76: 62–69CrossRefGoogle Scholar
  27. 27.
    Chen Z W, Pasang T and Qi Y 2008 Shear flow and formation of nugget zone during friction stir welding of aluminium alloy 5083-O. Mater. Sci. Eng. A 474: 312–316CrossRefGoogle Scholar
  28. 28.
    Saeid T, Abdollah-zadeh A, Assadi H, et al 2008 Effect of friction stir welding speed on microstructure and mechanical properties of a duplex stainless steel. Mater. Sci. Eng. A 496: 262–268CrossRefGoogle Scholar
  29. 29.
    Morisada Y, Imaizumi T and Fujii H 2015 Clarification of material flow and defect formation during friction stir welding. Sci. Technol. Weld. Join. 20: 130–137CrossRefGoogle Scholar
  30. 30.
    Guasp M R, Daviu A A, Sanchez M P, Panadero R P and Cruz J P 2008 A general approach for transient detection of slip-dependent fault components based on the discrete wavelet transform. IEEE Trans. Ind. Electron. 55: 4167–4180CrossRefGoogle Scholar
  31. 31.
    Pal S, Pal S K and Samantaray A K 2008 Neurowavelet packet analysis based on current signature for weld joint strength prediction in pulsed metal inert gas welding process. Sci. Technol. Weld. Join. 13: 638–645CrossRefGoogle Scholar
  32. 32.
    Mishra R S and Ma Z Y 2005 Friction stir welding and processing. Mater. Sci. Eng. R 50: 1–78CrossRefGoogle Scholar

Copyright information

© Indian Academy of Sciences 2019

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringIndian Institute of Technology GuwahatiGuwahatiIndia

Personalised recommendations