Autor: |
Huang, Pin-Hua, Coffee, Ryan, Dresselhaus-Marais, Leora |
Rok vydání: |
2022 |
Předmět: |
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Zdroj: |
Integrating Materials and Manufacturing Innovation, 12, 83-91 (2023) |
Druh dokumentu: |
Working Paper |
DOI: |
10.1007/s40192-023-00295-6 |
Popis: |
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 ($x$, $y$, $\phi$) 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. |
Databáze: |
arXiv |
Externí odkaz: |
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