Investigation of Dissimilar Resistance Spot Welding Process of AISI 304 and AISI 1060 Steels with TLBO-ANFIS and Sensitivity Analysis

In this work, the process of dissimilar resistance spot welding (RSW) for AISI 304 and AISI 1060 steel sheets is experimentally investigated. The effects of the main process parameters such as welding current, electrode force, welding cycle, and cooling cycle on the tensile-shear strength (TSS) of dissimilar RSW joints are studied. To this aim, using a central composite experimental design based on response surface methodology (RSM), the experimental tests were performed. Furthermore, from the test results, an adaptive neuro-fuzzy inference system (ANFIS) was developed to model and estimate the TSS. The optimal parameters of the ANFIS system were obtained using a teaching-learning-based optimization (TLBO) algorithm. In order to model the process behavior, the results of experiments were used for the training (70% of the data) and testing (30% of the data) of the adaptive inference system. The accuracy of the obtained model was investigated via different plots and statistical criteria including root mean square error, correlation coefficient, and mean absolute percentage error. The findings show that the ANFIS network successfully predicts the TSS. In addition, the network error in estimating the TSS in the training and test section is equal to 0.08% and 5.87%, respectively. After modeling with TLBO-ANFIS, the effect of each input parameter on TSS of the dissimilar joints is quantitatively measured using the Sobol sensitivity analysis method. The results show that increasing in welding current and welding cycle leads to an increase in the TSS of joints. It is concluded that TSS decreases with increases in the electrode force and cooling cycle.