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

Authors: Saurabh Kumar Gupta; K.N. Pandey; Rajneesh Kumar

Addresses: Department of Mechanical Engineering, Raj Kumar Goel Institute of Technology, Ghaziabad 201003, U.P., India ' Mechanical Engineering Department, Motilal Nehru National Institute of Technology, Allahabad 211004, U.P., India ' Engineering Division, National Metallurgical Laboratory, Burmamines, Jamshedpur 831007, India

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.

Keywords: friction stir welding; FSW; aluminium alloys; genetic algorithm; optimisation; tensile strength; grain size; analysis of variance; regression model.

DOI: 10.1504/IJMPT.2018.090818

International Journal of Materials and Product Technology, 2018 Vol.56 No.3, pp.253 - 270

Accepted: 15 Dec 2016
Published online: 28 Mar 2018 *

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