Quality assessment for resistance spot welding based on binary image of electrode displacement signal and probabilistic neural network

Abstract To improve the efficiency of acquiring monitored features and present a reliable quality assessment method for resistance spot welding, a novel method for converting the electrode displacement signal into binary image is proposed. The probabilistic neural network is adopted to provide a probabilistic viewpoint and a deterministic classification result of weld quality when the image characteristic of displacement signal is selected as monitored features. Test results of the classifier demonstrate that it is feasible and reliable to utilise binary image of the displacement signal to evaluate weld quality. The method reserves weld quality information as much as possible and it avoids complex algorithm for extracting and selecting monitored features. At the same time, when there are small samples, the classifier can identify good or bad weld rapidly and accurately even though the weld is from abnormal welding process, such as expulsion, current shunting and small edge distance condition.