Experimental modelling and genetic algorithm-based optimisation of friction stir welding process parameters for joining of dissimilar AA5083-O and AA6063-T6 aluminium alloys. (2018)

• Record Type: Journal Article
• Authors: Gupta, Saurabh Kumar; Pandey, K.N.; Kumar, Rajneesh
• Is Part Of: International journal of material & product technology. Volume 56:Number 3(2018)
• Page Start: 253
• Page End: 270
• Publication Date: 2018
• Subjects: friction stir welding -- FSW -- aluminium alloys -- genetic algorithm -- optimisation -- tensile strength -- grain size -- analysis of variance -- regression model; Manufacturing processes -- Periodicals; Materials -- Testing -- Periodicals; 620.11
• Languages: English
• ISSNs: 0268-1900

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.