About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Denoising of Electron Back Scatter Patterns for Improved EBSD Characterization Using Deep Learning |
Author(s) |
Mani Krishna Karri, Radhakrishnan Madhavan , Mangesh V Pantawane, Ramniwas Singh, Narendra Dahotre |
On-Site Speaker (Planned) |
Mani Krishna Karri |
Abstract Scope |
Electron back scatter diffraction (EBSD) enables in-depth characterization of the microstructures by indexing experimental electron back scatter patterns (EBSPs). The success of the EBSD depends on the quality of the EBSPs. In case of samples with poor EBSPs (highly deformed, nano crystalline, faceted or in-situ experiments) EBSD characterization is quite challenging and existing automated EBSP indexing methods largely fail.
In this work, a novel machine learning (ML) method of refining noisy EBSPs is proposed. For this, conditional generative adversarial neural networks (c-GAN) have been employed. The ML model was trained using 10000 EBSPs acquired under different settings and samples (additively manufactured FCC, BCC and HCP alloys) ensuring enough diversity and complexity in training data set. The trained model has brought out an improvement of more than 3 times in EBSD indexing success rate on test data, accompanied by betterment of indexing accuracy. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Machine Learning, Computational Materials Science & Engineering |