A Comparative Study of Metaheuristic Optimization Approaches to Optimize Laser Welding Process Parameter with Pre-Set Weld Size Magnitude for AISI 416 and AISI 440 FSe Stainless Steels

Abstract

Optimization methods are used to accurately predict laser welding process parameters, helping to save material effort and time in determining the desired output variables. Based on a mathematical model, parameter selection is considered a binding optimization problem. The work involved is closely related to evolutionary optimization algorithms. This article proposes highly effective meta-heuristic methods: the GA (Genetic Algorithm), the JAYA optimization algorithm, and the MDE (Modified Differential Evolution) algorithm, which optimize the parameters of the laser welding to achieve the desired size for the weld. The performance of these three methods is evaluated on laser welds for AISI 416 and AISI 440 FSe stainless steels. With the same initial conditions, the MDE algorithm outperforms the other algorithms, GA and JAYA algorithms, regarding the best fitness value after ten runs. Thus, the MDE algorithm is used to optimize three parameters: Laser Power (LP), Welding Speed (WS), and Fiber Diameter (FD) to achieve two desired welding dimensions: the Width of the Weld Zone (WWZ) and the Penetration Depth of the Weld (PDW) for laser welds.