Prediction of mechanical properties of Mg-rare earth alloys by machine learning

In this work, the quantitative relationship among the composition, processing history and mechanical properties of Magnesium-rare earth alloys was established by machine learning (ML). Based on support vector regression (SVR) algorithm, ML models were established with inputs of 310 sets of data, which can predict ultimate tensile strength (UTS), yield strength (YS) and elongation (EL) with well accuracy. In order to verify the general applicability of our model, new data were collected from the literature, and the ML models was used to predict their mechanical properties respectively. The MAPE of UTS, YS and EL predicted by SVR model are 9%, 12% and 36%, respectively. The reasons for the deviation of the predicted results were also analyzed. The effects of rare earth elements on UTS, YS and EL were analyzed by the SVR models. The established ML model was used to recommend the composition and processing history of new Magnesium-rare earth alloys with high mechanical properties.

Automated summary

In this study, a quantitative relationship between the composition, processing history, and mechanical properties of Magnesium-rare earth alloys was established using machine learning. Support vector regression (SVR) algorithms were employed to develop ML models with 310 sets of data, accurately predicting the ultimate tensile strength, yield strength, and elongation. The general applicability of the models was verified by collecting and predicting new data from the literature, resulting in a mean absolute percentage error (MAPE) of 9% for UTS, 12% for YS, and 36% for EL. The effects of rare earth elements on mechanical properties were analyzed, and the ML models were utilized to recommend the composition and processing history of new high-strength Magnesium-rare earth alloys.