Monitoring of Resistance Spot Welding Process

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Resistance spot welding (RSW) is one of the common assembling process of thin metals. Due to the inaccessibility of nugget in inspection process, usually destructive tests are used to assess weld quality which is time consuming and costly. Finding a practical solution for monitoring of RSW would reduce necessity of these tests for quality control. In this study, a new designed algorithm is used for processing of a video recorded by a high speed camera from resistance spot welding process, to determine the displacement of the electrode (DE), which can be easily applied to any similar process. It was found that the diagram of DE can provide appropriate information about the ongoing events during the resistance spot welding process and gives a suitable tool for monitoring and controlling RSW. The effect of environmental conditions on the DE diagram was also investigated. Impact of expanded heat, melting and expulsion on displacement diagram during the process was discussed. The results showed that current and time have similar trend in DE, higher pressure will result in diagram’s compaction. It was also concluded that shunt and contamination have considerable effects on displacement diagram and strengths of weld. Using holders as external constraint can improve weld strength for about 12%. An artificial neural network (ANN)-based program was developed to introduce a relation between displacement diagram and weld quality. In most cases, the predicted values are closed to the experiments. 36 different input modes for welding were investigated in this regard. It is shown that by applying the developed code on DE graph the weld quality can be predicted without need to any destructive test.

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  1. 1.

    Moshayedi H, Sattari-Far I (2012) Numerical and experimental study of nugget size growth in resistance spot welding of austenitic stainless steels. J Mater Process Technol 212:347–354

  2. 2.

    Moshayedi H, Sattari-Far I (2014) Resistance spot welding and the effects of welding time and current on residual stresses. J Mater Process Technol 214:2545–2552

  3. 3.

    Ma Y, Wu P, Xuan C, Zhang Y, Su H (2013) Review on techniques for on-line monitoring of resistance spot welding process. Adv Mater Sci Eng 2013

  4. 4.

    Zhou K, Cai L (2013) Online nugget diameter control system for resistance spot welding. Int J Adv Manuf Technol 68:2571–2588

  5. 5.

    Brezovnik R, Cernelic J, Petrun M, Dolinar D, Ritonja J (2017) Impact of the switching frequency on the welding current of a spot-welding system. IEEE Trans Ind Electron 64:9291–9301

  6. 6.

    Kaščák L, Spišák E (2016) Evaluation of the influence of the welding current on the surface quality of spot welds. Int J Eng Sci (IJES) 5:32–37

  7. 7.

    Gomes GF, Viéville P, Durrenberger L (2017) Dynamic behavior investigation of spot welding machines and its influence on weld current range by modal analysis. J Braz Soc Mech Sci Eng 39:765–773

  8. 8.

    Wan X, Wang Y, Zhao D (2016) Quality evaluation in small-scale resistance spot welding by electrode voltage recognition. Sci Technol Weld Join 21:358–365

  9. 9.

    Wan X, Wang Y, Zhao D (2016) Quality monitoring based on dynamic resistance and principal component analysis in small scale resistance spot welding process. Int J Adv Manuf Technol 86:3443–3451

  10. 10.

    Fan Q, Xu G, Wang T (2018) The influence of electrode tip radius on dynamic resistance in spot welding. Int J Adv Manuf Technol 95:3899–3904

  11. 11.

    Xing B, Xiao Y, Qin QH, Cui H (2018) Quality assessment of resistance spot welding process based on dynamic resistance signal and random forest based. Int J Adv Manuf Technol 94:327–339

  12. 12.

    Xing B, Xiao Y, Qin QH (2018) Characteristics of shunting effect in resistance spot welding in mild steel based on DE. Measurement 115:233–242

  13. 13.

    Jou M (2003) Real time monitoring weld quality of resistance spot welding for the fabrication of sheet metal assemblies. J Mater Process Technol 132:102–113

  14. 14.

    Zhang H, Hou Y, Zhao J, Wang L, Xi T, Li Y (2017) Automatic welding quality classification for the spot welding based on the Hopfield associative memory neural network and Chernoff face description of the DE signal features. Mech Syst Signal Process 85:1035–1043

  15. 15.

