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

Abstract

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.

Machine Learning Approaches for Predicting the Ablation Performance of Ceramic Matrix Composites

“…To develop a more reliable model for regression problems and increasing the performance from decision tree temperature prediction model, random forest (RF) regression was introduced which entailed training an ensemble of decision trees on a random subset of the bootstrap sampled data, and then combining their predictions following bootstrap aggregations [37]. In this study, the row sampling with replacement method was utilized to collect a subset (bootstrap sampled) of torch test time-temperature data (d) from the total dataset d, where d > d. Then, the subset of time-temperature data was passed through the five individual base learner trees having a constant depth of 3 in each tree.…”

“…This conventional machine learning technique was also used to simulate the modulus of elasticity and compressive stress-strain curves. The decision tree, extra tree, XGBoost, and random forest regression models' feature relevance analyses revealed that the magnesium matrix composites' reinforcing particle form had the biggest impact on their mechanical properties [37].…”