Interpretable machine learning excavates a low-alloyed magnesium alloy with strength-ductility synergy based on data augmentation and reconstruction

Abstract

The application of machine learning in alloy design is increasingly widespread, yet traditional models still face challenges when dealing with limited datasets and complex nonlinear relationships. This work proposes an interpretable machine learning method based on data augmentation and reconstruction, excavating high-performance low-alloyed magnesium (Mg) alloys. The data augmentation technique expands the original dataset through Gaussian noise. The data reconstruction method reorganizes and transforms the original data to extract more representative features, significantly improving the model's generalization ability and prediction accuracy, with a coefficient of determination (R2) of 95.9 % for the ultimate tensile strength (UTS) model and a R2 of 95.3 % for the elongation-to-failure (EL) model. The correlation coefficient assisted screening (CCAS) method is proposed to filter low-alloyed target alloys. A new Mg-2.2Mn-0.4Zn-0.2Al-0.2Ca (MZAX2000, wt%) alloy is designed and extruded into bar at given processing parameters, achieving room-temperature strength-ductility synergy showing an excellent UTS of 395 MPa and a high EL of 17.9 %. This is closely related to its hetero-structured characteristic in the as-extruded MZAX2000 alloy consisting of coarse grains (16 %), fine grains (75 %), and fiber regions (9 %). Therefore, this work offers new insights into optimizing alloy compositions and processing parameters for attaining new high strong and ductile low-alloyed Mg alloys.

Keywords

• Magnesium alloy
• Interpretable machine learning
• Alloy design
• Hetero-structure
• Strength-ductility synergy