About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
M-13: Synthetic Data-assisted Unsupervised Domain Adaptation for Hierarchical Microstructure Reconstruction |
Author(s) |
Ali Riza Durmaz, Muhammad Kashan Karim, Oleg Shchyglo, Akhil Thomas, Chris Eberl |
On-Site Speaker (Planned) |
Ali Riza Durmaz |
Abstract Scope |
In materials science, a variety of full-field simulation tools are available which provide solid estimates of crystal growth or damaging processes. While these rule-based approaches currently do not model relations comprehensively, they can provide synthetic and low-cost data to train statistical models along with real-world data. The presented work utilizes synthetic data from phase-field simulations of martensite formation along with corresponding experimental micrographs to improve the analysis of the latter. Specifically, the image segmentation of prior austenite grain boundaries is tackled, which has implications for a range of different materials properties such as crack growth in fatigue. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Phase Transformations, Characterization |