Predicting wear resistance of high-carbon Cr-V alloy steel based on machine learning

The microstructure of Cr-V wear-resistant alloy steel was characterized by its complexity, with the evaluation of its wear resistance exhibiting multidimensional, strongly coupled, and nonlinear attributes. Contrary to the conventional research paradigm that relied on microstructure analysis and performance comparison, accurate prediction of wear resistance based on data-driven approaches was found to significantly expedite the research and development process for materials. In this study, microstructural analysis demonstrated that the grid-like arrangement of M7C3, MC, and M2C eutectic carbides, along with the dispersed granular M23C6 carbides within the matrix, significantly enhanced material hardness. The uniform distribution of carbides played a critical role in resisting abrasive wear. This investigation further introduced the mass score ratios of (Cr+V)/C and V/Cr as evaluation indices for carbides to optimize the characteristic dataset. The findings unequivocally revealed that optimization of the feature set significantly enhanced the performance of the predictive models, with the correlation coefficient (R) value showing an increase of 3.17–25.18 %, while the root mean square error (RMSE) value decreased by 33.26–71.15 %. Compared to the Random Forest (RF), Gradient Boosting Trees (GBT), and Kernel Ridge Regression (KRR) models，the Support Vector Regression(SVR) model demonstrated preeminent performance, evidenced by an R value of 0.979 and an RMSE value of 0.866 mg. The application of the trained SVR model to predict new samples yielded satisfactory results, with the predicted wear resistance rankings being entirely consistent with the experimental outcomes and exhibiting high prediction accuracy. The model possessed robust quantitative predictive capabilities, enabling rapid evaluation of the wear resistance of this category of steel. [Display omitted]