Machine Learning-Driven Multi-Objective Optimization of Bead Geometry and Energy Efficiency in Laser-Arc Hybrid Additive Manufacturing.

Laser-arc hybrid additive manufacturing (LAHAM) combines the benefits of arc-based deposition and laser precision but involves complex, nonlinear process interactions that challenge the prediction and control of bead geometry and energy consumption. This study develops a machine learning (ML) framework to predict bead width, height, and Deposition volume per unit energy (DVUE) in LAHAM. Using experimental data, multiple regression models-including Support Vector Regression, Gaussian Process Regression, Neural Networks, and XGBoost-were trained and evaluated. Gaussian Process Regression (GPR) demonstrated superior performance in capturing nonlinear relationships and was further optimized using Bayesian Optimization and Particle Swarm Optimization. The optimized GPR models were integrated with the NSGA-II multi-objective optimization algorithm to simultaneously minimize geometric deviations and maximize DVUE. Results show that the proposed approach effectively identifies Pareto-optimal process parameters, achieving a balance between deposition accuracy and energy utilization rate, thereby providing a reliable and intelligent strategy for process optimization in hybrid additive manufacturing.