Comparison of RSM with ANFIS in predicting tensile strength of dissimilar friction stir welded AA2024 -AA5083 aluminium alloys

Abstract

Friction stir welding (FSW) is a solid-state joining technique which has been employed in aerospace, marine, and automotive industries for joining aluminum, copper, titanium and other alloys. The FSW process parameters such as welding speed, tool rotational speed, pin profile, and axial force have a main role in determining the joint quality. A comparative study was achieved between the response surface method (RSM) and the adaptive neuro-fuzzy inference system (ANFIS) to improve the mechanical properties of dissimilar friction stir welded AA2024-AA5083 aluminium alloys in terms of the ultimate tensile strength (UTS). The effects of the welding parameters on the UTS were investigated using four-factor, three-level ANFIS model. The statistical results of the ANFIS model were compared with those of RSM. The results reveal that the developed ANFIS model is more powerful than the RSM model.