Abstract
In this study, a stable real-time monitoring system was established to monitor electrical and mechanical signals during resistance spot welding process. The sudden decrease of electrode voltage signal, the fluctuation of dynamic electrode force and the obvious decline of dynamic resistance could be used for recognizing expulsion phenomena during resistance spot welding which would reduce welding quality and should be avoided as much as possible. In order to research the welding quality estimation methods, four estimation models were built based on regression analysis and back-propagation neural network. The results showed that the estimation accuracy of back-propagation neural network was higher than the model of regression analysis, and the characteristic values of dynamic signals during resistance spot welding process could improve the estimation accuracy significantly.
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