Application Of The PSO Algorithm For Multi-Objective Optimization Of The TIG Welding Process For AA6061 Aluminum Alloy

Tungsten Inert Gas (TIG) welding is widely used for aluminum alloys due to its ability to produce high-quality joints with a narrow heat-affected zone, where weld quality is strongly influenced by parameters such as current, voltage, and travel speed. This study employs Particle Swarm Optimization (PSO) to simultaneously optimize tensile strength, hardness, and penetration depth in TIG welding of AA6061 aluminum alloy. A nonlinear predictive model developed from experimental data demonstrated high accuracy (R² > 0.95, RMSE < 3%), allowing reliable replacement of physical trials during optimization. PSO exhibited rapid convergence and generated a clear Pareto front illustrating the inherent trade-offs among performance objectives. The optimized solutions provide practical guidance for selecting suitable welding parameters under different quality priorities. Overall, the results confirm the effectiveness of PSO as a robust approach for multi-objective optimization in TIG welding processes.