Advertisement

Welding in the World

, Volume 62, Issue 4, pp 729–736 | Cite as

Wavelet analysis-based expulsion identification in electrode force sensing of resistance spot welding

  • Na Wu
  • Shujun Chen
  • Jun Xiao
Research Paper
  • 103 Downloads

Abstract

Resistance spot welding is one of the most widely used processes in metal fabrication, but the expulsion affects the spot quality. To develop a precise and reliable weld quality assessment method for resistance spot welding, the welding force signal was measured and a novel wavelet transform based on multi indexes is proposed. The impulse and resultant damping vibration signal, which is the most obvious feature of expulsion, was extracted from the welding force waveform by 7-level wavelet transform with Daubechies5 wavelet. The detail signal in level 6 was chosen as the target signal, as it covers the dominant frequency of expulsion. To obtain the local characteristics of the expulsion, a multi indexes were calculated which includes the peak-to-peak value, kurtosis index, and pulse index. The peak-to-peak measures the range of a signal value, and kurtosis index and pulse index are sensitive to the impulse signal. According to experimental analysis, the peak-to-peak value of the expulsion signal is significantly higher than that of other conditions, while other two indexes are not that obvious but with same trend. The experimental data results show that the multi expulsion indexes are effective indicators in evaluating expulsion in resistance spot welding.

Keywords

Quality assessment Resistance spot welding Wavelet transform Expulsion 

Notes

Funding information

This research is supported financially by the National Natural Science Foundation of China (Grant No. 51775007) and the China Scholarship Council (CSC).

References

  1. 1.
    Mironov LG, Afanasev LK, Zaitsev VA (1979) Control of the quality of welds directly during resistance spot welding. Weld Prod 26:40–43Google Scholar
  2. 2.
    Yuan SX, Luo Z, BU XZ, Song KL, Ao SS (2008) Study of acoustic signal in the process of resistance spot welding based on array sensor system. China Weld 17:58–61Google Scholar
  3. 3.
    Liu J, Xu G, Gu X, Zhou G (2015) Ultrasonic test of resistance spot welds based on wavelet package analysis. Ultrasonics 56:557–565CrossRefGoogle Scholar
  4. 4.
    Podržaj P, Polajnar I, Diaci J, Kariž Z (2005) Estimating the strength of resistance spot welds based on sonic emission. Sci Technol Weld Join 10:399–405CrossRefGoogle Scholar
  5. 5.
    Mei DS, Li DQ, Zhang ZD (2007) On-line monitoring method for electrode invalidation during spot welding of zinc coated steel. Mater Sci Eng A 499:279–281CrossRefGoogle Scholar
  6. 6.
    Li RX (2012) Quality monitoring of resistance spot welding based on process parameters. Energy Procedia 14:925–930CrossRefGoogle Scholar
  7. 7.
    Livshits AG (1997) Universal quality assurance method for resistance spot welding based on dynamic resistance. Weld J 76:383s–390sGoogle Scholar
  8. 8.
    Farson DF, Chen JZ, Ely K, French T (2004) Monitoring resistance spot nugget size by electrode displacement. J Manuf Sci Eng 126:391–394CrossRefGoogle Scholar
  9. 9.
    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 78:841–851CrossRefGoogle Scholar
  10. 10.
    Min J (2003) Real time monitoring weld quality of resistance spot welding for the fabrication of sheet metal assemblies. J Mater Process Technol 132:102–113CrossRefGoogle Scholar
  11. 11.
    Kaya Y, Kahraman N (2012) The effects of electrode force, welding current and welding time on the resistance spot weldability of pure titanium. Int J Adv Manuf Technol 60:127–134CrossRefGoogle Scholar
  12. 12.
    Luo Y, Wan R, Xie XJ, Zhu Y (2016) Expulsion analysis of resistance spot welding on zinc-coated steel by detection of structure-borne acoustic emission signals. Int J Adv Manuf Technol 84:1–8CrossRefGoogle Scholar
  13. 13.
    Podržaj P, Simončič S (2013) Resistance spot welding control based on the temperature measurement. Sci Technol Weld Join 18:551–557CrossRefGoogle Scholar
  14. 14.
    Doubov AA (2002) Express method of quality control of a spot resistance welding with usage of metal magnetic memory. Weld World Le Soudage Dans Le Monde 46:317–320Google Scholar
  15. 15.
    Podržaj P, Polajnar I, Diaci J, Kariž Z (2004) Expulsion detection system for resistance spot welding based on a neural network. Meas Sci Technol 15:592–598CrossRefGoogle Scholar
  16. 16.
    Podržaj, Primož, and Samo Simončič (2011) Resistance spot welding control based on fuzzy logic. Int J Adv Manuf Technol, 52: 959–967Google Scholar
  17. 17.
    Chen S, Sun T, Jiang X, Qi J, Zeng R (2016) Online monitoring and evaluation of the weld quality of resistance spot welded titanium alloy. J Manuf Process 23:183–191CrossRefGoogle Scholar
  18. 18.
    Zhang ZD, Li DQ, Yin XH (2001) Study on spot welding quality monitoring models by linear regression theory. Trans China Weld Inst 22:31–35Google Scholar
  19. 19.
    Zhang ZD, Li DQ, Tang DJ, Li XJ (2004) Measures for decreasing errors of ANN models of spot welding quality monitor. Trans China Weld Inst 25:113–116Google Scholar
  20. 20.
    Zhang PX, Zhang HJ, Chen JH, Ma YZ (2007) Quality monitoring of resistance spot welding based on electrode displacement characteristics analysis. Front Mech Eng 2:330–335CrossRefGoogle Scholar
  21. 21.
    Simončič S, Podržaj P (2012) Image-based electrode tip displacement in resistance spot welding. Meas Sci Technol 23:065401CrossRefGoogle Scholar
  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 19:468–475CrossRefGoogle Scholar
  23. 23.
    Matsuyama K. (1997). Nugget size sensing of spot weld based on neural network learning. Int Inst Weld, Doc. No.III- 1081-97 Google Scholar
  24. 24.
    Monari G et al. (1998) Prediction of spot welding diameter using neural networks. Int Inst Weld, Doc.III-1108-98Google Scholar
  25. 25.
    Luo Z, Shan P, Hu SS, Lian JR, Yi XL, Xue WF (2003) Application of the wavelet packet and its energy spectrum to identify nugget splash during the aluminum alloys spot welding. China Weld 12:98–102Google Scholar
  26. 26.
    Resnikoff HL, Wells RO (1998) The Mallat algorithm. In: Wavelet Analysis. Springer, New York, NYGoogle Scholar

Copyright information

© International Institute of Welding 2018

Authors and Affiliations

  1. 1.College of Mechanical Engineering and Applied Electronics Technology, Ministry of Education Engineering Research Center of Advanced Manufacturing Technology for Automotive ComponentsBeijing University of TechnologyBeijingChina

Personalised recommendations