Huang, S.-J.; Adityawardhana, Y.; Sanjaya, J. Predicting Mechanical Properties of Magnesium Matrix Composites with Regression Models by Machine Learning. J. Compos. Sci. 2023, 7, 347. https://doi.org/10.3390/jcs7090347
Huang, S.-J.; Adityawardhana, Y.; Sanjaya, J. Predicting Mechanical Properties of Magnesium Matrix Composites with Regression Models by Machine Learning. J. Compos. Sci. 2023, 7, 347. https://doi.org/10.3390/jcs7090347
Huang, S.-J.; Adityawardhana, Y.; Sanjaya, J. Predicting Mechanical Properties of Magnesium Matrix Composites with Regression Models by Machine Learning. J. Compos. Sci. 2023, 7, 347. https://doi.org/10.3390/jcs7090347
Huang, S.-J.; Adityawardhana, Y.; Sanjaya, J. Predicting Mechanical Properties of Magnesium Matrix Composites with Regression Models by Machine Learning. J. Compos. Sci. 2023, 7, 347. https://doi.org/10.3390/jcs7090347
Abstract
Magnesium matrix composites have attracted significant attention due to their lightweight nature and impressive mechanical properties. However, the fabrication process for these alloy compo-sites is often time-consuming, expensive, and labor-intensive. To overcome these challenges, this study employed machine learning (ML) techniques to predict the mechanical properties of magnesium matrix composites. Regression models were utilized to forecast the yield strength of magnesium alloy composites reinforced with various materials. The study incorporated previous research on matrix type, reinforcement type, heat treatment, and mechanical working. The re-gression models employed in this study included decision tree regression, random forest re-gression, extra tree regression, and XGBoost regression. Model performance was assessed using metrics such as RMSE and R2. The XGBoost Regression model out-performed others, exhibiting an R2 value of 0.94 and the lowest error rate. Feature importance analysis indicated that the rein-forcement particle form had the greatest influence on the mechanical properties. The study iden-tified the optimized parameters for achieving the highest yield strength, which was 186.99 MPa. Overall, this study successfully demonstrates the effectiveness of ML as a valuable tool for opti-mizing the production parameters of magnesium matrix composites.
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