Cause diagnosis of welding defects based on adaptive PSO-BP neural network with dynamic weighting

Considering the complex causes and various impact factors for welding defects, diagnosis methods based on artificial intelligence algorithms are regarded as one of the directions for the development of intelligentizing welding. In this study, an improved diagnosis method for welding defect based on PSO-BP neural network is proposed. Connection learning mechanism of neural network is used instead of the rule reasoning mechanism of traditional expert systems. It also makes adaptive adjustments to the PSO algorithm, introduced dynamic weight factors, and builds an adaptive PSO-BP neural network model. Compared with the traditional PSO-BP neural network model, the number of iterations required to train the improved PSO-BP neural network model reduced by 13.1%, the accuracy of diagnostic results increased from 93.3% to 96.7%, the precision increased from 91.3% to 98.3%, and the comprehensive performance index increased from 91.7% to 96.9%. The results show that the improved algorithm can significantly improve the efficiency and accuracy of welding defect diagnosis, and has good engineering application value.