Slurry erosion resistance, morphology, and machine learning modeling of plasma-sprayed Si₃N₄+TiC+VC and CrNi based ceramic coatings

This study presents a comprehensive analysis of the morphology, mechanical properties, erosion resistance, and machine learning modeling of Si₃N₄+TiC + VC and CrNi based ceramic coatings deposited via plasma spraying. The cross-sectional morphology analysis revealed good mechanical interlocking between the coatings and the substrate, with pores predominantly observed in the Si₃N₄+TiC + VC coating. The coating thicknesses were measured as follows: 260 μm for Si₃N₄+TiC + VC, 243 μm for Si₃N₄+TiC + VC blended with CrNi, and 232 μm for pure CrNi coating. Mechanical characterization showed that microhardness values ranged from 1933 HV for Si₃N₄+TiC + VC coating to 499 HV for pure CrNi coatings. Slurry erosion tests demonstrated that pure Si₃N₄+TiC + VC coating exhibited superior erosion resistance at 90° impingement angle compared to other coatings. Additionally, a rigorous evaluation of over 20 machine learning regression models revealed that Gaussian Process Regressors and Artificial Neural Network (ANN) with a layer size of 20, displayed satisfactory performance in modeling mass loss prediction. The erosion mechanisms observed in the coatings provided valuable insights into their response to slurry erosion, highlighting the influence of coating composition and impingement angles. The findings underscore the potential of Si₃N₄+TiC + VC ceramic coatings to enhance the erosion resistance of stainless steel, offering promising applications in various industries.