Multiscale Modeling of Surface Modification and Multifunctional Coating Using Atmospheric Pressure Plasma

Multi-element conversion coatings are being actively investigated as an alternative to chromate-based conversion coatings, which cause long-term environmental hazards. Atmospheric Pressure Plasma (APP) can be used for the deposition of such coatings as a new, nonchemical, stand-alone technique for effluent free surface modification and coating process. The case of ZrO2-SiO2 composite coatings for aluminum alloys was discussed in this dissertation. Often process variations can result in microstructural variations that are difficult to correlate with measured corrosion protection and mechanical performance. Multiscale/multiphysics models were developed in conjunction with artificial intelligence techniques to correlate processing, microstructure, properties, and performance of APP-based fabricated coatings. The accomplished objectives of this dissertation were to: 1) establish a fully coupled atmospheric plasma model for the cleaning of organic compound residues (contaminants) to determine the removal rate of contaminant species during surface cleaning operation, 2) establish a multiphysics link between the processing parameters, plasma chemistry, surface – plasma reactions, and growth of functional layers to understand the growth of solid oxide films (e.g., SiO2 and ZrO2) on aluminum alloys, 3) apply machine learning techniques to analyze the effects of different geometrical and operational parameters, including microwave power, oxygen radical and hydroxyl concentrations, torch-substrate distance, tilt angle, gas flow rates, and plasma temperature, on the hydrocarbon degradation rates and the growth of oxide layer on the substrate during the cleaning and coating deposition processes, respectively, 4) establish a multiscale modeling framework that integrated deep learning techniques with microstructural-based finite element method to predict effective elastic properties, mechanical behavior and failure of the ZrO2-SiO2-Al 6061composites, and 5) establish a multiscale model using density functional theory (DFT) and multiphysics modules to predict the growth rate of galvanic pitting and to understand the localized corrosion dynamics using full interfacial electro-chemical kinetics.