A quality assurance technique for resistance spot welding using a neuro-fuzzy algorithm

Resistance spot welding is widely used in the field of plate assembly; however, there is currently no satisfactory nondestructive quality evaluation for this type of welding, either in real time or on-line. Moreover, even though the rate of welding under conditions of expulsion has been high until now, there is still no established method of quality control against expulsion. Accordingly, this paper proposes a quality assurance technique for resistance spot welding using a neuro-fuzzy algorithm. Four parameters from an electrode separation signal, in the case of non-expulsion, and dynamic resistance patterns, in the case of expulsion, are selected as the fuzzy input parameters. These parameters are determined using a neuro-learning algorithm and then are used to construct a fuzzy inference system. When compared with the real strength for the total strength range, the fuzzy inference values of strength produced a specimen error within +/-4%, and the percentage of specimen errors within +/-1% was 88.8%. The tensile-shear strength limit for electrically coated zinc is 3,920 kN/mm(2). When evaluating whether the quality of the welding was good or bad according to this criterion, the probability of misjudgment that a good quality weld was a poor one was 0.43%, and the reverse was 2.59%. Finally, the proposed neuro-fuzzy inference system can infer the tensile-shear strength of resistance spot welding with a high efficiency in cases of both non-expulsion and expulsion. It is also anticipated that an on-line welding quality inspection system will be realized in the near future.