An integrated ANN and design of experiments technique to optimize the FSW input parameters of novel interlock lap weld

Abstract

Friction stir welding of lightweight aluminium alloys have advantage in automobile industry with its vast applications. This research work focuses on the influence of FSW process parameters on novel interlock lap weld of AA7075-T7-AA7475 tailor welded blank. Three levels of the parameters, including tool rotation speed (TRS), weld speed (WS) and plunge speed, were used to form L27 orthogonal array to optimize the input process conditions. Ultimate tensile strength and Vicker's micro hardness were measured to test the characteristics of the interlock welded samples. Scanning electron microscopy analyses have been carried out to study the surface morphologies and elemental components in the welded samples. Artificial neural network (ANN) has been used to predict the optimized process parameter associated with the novel interlock lap weld. The TRS and WS contributed significantly in improving the mechanical behaviour and microstructural characteristics of interlock lap welds. Visual inspection and surface morphology analysis showed uniform dispersal of aluminium alloy deposition throughout the interlock weld samples. The ultimate tensile strength and micro hardness prediction was carried out using ANN with 95% accuracy level. The predicted results of ANN were more accurate than the experimental results and regression model of fractional factorial design. The defined FSW interlock lap weld stands out as the substitute for typical FSW lap weld of aluminium alloys which fulfils the modern automotive industry demands in welding monocock frames.