Artificial neural network simulation and particle swarm optimisation of friction welding parameters of 904L superaustenitic stainless steel

Abstract

Purpose -Friction welding (FW) is a solid state joining process. Super austenitic stainless steel is the preferable material for high corrosion resistance requirements. These steels are relatively cheaper than austenitic stainless steel and it is expensive than nickel base super alloys for such applications. The purpose of this paper is to deal with the optimization of the FW parameters of super austenitic stainless steel using artificial neural network (ANN) simulation and particle swarm optimization (PSO). Design/methodology/approach -The FW experiments were conducted based on Taguchi L-18 orthogonal array. In FW, rotational speed, friction pressure, upsetting pressure and burn-off length are the important parameters which determine the strength of the weld joints. The FW trials were carried out on a FW machine and the welding time was recorded for each welding trial from the computerized control unit of the welding machine. The left partially deformed zone (L.PDZ) and right partially deformed zone (R.PDZ) were identified from the macrostructure and their values are considered for the output variables. The tensile test was carried out, and the yield strength and tensile strength of the joints were determined and their fracture surfaces were analyzed through scanning electron microscope (SEM). Findings -The tensile test was carried out, and the yield strength and tensile strength of the joints were determined and their fracture surfaces were analyzed through SEM. An ANN was designed to predict the weld time, L.PDZ, R.PDZ and tensile strength of the joints accurately with respect to the corresponding input parameters. Finally, the FW parameters were optimized using PSO technique. Research limitations/implications -There is no limitations, difficult weld by fusion welding process material can easily weld by FW process. Originality/value -The research work described in the paper is original.

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