Department of Applied Physics, Aalto University, PO Box 11000, 00076, Aalto, Finland.
NOMATEN Centre of Excellence, National Centre for Nuclear Research, A. Soltana 7, 05-400, Otwock-Swierk, Poland.
Sci Rep. 2023 Jul 10;13(1):11114. doi: 10.1038/s41598-023-37633-9.
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 [Formula: see text] ranging from 0.25 to 0.32) are achieved already with a relatively small dataset ([Formula: see text] 5000 sub-millimeter grains).
镁合金是最轻的结构材料之一，是轻量应用的优秀候选材料。然而，由于强度和延展性相对较低，其工业应用仍然有限。固溶合金化已被证明可以在相对较低的浓度下提高镁的延展性和可成形性。锌溶质具有显著的成本效益和普遍性。然而，添加溶质导致延展性提高的内在机制仍存在争议。在这里，我们通过使用数据科学方法对晶粒内特性进行高通量分析，研究了多晶 Mg 以及 Mg-Zn 合金中位错密度的演变。我们应用机器学习技术比较了合金化前后以及变形前后的电子背散射衍射（EBSD）图像，以提取单个晶粒的应变历史，并预测合金化和变形后的位错密度水平。我们的结果很有希望，因为即使在相对较小的数据集（[Formula: see text] 5000 个亚毫米晶粒）中，也已经可以实现中等预测（决定系数 [Formula: see text] 范围从 0.25 到 0.32）。
