Plastic anisotropy and twin distributions near the fatigue crack tip of textured Mg alloys from in s
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Hexagonal metals have anisotropic coefficients of thermal expansion causing grain-level internal stresses during heating. High energy x-ray diffraction microscopy, a non-destructive, in situ, micromechanical and microstructural characterization technique, has been used to determine the anisotropic coefficients of thermal expansion (CTEs) for Ti-7Al. Two samples of polycrystalline α-phase Ti-7Al were continuously heated from room temperature to 850 °C while far-field HEDM scans were collected. The results showed a change in the ratio of the CTEs in the ‘a’ and ‘c’ directions which explains discrepancies found in the literature. The CTE additionally appears to be affected by the dissolution of α 2 precipitates. Analysis of the grain-resolved micromechanical data also shows reconfiguration of the grain scale stresses likely due to anisotropic expansion driving crystallographic slip.
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X-ray diffraction (XRD) data analysis can be a time-consuming and laborious task. Deep neural network (DNN) based models trained with synthetic XRD patterns have been proven to be a highly efficient, accurate, and automated method for analyzing common XRD data collected from solid samples in ambient environments. However, it remains unclear whether synthetic XRD-based models can be effective in solving micro(μ)-XRD mapping data for in situ experiments involving liquid phases, which always have lower quality and significant artifacts. In this study, we collected μ-XRD mapping data from a LaCl 3 -calcite hydrothermal fluid system and trained two categories of models to analyze the experimental XRD patterns. Here, the models trained solely with synthetic XRD patterns showed low accuracy (as low as 64%) when solving experimental μ-XRD mapping data. However, the accuracy of the DNN models significantly improved (90% or above) when we trained them with a data set containing both synthetic and a small number of labeled experimental μ-XRD patterns. This study highlights the importance of labeled experimental patterns in training DNN models to solve μ-XRD mapping data from in situ experiments involving liquid phases.
In the present work, we develop a comprehensive functional phase diagram for the Ba–Ca–Ta–O quaternary system Ba 3 Ca 1+ x Ta 2− x O 9−3 x /2 (0 ≤ x ≤ 0.36) between 1000 and 1550 °C, coupled with theoretical calculations of the cationic ordering in supercells.
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