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

Abstract

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.