| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
Data Science and Analytics for Materials Imaging and Quantification
|
| Presentation Title |
Computer Vision and Machine Learning for Microstructural Characterization and Analysis |
| Author(s) |
Elizabeth A. Holm, Ryan Cohn, Nan Gao, Katelyn Jones, Bo Lei, Srujana Yarasi |
| On-Site Speaker (Planned) |
Elizabeth A. Holm |
| Abstract Scope |
The characterization and analysis of microstructure is the foundation of microstructural science, connecting the materials structure to its composition, process history, and properties. Microstructural quantification traditionally involves a human deciding a priori what to measure and then devising a purpose-built method for doing so. However, recent advances in data science, including computer vision (CV) and machine learning (ML) offer new approaches to extracting information from microstructural images. This talk presents CV approaches to numerically encode the visual information contained in a microstructural image, which then provides input to supervised or unsupervised ML algorithms that find associations and trends in the high-dimensional image representation. CV/ML systems for microstructural characterization and analysis include image classification, semantic segmentation, object detection, and instance segmentation. These tools enable new approaches to microstructural analysis, including the development of new visual metrics. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Characterization, Computational Materials Science & Engineering |