Friction stir welding (FSW) is a solid-state welding process, which has a significant role in solid-state welding processes for nonferrous alloys. Conventional arc welding processes for aluminum alloys such as metal inert gas (MIG) and tungsten inert gas (TIG) are replaced by FSW. The effect of FSW parameters such as rotational and traverse speeds, tool geometry, plunge depth, tilt angle, etc., on weld quality were considered in several optimization studies. Hence, the effect of fixture position is included in this study. Multi-criteria decision-making (MDCM) techniques such as grey relational analysis (GRA) were used to determine the optimal condition among experimental runs designed by response surface methodology (RSM). The Taguchi method was widely applied with MCDM techniques. Therefore, the experiments were conducted according to response surface methodology. Input parameters were (14, 16 and 18) mm for shoulder diameter (SD), (0.0, 0.2 and 0.4) mm for plunge depth (PD), and (30, 60 and 90) mm for fixture position (FP), which is the distance between fixture bolts used to fix the welded plate. The results obtained by GRA were similar to the ANOVA optimizer, and the optimum process conditions are shoulder diameter of 14 mm, plunge depth of 0.2 mm, and fixture position of 60 mm.

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Standard friction stir welding process parameters have a considerable impact on the quality of functional parts produced by underwater friction stir welding (UWFSW) with additive water. Hybrid statistical techniques may be used to optimize operating parameters in order to improve the aim function. The tensile strength (UTS)of parts fabricated with UWFSW by Al 6063 material in accordance with ASTMD638-14 tests is investigated in this study. In the construction of test specimens, three parameters were varied: rotational speed from 1000 to 1800 rpm, travel speed from 4 to 10 mm/s, and shoulder diameter from 10 to 20 mm. The response surface methodology (RSM) based central composite design (CCD) matrix for the parametric combination was constructed using a second-order polynomial fitting model. The maximum UTS of testing samples on the 201T universal testing machine (UTM) was 208.27 MPa. These process parameters are also optimized using hybrid optimization approaches such as response surface methodology- genetic algorithm (RSM-GA). RSM-GA had the highest precision of 98.99 percent, which resulted in optimal characteristics such as rotating speed 1800 rpm, travelling speed 4 mm/s, and shoulder diameter 15 mm, which resulted in a maximum tensile strength of 199.0212 MPa.

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