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

ABSTRACT: 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

SUBMITTER: Salmenjoki H 

PROVIDER: S-EPMC10333208 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature