NSGA-II and Powell Optimized Inverse Design of MAO Coatings

= 0.948 (thickness) and 0.902 (porosity), exceeding conventional benchmarks. For inverse design, multiobjective optimization navigates trade-offs between deposition efficiency and barrier performance, achieving theoretical convergence (errors <2 μm, <0.5%) within 750-1000 evaluations for target specifications (8.0 μm, 21% porosity). Experimental validation across contrasting targets (7.5 μm/32% and 13.5 μm/17% porosity) confirms practical reliability (relative errors 6.0% and 5.6%) within intrinsic MAO variability (±5-15%), substantiating that the framework resolves stochastic discharge effects through variational inference-based uncertainty quantification. This work establishes a scalable computational platform for intelligent coating design, with explicit extension pathways to adhesion strength optimization, substrate topography engineering, dynamic process control, and Overall Equipment Effectiveness (OEE) modeling, thereby bridging data-driven optimization with mechanistic process understanding to accelerate industrial adoption.