An ANFIS based approach for predicting the weld strength of resistance spot welding in artificial intelligence development

Abstract

Artificial intelligence (AI) is a modern approach which has the ability to capture nonlinear relationships and interaction effects. Frequently, AI methods have been used by researchers to predict output responses of the Resistance spot welding (RSW) due to the complex- ity during the welding process and numerous interferential factors, especially the short-time property of the process. The present study is to investigate the weld strength of spot weld for high strength steel sheets of CR780 using the Adaptive neuro fuzzy inference system (ANFIS). These results were compared with those obtained by conventional Artificial neural network (ANN). The input parameters were extracted through the dynamic resistance signal which was obtained from the primary circuit of the welding machine. Both the ANN and ANFIS models were utilized for the formulation of mathematical model with an off-line dynamic resistance response of the RSW at a particular parameters setting. The performances of both models were compared in terms of correlation coefficient value (R), Root mean squared error (RMSE), and Mean absolute percentage error (MAPE). While both methods were capable of predicting the weld strength, it was found that ANFIS model could predict more precisely than ANN.