Machine learning applied to high-entropy alloy coatings process parameters and composition optimization – A case study

Recent research has indicated that Al0.1-0.5CoCrCuFeNi and MnCoCrCuFeNi high entropy alloys (HEAs) exhibit superior mechanical and thermal properties under extreme conditions. This chapter provides an account of the wear and surface characteristics of cold-sprayed HEA coatings at various temperatures. The inputs of surface roughness and volume variation are analyzed by analysis of variance (ANOVA), employing formulas optimized by genetic algorithms. Gaussian process regression, support vector regression (SVR), and artificial neural networks are machine learning (ML) methods that predict surface roughness and volume variation with high accuracy. For surface roughness, SVR achieves a coefficient of determination of 0.97, which is lower than that determined from the other models. Furthermore, all three models achieve a coefficient of determination of 0.99 for volume variation. The results indicate the ability of ML to generalize effectively across datasets, capturing nonlinear patterns with precision. These findings emphasize the potential of HEAs for high-wear applications and the reliability of predictive modeling.