Prediction of resistance spot weld quality of 780 MPa grade steel using adaptive resonance theory artificial neural networks

Abstract

In this study, the weld quality of 780 MPa grade dual phase (DP) steel with 1.0 mm thickness was predicted using adaptive resonance theory (ART) artificial neural networks. The welding voltage and current signals measured during resistance spot welding (RSW) were used as the input layer data, and the tensile shear strength, nugget size, and fracture shape of the weld were used as the output layer data. The learning was performed by the ART artificial neural networks using the input layer and output layer data, and the patterns of learning result were classified by the setting of vigilance parameter, ϱ. When the vigilance parameter is 0.8, the best-predicted results were obtained for the tensile shear strength, nugget size, and fracture shape of welds.