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.

Welding input parameters play a very significant role in determining the quality of a weld joint. The quality of the joint can be defined in terms of mechanical properties, distortion and weld-bead geometry. Generally, all welding processes are employed with the aim of obtaining a welded joint with the desired characteristics. The purpose of this study is to propose a method to decide near optimal settings for the welding process parameters in friction welding of (AISI 904L) super austenitic stainless steel by using non conventional techniques and genetic algorithm (GA). Grey relational analysis and the desirability approach were applied to optimize the input parameters by considering multiple output variables simultaneously. An optimization method based on genetic algorithm was then applied to resolve the mathematical model and to select the optimum welding parameters. The main objective of this work is to determine the friction welding process parameters to maximize the fatigue life and minimize the width of the partial deformation zone (left & right) and welding time. This study describes how to obtain near optimal welding conditions over a wide search space by conducting relatively a smaller number of experiments. The optimized values obtained through these evolutionary computational techniques were also compared with experimental results. ANOVA analysis was carried out to identify the significant factors affecting fatigue strength, welding time and partially deformed zone and to validate the optimized parameters.

Friction welding is a type of solid state welding which plays an important role in joining metal surfaces with the help of frictional heat accompanied with high force. Optimization of process parameters of friction welding is important for all types of materials. Optimization of input parameters of friction welding also plays a very significant role in determining the quality of a weld joint. Purpose of this study is to optimize the welding process parameters in friction welding of AISI 904L super austenitic stainless steel by using regression analysis and evolutionary algorithms. This study is to determine optimimum welding process parameters of friction welding with the help of genetic algorithm (GA) and simulated annealing (SA). Also, it explains how to obtain near optimum welding conditions in a wide range by conducting a relatively small number of experiments. Results of these evolutionary computational techniques were compared with experimental results. Finally, an optimization parameter is obtained for a maximum fatigue life and a minimum welding time.