Indexing of electron back-scatter diffraction patterns using a convolutional neural network
| Autor: | Zihao Ding, M. De Graef, Elena Pascal |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
010302 applied physics
Diffraction Materials science Polymers and Plastics business.industry Search engine indexing Metals and Alloys Pattern recognition 02 engineering and technology 021001 nanoscience & nanotechnology 01 natural sciences Convolutional neural network Electronic Optical and Magnetic Materials Visualization Backscatter X-ray Robustness (computer science) 0103 physical sciences Data_FILES Ceramics and Composites Artificial intelligence 0210 nano-technology business |
| Zdroj: | Acta Materialia. 199:370-382 |
| ISSN: | 1359-6454 |
| DOI: | 10.1016/j.actamat.2020.08.046 |
| Popis: | Accurate indexing of EBSD patterns presents a challenging problem. We propose a new convolutional neural network (EBSD-CNN) to realize real-time indexing of EBSD patterns; we implement a disorientation loss function to adapt a standard CNN model for crystallographic orientation indexing. The indexing accuracy, rate, and robustness against noise are evaluated using both simulated and experimental data, and compared with other indexing methods (Hough-based indexing, dictionary indexing, and spherical indexing). The results suggest that a CNN can provide an alternative to commercial Hough-transform-based indexing with comparative accuracy and rate. We obtain insight into the network functionality by visualization of selected filters. |
| Databáze: | OpenAIRE |
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