Joining of dissimilar aluminum alloys are widely used in automobile, aerospace and shipbuilding industries. The defect-free joining of aluminum alloys using conventional technique is a challenging task for a welding engineer. Friction stir welding has been established as one of the most promising processes for defects-free joining of aluminum alloys. In this study, a hybrid approach of grey relational analysis with principal component analysis, is applied for multi-objective optimization of process parameters for friction stir welding of dissimilar AA5083/AA6063 aluminum alloys. Three responses namely tensile strength, average hardness at weld nugget zone and average grain size at weld nugget zone, and four process parameters with three levels have been selected for the study. Taguchi method based L27 orthogonal array design matrix is used for experiments. The optimal set of process parameters using hybrid approach was found as 900 r/min of tool rotational speed, 60 mm/min of welding speed, 18 mm of shoulder diameter and 5 mm of pin diameter. Improved performance of each response was obtained from the confirmation tests at optimum level of parameters.
Multi Objective Optimization of Process Parameters by Firefly Algorithm during the Friction Stir Welding of Metal Matrix Composites