    Kuščer L, Polajnar I, Diaci J (2011) A method for measuring displacement and deformation of electrodes during resistance spot welding. Meas Sci Technol 22:067002

  16. 16.

    Park Y, Cho H (2004) Quality evaluation by classification of electrode force patterns in the resistance spot welding process using neural networks. Proc Inst Mech Eng B J Eng Manuf 218:1513–1524

  17. 17.

    Sun H, Lai X, Zhang Y, Shen J (2007) Effect of variable electrode force on weld quality in resistance spot welding. Sci Technol Weld Join 12:718–724

  18. 18.

    Zhou K, Cai L (2014) Study on effect of electrode force on resistance spot welding process. J Appl Phys 116:084902

  19. 19.

    Charde N, Ahmad R, Abidin NIZ (2016) Interpreting the weld formations using acoustic emission for the carbon steels and stainless steels welds in servo-based resistance spot welding. Int J Adv Manuf Technol 86:1–8

  20. 20.

    Polajnar I, Grum J, Esmail EA (1999) Esmail: sources of acoustic emission in resistance spot welding. J Mech Eng

  21. 21.

    Simončič S, Podržaj P (2012) Image-based electrode tip displacement in resistance spot welding. Meas Sci Technol 23:065401

  22. 22.

    Simončič S, Podržaj P (2014) Resistance spot weld strength estimation based on electrode tip displacement/velocity curve obtained by image processing. Sci Technol Weld Join 19:468–475

  23. 23.

    Zhang H, Hou Y, Zhang J, Qi X, Wang F (2015) A new method for nondestructive quality evaluation of the resistance spot welding based on the radar chart method and the decision tree classifier. Int J Adv Manuf Technol 78:841–851

  24. 24.

    Podržaj P, Simončič S (2014) A machine vision-based DE measurement. Welding in the World 58:93–99

  25. 25.

    Podržaj P, Polajnar I, Diaci J, Kariž Z (2004) Expulsion detection system for resistance spot welding based on a neural network. Meas Sci Technol 15:592

  26. 26.

    Hoseini HT, Farahani M, Sohrabian M (2017) Process analysis of resistance spot welding on the Inconel alloy 625 using artificial neural networks. Int J Manuf Res 12:444–460

  27. 27.

    Boriwal L, Sarviya R, Mahapatra M (2018) Modeling the resistance spot welding of galvanized steel sheets using neuro-fuzzy method. In: International proceedings on advances in soft computing, intelligent systems and applications. Springer, pp 37–50

  28. 28.

    AWS D8.9 (2012) Recommended practices for test methods for evaluating the resistance spot welding behavior of automotive sheet steel materials. American Welding Society, Inc. In: AWS

  29. 29.

    Otsu N (1979) A threshold selection method from gray-level histograms. IEEE transactions on systems, man, and cybernetics 9:62–66

  30. 30.

    Lim JS (1990) Two-dimensional signal and image processing. Englewood Cliffs, NJ, Prentice Hall 1990 710 p

  31. 31.

    Gonzalez RC, Woods RE, Eddins SL (2009) Digital image processing using MATLAB. 2004. Publisher T Robbins. Printed in USA 11:531–534

  32. 32.

    Soille P (2013) Morphological image analysis: principles and applications. Springer Science & Business Media

  33. 33.

    Song Q, Zhang W, Bay N (2005) An experimental study determines the electrical contact resistance in resistance welding. Weld J 84:73s–76s

  34. 34.

    Karimi M, Haghshenas,N., and Masoumijalal,B. (2013) MULTY-crack detection. Tech J Eng Appl Sci, 18: 3484–3490

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Correspondence to H. Moshayedi.

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Haghshenas, N., Moshayedi, H. Monitoring of Resistance Spot Welding Process. Exp Tech 44, 99–112 (2020) doi:10.1007/s40799-019-00341-z

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  • Resistance spot welding
  • Monitoring
  • Displacement of electrode
  • Neural network based program
  • Weld quality