About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
|
Presentation Title |
Comparing Microstructure Representations for Machine Learning Models Predicting Material Properties |
Author(s) |
Akhil Thomas, Ali Riza Durmaz, Harald Sack, Chris Eberl |
On-Site Speaker (Planned) |
Ali Riza Durmaz |
Abstract Scope |
For making effective and efficient machine learning (ML) models that predict material properties, it is necessary to identify the right kind of representations of its input material structure and/or process, besides optimizing over different types of ML models. In the presented work, we take the example of initial fatigue damage prediction using a multi-modal High-Cycle-Fatigue dataset and explore various input microstructure representations for the task. In particular, we examine and compare various representations of Electron Backscatter Diffraction (EBSD) data and other modalities registered to EBSD. Various input representations allow different types of ML models to be used, which are also presented. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Computational Materials Science & Engineering, Characterization |