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

H. Salmenjoki, S. Papanikolaou, D. Shi, D. Tourret, C. M. Cepeda-Jiménez, M. T. Pérez-Prado, L. Laurson, M. J. Alava*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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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 R2 ranging from 0.25 to 0.32) are achieved already with a relatively small dataset (∼ 5000 sub-millimeter grains).

Original languageEnglish
Article number11114
Pages (from-to)1-7
Number of pages7
JournalScientific Reports
Issue number1
Publication statusPublished - Dec 2023
MoE publication typeA1 Journal article-refereed


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