| 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 |
L-25: Multi-class Inclusion Identification via Machine Learning of Multilevel Image Features |
| Author(s) |
Nan Gao, Mohammad Abdulsalam, Bryan Webler, Elizabeth Holm |
| On-Site Speaker (Planned) |
Nan Gao |
| Abstract Scope |
Multilevel image features including contrast and morphology are investigated via computer vision (CV) and machine learning (ML) for inclusion classification. In the steel industry, distinguishing inclusions from steel substrates relies heavily on Energy Dispersive X-Ray Spectroscopy (EDS) equipped on a Scanning Electron Microscope (SEM). But more efficient, timely and cost-effective analysis methods are still needed since EDS is time-consuming for element analysis. Considering the capability of pulling out high level features and superfast processing speed via CV and ML, these techniques offer us opportunities to solve this issue. State-of-the-art pretrained CNNs are utilized to capture morphologic features. Additionally, color features using histogram and color moment representations are incorporated into features vectors for the formation of multilevel features. Fewer classification errors were observed especially for inclusions with similar chemical composition. This study can be used to explore the potential of using CV and ML instead of SEM/EDS for element analysis. |
| Proceedings Inclusion? |
Planned: Supplemental Proceedings volume |