A Comprehensive Approach for Resistance Spot Welding Quality Estimation Using Dynamic Resistance Based Model

Abstract:

On-line quality assessment becomes one of the most critical requirements for improving the efficiency of automatic resistance spot welding (RSW) processes. Accurate and efficient model to perform non-destructive quality estimation is an essential part of the assessment. Besides the usual welding parameters, various measured variables have been considered for quality estimation in RSW. Among these variables, dynamic resistance gives a relative clear picture of the welding nugget formation and presents a significant correlation withseveral RSW quality indicators. This paper presents a structuredand comprehensiveapproach developed to design an effective dynamic resistancebased model for on-line quality estimation in RSW. The proposed approach examines welding parameters and conditions known to have an influence on weld quality, and builds a quality estimation model step by step. The modeling procedure begins by examining, through a structured experimental design, the relationships between welding parameters, typical characteristics of the dynamic resistance curves and multiple welding quality indicators. Using these results and various statistical tools, different integrated quality estimation models combining an assortment of dynamic resistance attributes are developed and evaluated. The results demonstrate that the proposed approach can lead to a consistentmodel able to accurately and reliably provide an appropriate estimationof the weld quality under variable welding conditions.

• Periodical: Applied Mechanics and Materials (Volumes 249-250)
• Pages: 732-738
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.249-250.732
• Online since: December 2012
• Authors: A. El Ouafi, R. Belanger, M. Guillot
• Keywords: Artificial Neural Network (ANN), Design of Experiment (DOE), Dynamic Resistance, Resistance Spot Welding, Weld Quality Estimation

References

[1] Podržaj P., Polajnar I., Diaci J., Kariž Z., Overview of resistance spot welding control, Science and technology of welding and joining, vol. 13, no. 3 (2008 ) 215-224.

DOI: 10.1179/174329308x283893

[2] Zhang H., Senkara J., Resistance welding: fundamentals and applications, CRC Press, Taylor & Francis Group, (2006).

[3] Klimpel A., Investigation and quality control of resistance spot welding, Welding International, vol. 3 no. 12 (1989) 1040-1045.

DOI: 10.1080/09507118909449077

[4] Chien C.S., Kannatey-AsibuJr . E., Investigation of monitoring systems for resistance spot welding, Welding Journal, vol. 81, no. 9 (2002)195-199.

[5] Bhattacharya S., Andrews D.R., Significance of dynamic resistance curve in the theory and practice of spot welding, Welding and metal fabrication, Vol. 42, no. 8 (1974) 296-301.

[6] Dickinson D.W., Franklin J.E., Stanya A., Characterization of spot welding behaviour by dynamic electrical parameter monitoring, Welding Journal, vol. 59, no. 6 (1980) 170-176.

[7] Li W., Hu S.J., Ni J., On-line quality estimation in resistance spot welding, Journal of Manufacturing Science and Engineering, vol. 122, no. 3 (2000) 511-512.

DOI: 10.1115/1.1286814

[8] Cho Y., Kim Y, Rhee S., Development of a quality estimation model using multivariate analysis during resistance spot welding, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, vol. 215, no. 11 (2001).

DOI: 10.1243/0954405011519439

[9] Lee S.R., Y.J. Choo, Lee T.Y., Kim M.H., Choi S.K., A quality assurance technique for resistance spot welding using a neuro-fuzzy algorithm, Journal of Manufacturing Systems, vol. 20, no. 5 (2001) 320-328.

DOI: 10.1016/s0278-6125(01)80051-0

[10] Mason, R.L., Gunst, R.F., Hess, J.L., Statistical design and analysis of experiments - with applications to engineering and science, John Wiley & Sons publication, (2003).