Integration of Artificial Neural Networks and Process Simulation for Optimizing Direct Current Atmospheric Plasma Spraying of Enhanced Zirconia Coatings

This study investigates the application of Direct Current Atmospheric Plasma Spraying (DC-APS) for depositing zirconium dioxide (ZrO2) coatings to enhance component performance and durability in advanced applications. ZrO2's excellent thermal and mechanical properties make it ideal for industries like aerospace and renewable energy. We focus on optimizing critical process parameters, such as plasma jet temperature and particle injection velocity, to improve coating characteristics. Using the Jets&Poudres (JP) simulation code, we analyze particle dynamics and heat transfer, revealing how process variations influence coating morphology and adhesion. Additionally, we employ artificial neural networks (ANNs) to model the deposition process, achieving high accuracy in predicting performance metrics. Our findings confirm that DC-APS is an effective method for producing high-quality ZrO2 coatings and underscore the importance of process optimization and AI integration for enhancing thermal spray applications.