Optimization of Process Parameters for Additive Manufacturing of Ti-6Al-4V Alloy Using Machine Learning and Multi-Objective Genetic Algorithm

• Machine learning models (RF, SVR, ANN) accurately predict density, surface roughness, and tensile strength of LPBF Ti-6Al-4V with R² > 0.95. • Multi-objective genetic algorithm identifies Pareto-optimal process parameters balancing multiple quality objectives. • Optimized parameters yield a 15% increase in tensile strength and a 30% reduction in surface roughness compared to baseline. • The framework reduces experimental trials by up to 70%, accelerating process development in additive manufacturing.