Machine learning based prediction of Young's modulus of stainless steel coated with high entropy alloys

The High Entropy Alloy (HEA) coatings exhibit diverse properties contingent upon their composition and microstructure, addressing current industrial requirements. Machine Learning (ML) regression emerges as a proficient solution for predicting the properties of HEA coatings, offering a significant reduction in experimental work. The ML regressions including Support Vector Regression (SVR), Gaussian Process Regression (GPR), Ridge Regression (RR), and Polynomial Regression (PR), are effectively employed to predict Young's modulus of HEA coated Stainless Steel (SS) through a significant database. The statistical responses of the developed regression models are analyzed through evaluation indices of Coefficient of determination (R²), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). Among the regression models, the 2-degree PR model stands alone with a high prediction accuracy of R²-0.95, MAE-16.12, and RMSE-21.53. The 2-degree PR model demonstrates a significant correlation between the predicted and experimental Young's modulus, contributing to the accurate prediction of unknown Young's modulus of the HEA-coated SS. The prediction of Young's modulus by the PR model is more reliable, as proved by an error percentile of ±4.76 %, compared to the experimental values of Young's modulus.