Automated Analysis Framework of Strain Partitioning and Deformation Mechanisms Via Hrdic-Ebsd Fusion and Computer Vision: Application to a Mg Alloy

• DOI: 10.2139/ssrn.4836423
• Publication Date: 2024-05-21T15:21:06Z
• Authors: Ran Ni; Carl J. Boehlert; Ying Zeng; Bo Chen; Saijun Huang; J. Zheng; Hao Zhou; Qudong Wang; Dongdi Yin
• Keywords: Strain (injury)

Investigation of strain partitioning and the underlying deformation mechanisms for both grain interior boundary (GB) is critical understanding complex plastic hexagonal close-packed (HCP) metals. To this end, an automated analysis framework based on high resolution digital image correlation (HRDIC) EBSD data fusion computer vision, integrating nanoscale a large field view, presented here. The consists of: (1) HRDIC-EBSD fusion; (2) Segmenting into individual grains each with core mantle; (3) Data clustering matrix slip bands (SBs) different directions grain; (4) Full system (SS) identification (including plane direction) assignment SS SBs. capabilities were demonstrated Mg-10Y (wt. %) alloy during room temperature compression, which enabled grain-by-grain analysis. was segmented clusters, including mantle, core, matrix, SBs, then clusters analyzed statistically quantitatively. pixel-based activity, takes account SB morphology, obtained from statistical perspective. GB deformation, involving distribution, transfer, sliding (GBS), quantitatively analyzed. segment results, contained ~1.5% misidentified pixels, manually checked corrected. Overall, provides methodology to automatically analyze containing microstructure features, mechanisms.