Multi-objective optimization of activated cold metal transfer welded AA 6063-MgAZ31B dissimilar alloy joint using Taguchi and particle swarm algorithm

Welding is an essential manufacturing technique in the automobile, aerospace and shipbuilding industries. Manufacturing of ships and aircraft requires lightweight material. Aluminium and magnesium alloys are most widely used for ship and aircraft manufacturing. Welding of aluminium-magnesium dissimilar alloys is very difficult because of differences in physical and chemical properties. These industries face specific problems when joining dissimilar alloys butt joints of greater plate thickness. Cold metal transfer (CMT) welding has low heat input and can weld dissimilar aluminium and magnesium alloys. The main challenge of CMT welding is low penetration. Deeper penetration is obtained using a thin layer of activated flux on the surface of the weld region. This research uses activated CMT (A-CMT) welding to join AA 6063 and MgAZ31B. Welding current, welding speed, and activated flux like SiO₂ and TiO₂ were selected as process parameters. In this study, process parameter optimization can be obtained using the integrated Taguchi and Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to improve the depth of penetration (DOP). The regression model is developed for objective functions using experimental results, and the optimal set of process parameters is determined through the modified MOPSO algorithm. Modified MOPSO achieves the best possible result with a 50 Pareto optimal solution. The result of MOPSO shows that the maximum DOP obtained is 4.26 mm and the minimum weld width obtained is 5.7678 mm at a welding current of 139.9761 A and the welding speed of 6.0134 mm/s with SiO₂ activated flux. This study also used image processing approaches to detect weld defects such as porosity and surface cracks using digital images of weld beads. This approach shows that the weld defects mainly occur due to improper selection of process parameters.