Predictive Modeling of Porosity in Al₂O₃–TiO₂ Ceramic Coatings Produced by Plasma Spraying

Background: Plasma spraying is a proven technique for applying ceramic coatings to enhance the mechanical and chemical resistance of components exposed to abrasive and corrosive environments. However, controlling coating porosity remains a critical factor that directly affects the coating's performance and lifespan. Contribution: This study contributes to the field by developing a predictive model that quantifies the influence of key plasma spraying parameters on the porosity of Al₂O₃–TiO₂ coatings. The model enables process optimization and quality control for applications requiring high-performance surface protection. Method: An orthogonal experimental design (N27) was implemented to systematically vary three process parameters: spray distance (Lp), plasma current intensity (Ip), and powder feed rate (Gp). A total of 27 coating samples were produced and analyzed. Results: The resulting porosity ranged from 5.96% to 14.52% depending on parameter combinations. The developed second-order polynomial regression model demonstrated good predictive accuracy, with deviation between measured and predicted values ranging from −8.67% to +13.96%, and typically within acceptable engineering limits. Conclusion: The findings confirm that process parameters significantly affect coating porosity, and that the proposed model is a useful tool for optimizing plasma spray operations.