Hybrid fuzzy-grey-Taguchi based multi weld quality optimization of Al/Cu dissimilar friction stir welded joints

Nowadays aluminum alloys substitute copper in various applications for weight reduction and cost savings. This paper presents fuzzy-grey Taguchi technique for optimization of friction stir welding condition with seven weld quality attributes of dissimilar Al/Cu joints with the minimum number of experiments for effective productivity and product quality. Taguchi's L₁₆ orthogonal array was used to conduct the experiments. Fuzzy inference system was adapted to convert the multi quality characteristics into an equivalent single quality parameter which was optimized by Taguchi approach. Four parameters namely, rotational speed of the tool, welding speed, plunging depth and tool pin offset were varied in four levels for investigating the effects on the process output like tensile strength, compressive strength, percentage of elongation, bending angle, weld bead thickness and average hardness at the nugget zone. The hardness profile is consistent with the variation of the structure within the nugget zone (NZ). Confirmation experiment was conducted using predicted optimum parameter setting and it showed that the proposed approach could efficiently optimize weld quality parameters. The microstructural analyses were also performed for all the zones of the joints at both Al and Cu sides. It revealed the finer grain size at the NZ compared to the base material due to dynamic recrystallization.