Experimental modelling and genetic algorithm-based optimisation of friction stir welding process parameters for joining of dissimilar AA5083-O and AA6063-T6 aluminium alloys

Abstract

Friction stir welding (FSW) is a solid state joining process and one of the most promising technique for defect free joining of aluminium alloys. In this paper, second order regression modelling and genetic algorithm-based optimisation of FSW process parameters is presented for joining of dissimilar AA5083-O and AA6063-T6 aluminium alloys. For developing the regression model, experiments were performed as per L27 orthogonal array and models were developed with the help of MINITAB software. For genetic algorithm-based process parameter optimisation, regression models were considered as objective functions. The regression models have been found satisfactory for predicting the responses at 99% confidence level. The derived set of optimal process parameters were found as tool rotational speed of 900 rpm, welding speed of 60 mm/min, shoulder diameter of 18 mm and pin diameter of 5 mm for maximum tensile strength and minimum grain size.