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 ...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.Cat Boost yielded the highest prediction accuracy with R^(2) 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 R^(2) 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.展开更多
基金supported by Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950)。
文摘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.Cat Boost yielded the highest prediction accuracy with R^(2) 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 R^(2) 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.
