Skip to main content
Log in

Quality evaluation in resistance spot welding by analysing the weld fingerprint on metal bands by computer vision

  • Original Article
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

This paper introduces a novel method for the quality evaluation of resistance spot welds. The evaluation is based on computer vision methods, which allow non-destructive on-line real-time processing. The input of the system is the image of a weld imprint on a metal band which covers the electrodes against wear and soiling. The shape and size of the structures within the imprint correlate with the nugget area and, therefore, allow an accurate estimation of the quality of the spot weld. The system segments the electrode imprint and computes the nugget area from the minimum and maximum axis of a fitted ellipse. This method does not need training samples to perform reliable quality estimation. Additionally, the used algorithms are easy to implement and efficient, which guarantees real-time ability. Since there is only a single low-cost camera needed, the hardware can be placed directly on the gun arm, which makes a fast evaluation possible.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Fitzgibbon A, Pilu M, Fisher R (1999) Direct least square fitting of ellipses. IEEE Trans Pattern Anal Mach Intell 21(5):476–480

    Article  Google Scholar 

  2. Sonka M, Hlavac V, Boyle R (1998) Image processing: analysis and machine vision, 2nd edn. International Thomson Publishing, London

    Google Scholar 

  3. Tsai CL, Jammal OA Papritan JC, Dickinson DW (1992) Modeling of resistance spot weld nugget growth. Weld J 71(2):47–54

    Google Scholar 

  4. Taylor JL, Xie P (1987) A new approach to the displacement monitor in resistance spot welding of mild steel sheet. Metal Constr 19(2):72–75

    Google Scholar 

  5. Howe P (1994) Spot weld spacing effect on weld button size. In: Proceedings of the AWS Sheet Metal Welding Conference VI, Detroit, Michigan, October 1994, paper C3

  6. Dickinson DW, Franklin JE, Stanya A (1980) Characterization of spot welding behavior by dynamic electrical parameter monitoring. Weld J 59(6):170–176

    Google Scholar 

  7. Bhattacharya S, Andrews DR, Green LW (1975) In-process quality control of spot weld. Metal Constr, pp 227–229

  8. Lee H-T, Wang M, Maev R, Maev E (2003) A study on using scanning acoustic microscopy and neural network techniques to evaluate the quality of resistance spot welding. Int J Adv Manuf Technol 22(9–10):727–732

    Article  Google Scholar 

  9. Cho YO, Rhee SE (2004) Quality estimation of resistance spot welding by using pattern recognition with neural networks. IEEE Trans Instrum Meas 53(2):330–334

    Article  Google Scholar 

  10. Cho YO, Rhee SE (2002) Primary circuit dynamic resistance monitoring and its application to the quality estimation during resistance spot welding. Weld J 81(6):104–111

    Google Scholar 

  11. Lin ZH, Zhang YA, Chen GU, Li YO (2004) Study on real-time measurement of nugget diameter for resistance spot welding using a neuro-fuzzy algorithm. In: Proceedings of the IEEE Instrumentation and Measurement Technology Conference (IMTC 2004), Como, Italy, May 2004, pp 2230–2233

  12. Lee SA, Choo YO (2001) A quality assurance technique for resistance spot welding using a neuro-fuzzy algorithm. J Manuf Syst 20(5):320–328

    Article  Google Scholar 

  13. Dilthey UL, Dickersbach JO (1999) Application of neural networks for quality evaluation for resistance spot welds. ISIJ Int 39(10):1061–1066

    Google Scholar 

  14. Aravinthan A, Sivayoganathan K, Al-Dabass D, Balendran V (2001) A neural network system for spot weld strength prediction. In: Proceedings of the United Kingdom Simulation Society Conference (UKSim 2001), Cambridge, England, March 2001, pp 156–160

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Johannes Ruisz.

Additional information

This work was carried out by the K-plus Competence Centre, Advanced Computer Vision GmbH, and was funded by the K-plus program.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ruisz, J., Biber, J. & Loipetsberger, M. Quality evaluation in resistance spot welding by analysing the weld fingerprint on metal bands by computer vision. Int J Adv Manuf Technol 33, 952–960 (2007). https://doi.org/10.1007/s00170-006-0522-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-006-0522-6

Keywords

Navigation