论文标题
3D暗场X射线显微镜中的自动脱位表征
Automating Dislocation Characterization in 3D Dark Field X-ray Microscopy
论文作者
论文摘要
晶体中的机械性能与1D线缺陷的排列密切相关，该缺陷称为位错。最近，黑场X射线显微镜（DFXM）已成为使用多维扫描在晶体中图像和解释脱位的新工具。但是，重建高维DFXM扫描中有意义的脱位信息所需的方法仍然是偏生的，需要大量的手动监督（即\ textit {succorion {succorion}）。在这项工作中，我们提出了一种新的相对无监督的方法，该方法使用革兰氏 - schmidt正交化来从3D数据集（$ x $，$ y $，$ ϕ $）中提取特定于位错的信息（功能），以代表大型数据集作为每个位置的3件组件的数组，每个位置都适用于每个位置，对应于弱小的条件和强度较强的条件。此方法提供了重要的机会，可以显着减少数据集大小，同时仅保留对于数据重建很重要的晶体学信息。
Mechanical properties in crystals are strongly correlated to the arrangement of 1D line defects, termed dislocations. Recently, Dark field X-ray Microscopy (DFXM) has emerged as a new tool to image and interpret dislocations within crystals using multidimensional scans. However, the methods required to reconstruct meaningful dislocation information from high-dimensional DFXM scans are still nascent and require significant manual oversight (i.e. \textit{supervision}). In this work, we present a new relatively unsupervised method that extracts dislocation-specific information (features) from a 3D dataset ($x$, $y$, $ϕ$) using Gram-Schmidt orthogonalization to represent the large dataset as an array of 3-component feature vectors for each position, corresponding to the weak-beam conditions and the strong-beam condition. This method offers key opportunities to significantly reduce dataset size while preserving only the crystallographic information that is important for data reconstruction.
