@article{Jain2025, 
author = {Reliance Jain and Sandeep Jain and Sheetal Kumar Dewangan and Sumanta Samal and Hansung Lee and Eunhyo Song and Younggeon Lee and Byungmin Ahn},
title = {Prediction of alloying element effects on the mechanical behavior of high-pressure die-cast Mg-based alloys},
year = {2025},
journal = {Journal of Magnesium and Alloys},
volume = {13},
number = {8},
pages = {3819-3828},
keywords = {Lightweight alloys, High-pressure die casting, Machine learning, Predictive analysis, Alloys development},
url = {https://www.sciopen.com/article/10.1016/j.jma.2025.06.023},
doi = {10.1016/j.jma.2025.06.023},
abstract = {Achieving optimal mechanical performance in high-pressure die-cast (HPDC) Mg-based alloys through experimental methods is both costly and time-intensive due to significant variations in composition. This study leverages machine learning (ML) techniques to accelerate the development of high-performance Mg-based alloys. Data on alloy composition and mechanical properties were collected from literature sources, focusing on HPDC Mg-based alloys. Six ML models—extra trees, CatBoost, k-nearest neighbors, random forest, gradient boosting, and decision tree—were trained to predict mechanical behavior. CatBoost yielded the highest prediction accuracy with R2 scores of 0.95 for ultimate tensile strength (UTS) and 0.92 for yield strength (YS). Further validation using published datasets reaffirmed its reliability, demonstrating R2 values of 0.956 (UTS) and 0.936 (YS), MAE of 1% and 2.8%, and RMSE of 1% and 3.5%, respectively. Among these, the CatBoost model demonstrated the highest predictive accuracy, outperforming other ML techniques across multiple optimization metrics.}
}