Deep Learning applied to EBSD; State of the art and perspective
1 : Laboratoire dÉtude des Microstructures et de Mécanique des Matériaux
Université de Lorraine, Centre National de la Recherche Scientifique, Arts et Métiers Sciences et Technologies, Centre National de la Recherche Scientifique : UMR7239
2 : Labex DAMAS
Université de Lorraine
Deep learning is ideal for dealing with large amounts of data, but crystalline orientation data from EBSD is an exception. Most of the work published in the field exploits features derived from EBSD, but very few works directly employ the rich orientation data. In this paper, we will review the state of the art of what has been tested to process orientation data in deep learning models, and outline possible perspectives.
| Subject : | : | Oral |
| Topics | : | Numerical methods |
| Keywords | : | EBSD ; Deep Learning ; Cristalline orientation |
| PDF version | : |  PDF version |
