Modeling and prediction of weld shear strength in friction stir spot welding using design of experiments and neural network

Abstract

Friction Stir Spot Welding (FSSW) is a kind of the friction stir welding (FSW) process, creates a spot, lap-weld without bulk melting work materials. The tensile shear strength of the FSSW welded joints mainly depends on the pin height, tool rotation and welding time. In the present study, two of the techniques, namely factorial design and neural network (NN) were used for modeling and predicting the tensile shear strength of EN AW 5005 aluminum alloy. Tensile shear strength was taken as a response variable measured after welding pin height, tool rotation and welding speed were taken as input parameters. Relationships between tensile shear strength and welding parameters have been investigated. The level of importance of the FSSW parameters on the tensile shear strength was determined by using the analysis of variance method (ANOVA). The mathematical relation between the tensile shear strength and FSSW welding parameters were established by regression analysis method. This mathematical model may be used in estimating the tensile shear strength of FSSW joints without performing any experiments. Finally, predicted values of tensile shear strength by techniques, NN and regression analysis, were compared with the experimental results and their nearness with the experimental values assessed. Results show that, NN is a good alternative to empirical modeling based on full factorial design.