Machine learning dislocation density correlations and solute effects in Mg-based alloys

Magnesium alloys, among the lightest structural materials, represent excellent candidates for lightweight applications. However, industrial applications remain limited due to relatively low strength and ductility. Solid solution alloying has been shown to enhance Mg ductility and formability at relatively low concentrations. Zn solutes are significantly cost effective and common. However, the intrinsic mechanisms by which the addition of solutes leads to ductility improvement remain controversial. Here, by using a high throughput analysis of intragranular characteristics through data science approaches, we study the evolution of dislocation density in polycrystalline Mg and also, Mg–Zn alloys. We apply machine learning techniques in comparing electron back-scatter diffraction (EBSD) images of the samples before/after alloying and before/after deformation to extract the strain history of individual grains, and to predict the dislocation density level after alloying and after deformation. Our results are promising given that moderate predictions (coefficient of determination R ranging from 0.25 to 0.32) are achieved already with a relatively small dataset (∼ 5000 sub-millimeter grains).

HS acknowledges the support from Finnish Foundation for Technology Promotion. MA and SP acknowledge support from the European Union Horizon 2020 research and innovation programme under grant agreement No 857470 and from European Regional Development Fund via Foundation for Polish Science International Research Agenda PLUS programme grant No MAB PLUS/2018/8. LL acknowledges the support of the Academy of Finland via the Academy Project COPLAST (Project no. 322405). DT gratefully acknowledges support from the Spanish Ministry of Science through a Ramon y Cajal Fellowship (Ref. RYC2019-028233-I). CMCJ acknowledges the financial support from the Spanish Ministry of Science, Innovation and Universities under project PID2020-118626RB-I00. The research leading to these results has received funding from the Spanish Ministry of Science, Innovation and Universities under project PID2019-111285RB-I00.