# Artificial Intelligence-Enabled Crystallographic Texture Quantification: A Comprehensive Review of Machine Learning, Deep Learning, and Generative Approaches for EBSD Analysis and Orientation Distribution Mapping
- DOI
- 10.5281/zenodo.21550738
- Published
- 2026
- Container
- Not recorded
- Publisher
- Zenodo
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.5281/zenodo.21550738,
title = {\# Artificial Intelligence-Enabled Crystallographic Texture Quantification: A Comprehensive Review of Machine Learning, Deep Learning, and Generative Approaches for EBSD Analysis and Orientation Distribution Mapping},
author = {geruganti, sudhakar},
year = {2026},
doi = {10.5281/zenodo.21550738},
url = {https://doi.org/10.5281/zenodo.21550738}
}RIS
TY - JOUR TI - # Artificial Intelligence-Enabled Crystallographic Texture Quantification: A Comprehensive Review of Machine Learning, Deep Learning, and Generative Approaches for EBSD Analysis and Orientation Distribution Mapping AU - geruganti, sudhakar PY - 2026 DO - 10.5281/zenodo.21550738 UR - https://doi.org/10.5281/zenodo.21550738 ER -
APA
sudhakar, G. (2026). # Artificial Intelligence-Enabled Crystallographic Texture Quantification: A Comprehensive Review of Machine Learning, Deep Learning, and Generative Approaches for EBSD Analysis and Orientation Distribution Mapping. https://doi.org/10.5281/zenodo.21550738