Smart Nanocomposite Coatings via AI-Driven In-Situ Sputtering Optimization

Thesis Abstract

The advancement of protective and functional coatings in modern engineering hinges on the precise control of microstructure and properties at the nanoscale, which remains challenged by variability in in-situ sputtering processes and the trade-off between hardness, toughness, and corrosion resistance. This study addresses the problem of achieving adaptive, high-performance nanocomposite coatings by integrating artificial intelligence (AI) with real-time in-situ sputtering optimization to tailor deposition parameters for desired multifunctional properties. The aim is to develop an AI-driven framework that autonomously tunes magnetron sputtering conditions to produce nanocomposites with optimized mechanical, tribological, and corrosion-resistant attributes. Specific objectives are (i) to model the relationship between process variables (power, pressure, target-to-substrate distance, gas composition) and coating microstructure using supervised learning; (ii) to implement a real-time control loop that updates deposition parameters based on in-situ sensor feedback (spectroscopic ellipsometry, in-situ X-ray diffraction, and optical emission spectroscopy); (iii) to evaluate the performance of TiN-Fe3O4 and Al2O3-CNT nanocomposites under standardized wear, hardness, and corrosion tests; and (iv) to compare AI-driven results with conventional fixed-parameter sputtering approaches to quantify improvements in coating quality and process efficiency. The methodology employs a mixed-methods research design combining experimental synthesis and quantitative modeling. The population comprises magnetron-sputtered nanocomposite systems on hardened steel substrates, with a sample set of 60 coating runs across two material systems (TiN-Fe3O4 and Al2O3-CNT) and multiple thicknesses (1.5, 3.0, and 5.0 µm). Data collection instruments include in-situ sensors (spectroscopic ellipsometry for thickness and refractive index, in-situ XRD for phase evolution, optical emission spectroscopy for plasma diagnostics) and ex-situ characterizations (transmission electron microscopy for microstructure, nanoindentation for hardness and modulus, scratch testing for adhesion, ball-on-disk tribometry for wear resistance, and electrochemical impedance spectroscopy for corrosion performance). The analytical approach combines machine learning models with statistical validation multiple linear regression and random forest regression to map process-structure-property relationships, support vector regression for real-time parameter optimization, and ANOVA to assess significance across coating sets. Model validation employs cross-validation and a hold-out test cohort (20% of runs), with performance metrics including R-squared, RMSE, and mean absolute error. A theoretical framework grounded in process-structure-property theory and control theory underpins the AI-driven optimization, with relevant theories including Knowledge-Based Control and the Theory of Tribo-Corrosion Synergy guiding interpretation of results. The study hypothesizes that AI-driven in-situ adjustments will produce nanocomposite coatings with statistically superior hardness (? 25 GPa for TiN-Fe3O4, ? 18 GPa for Al2O3-CNT), reduced wear rates by at least 35% under standardized sliding conditions, and enhanced corrosion resistance (lower corrosion current density by an order of magnitude) relative to conventional sputtering. Expected findings include robust predictive models that accurately forecast coating density, phase composition, and tribological performance from real-time sensor data, and demonstrable improvements in surface roughness, adhesion, and defect suppression through closed-loop control. The research is expected to contribute to knowledge by (a) delivering a generalizable AI-augmented sputtering framework applicable to diverse nanocomposites, (b) elucidating real-time correlations between plasma diagnostics and emergent microstructures, and (c) providing a decision-support system that reduces process variability and accelerates deployment of high-performance coatings in aerospace, automotive, and energy applications. The main conclusion anticipates that AI-enabled in-situ optimization can achieve reproducible, high-quality nanocomposite coatings with tunable properties, surpassing traditional parameterization methods. Recommendations include scaling the framework to multi-robot sputtering environments, integrating additional sensing modalities (in-situ Raman spectroscopy, QCM-D), and exploring adaptive algorithms that incorporate degradation monitoring for predictive maintenance in industrial coating plants.