Self-organizing map based quality assessment for resistance spot welding with featured electrode displacement signals

Abstract

To classify the quality of the resistance spot welding process, a relationship between the welder electrode displacement curve characteristics and the weld shear force has been explored. Eleven statistical features of the displacement signals are extracted to represent the welding quality. Self-organizing map (SOM) neural networks have been employed to discover their quantitative relationship. In order to identify the influence of various displacement curve features, all of the available combinations have been used as inputs for SOM neural networks. Further we analyze the impact of each feature on the classification results, yielding the best quality-indicative combination of characteristics. There is no determinant relationship between the welding quality and the level of expulsion rate. The quality of welding is most impacted by the maximum electrode displacement, the span of welding process and the centroid of the electrode displacement curve. The experiments show that SOM is feasible to assess the welding quality and can render the visualized intuitive evaluation results.