Predicting Mechanical Properties of Magnesium Matrix Composites with Regression Models by Machine Learning

Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy composites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study introduces a novel use of machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites, providing an innovative and cost-effective alternative to conventional methods. Various regression models, including decision tree regression, random forest regression, extra tree regression, and XGBoost regression, were employed to forecast the yield strength of magnesium alloy composites reinforced with diverse materials. This approach leverages existing research data on matrix type, reinforcement type, heat treatment, and mechanical working. The XGBoost Regression model outperformed the others, exhibiting an R2 value of 0.94 and the lowest error rate. Feature importance analysis from the best model indicated that the reinforcement particle form had the most significant influence on the mechanical properties. Our research also identified the optimized parameters for achieving the highest yield strength at 186.99 MPa. This study successfully demonstrated the effectiveness of ML as a valuable, novel tool for optimizing the production parameters of magnesium matrix composites.

Automated summary

By introducing machine learning techniques, this study successfully predicted the mechanical properties of magnesium matrix composites and proposed an innovative and cost-effective alternative. The XGBoost regression model performed the best, and the form of reinforcement particles had the most significant influence on the mechanical properties.