Application of Taguchi method and multiobjective particle swarm algorithm in parametric optimization of an industrial robotic arm welding system

This study presents an integrated parametric optimization framework combining the Taguchi method with a multi-objective particle swarm optimization (MOPSO) algorithm to enhance the welding performance of an industrial robotic gas metal arc welding (GMAW) system. The Taguchi method is first employed to identify the most influential parameters and their preliminary optimal levels for welding quality, based on orthogonal array experimentation. These results are then used as the initial population for the MOPSO algorithm, which performs a refined global search to obtain Pareto-optimal solutions balancing multiple, often conflicting, objectives. The optimization focuses on three critical quality indices: depth-to-width (D/W) ratio, heat input, and wire consumption. Experimental results using S400 low-carbon steel demonstrate that the proposed hybrid method outperforms the conventional Taguchi-only approach, achieving a 6.17% increase in D/W ratio, a 17.63% reduction in heat input, and a 15.68% decrease in wire consumption. The findings confirm that the Taguchi-MOPSO hybrid approach effectively improves weld quality, reduces energy and material usage, and enhances overall process efficiency in robotic welding applications.