Automating Dislocation Characterization in 3D Dark Field X-ray Microscopy
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2022
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| _version_ | 1866918279910522880 |
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| author | Huang, Pin-Hua Coffee, Ryan Dresselhaus-Marais, Leora |
| author_facet | Huang, Pin-Hua Coffee, Ryan Dresselhaus-Marais, Leora |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2211_05247 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Automating Dislocation Characterization in 3D Dark Field X-ray Microscopy Huang, Pin-Hua Coffee, Ryan Dresselhaus-Marais, Leora Materials Science 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. |
| title | Automating Dislocation Characterization in 3D Dark Field X-ray Microscopy |
| topic | Materials Science |
| url | https://arxiv.org/abs/2211.05247 |