Automatic Determination of the Weak-Beam Condition in Dark Field X-ray Microscopy

• Published in: Integrating materials and manufacturing innovation Vol. 12; no. 2; pp. 83 - 91
• Main Authors: Huang, Pin-Hua, Coffee, Ryan, Dresselhaus-Marais, Leora
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2023; Springer Nature B.V; Springer
• Subjects: 3D Materials Science 2022; Characterization and Evaluation of Materials; Chemistry and Materials Science; Crystal defects; Crystal dislocations; Crystallography; Datasets; DFXM; diffraction imaging processing; dislocations; Image reconstruction; MATERIALS SCIENCE; Mechanical properties; Metallic Materials; Multidimensional methods; Nanotechnology; orthogonalization; Structural Materials; Surfaces and Interfaces; Thematic Section: 3D Materials Science 2022; Thin Films; X ray microscopy; DFXM; Orthogonalization; Diffraction imaging processing; Dislocations
• ISSN: 2193-9764; 2193-9772
• DOI: 10.1007/s40192-023-00295-6

Mechanical properties in crystals are determined by 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., 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.