MIG/MAG Welding Parameter Optimization via Machine Learning: Multi-Objective Evolutionary Algorithms for Welding Speed, Current, and Voltage
A rigorous engineering analysis of applying Machine Learning and Multi-Objective Evolutionary Algorithms (NSGA-III, MOEA/D, Bayesian Optimization) to MIG/MAG welding process parameter optimization. Covers Gaussian Process surrogate modeling, Physics-Informed Neural Networks, Pareto front analysis, and closed-loop adaptive control with quantified industrial case studies.