| About this Abstract |
| Meeting |
2021 TMS Annual Meeting & Exhibition
|
| Symposium
|
AI/Data informatics: Tools for Accelerated Design of High-temperature Alloys
|
| Presentation Title |
Predicting Yield Stress of High Temperature Alloys via Computer Vision and Machine Learning |
| Author(s) |
Nan Gao, Zongrui Pei, Youhai Wen, Michael Gao, Elizabeth Holm |
| On-Site Speaker (Planned) |
Nan Gao |
| Abstract Scope |
Understanding the linkage between microstructure and properties is especially important to material design for high temperature performance. Generally, microstructures are characterized by visual inspection and metallographic measurements. Although morphology information can be captured and observed, the rich, multiscale microstructural feature data contained in a typical micrograph is rarely fully quantified or exploited. In this research, pre-trained convolutional deep neural networks (CNNs) are used to extract visual information from images, and machine learning methods are trained to make predictions of mechanical properties based on features that exist at a hierarchy of length scales. The temperature-dependent yield stress of steel alloys is predicted with good fidelity, and links to microstructural features that influence mechanical response are made. We find that computer vision and machine learning are promising tools for connecting microstructure to properties. |
| Proceedings Inclusion? |
Planned: |