| About this Abstract |
| Meeting |
2020 TMS Annual Meeting & Exhibition
|
| Symposium
|
ICME Gap Analysis in Materials Informatics: Databases, Machine Learning, and Data-Driven Design
|
| Presentation Title |
Computational Classification, Generation and Time-evolution Prediction of Alloy Microstructures with Deep Learning |
| Author(s) |
Fei Zhou |
| On-Site Speaker (Planned) |
Fei Zhou |
| Abstract Scope |
The solidification conditions and resulting microstructures have direct and decisive effects on the mechanical properties of alloys, and are therefore of utmost importance for their performance. However, the length and time scales involved in microstructures are extremely demanding for computational modeling efforts. We demonstrate the usefulness of deep learning approaches in processing, analyzing, and quantifying electron micrograph images of alloys, including classification of microstructure types with convolutional neural networks (CNN), generation of new microstructure images with generative adversarial networks (GAN), and prediction of microstructural time evolution with recurrent neural networks (RNN). |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |