A machine learning-based prediction of crystal orientations for multicrystalline materials
We established a rapid, low-cost, and accurate technique to measure crystallographic orientations in multicrystalline materials by optical images and machine learning. A long short-term memory neural network was trained with pairs of light reflection patterns and the correct orientations of each gra...
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| Auteurs principaux: | , , , , |
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| Format: | Artigo |
| Langue: | Inglês |
| Publié: |
AIP Publishing LLC
2023-06-01
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| Collection: | APL Machine Learning |
| Accès en ligne: | http://dx.doi.org/10.1063/5.0138099 |
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