NSGA-II and Powell Optimized Inverse Design of MAO Coatings

Microarc oxidation (MAO), a key surface modification technique for in situ ceramic coating growth on valve metals, is hindered by stochastic plasma discharge dynamics that induce substantial property variability, impeding quality assurance, and industrial scalability. Existing empirical models fail to capture this nonlinearity due to small-sample constraints inherent to MAO process development. Here, we present a modular hybrid inverse design framework integrating (i) a composite deep variational autoencoder (ComDeep-VAE) for physics-aware data augmentation and (ii) Powell-optimized surrogate modeling with NSGA-II multiobjective optimization, establishing a generalizable platform extensible to mechanical integrity characterization and manufacturing system analytics. The ComDeep-VAE reconstructs high-dimensional process-parameter spaces while preserving experimentally validated statistical distributions, reducing the predictive mean absolute error by 60.8% (thickness) and 72.3% (porosity) relative to raw-data baselines using limited trials. The surrogate model achieves R ² = 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.