| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
3rd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2025)
|
| Presentation Title |
MLOgraphy++: A Context-Enhanced U-Net Approach for Robust Grain Boundary Segmentation in Metallographic Images |
| Author(s) |
Inbal Cohen, Julien Robitaille, Francis Quintal Lauzon, Ofer Beeri, Shai Avidan, Gal Oren |
| On-Site Speaker (Planned) |
Gal Oren |
| Abstract Scope |
Our work addresses the challenge of accurately identifying grain boundaries in metallographic images, where intricate texture boundaries complicate segmentation. Current state-of-the-art models, such as the Segment Anything Model (SAM), require prompts based on prior grain knowledge, limiting their usability in texture-only segmentation tasks. Manual annotation is also time-consuming and subjective. Existing methods often rely on small, annotated patches with post-processing steps, which can lead to overfitting and reduced generalizability. We introduce MLOgraphy++, a U-Net-based approach that leverages large context windows with partial labels, eliminating the need for post-processing. MLOgraphy++ effectively handles incomplete boundaries during inference by training with contextual variation. Using the Heyn intercept method as a more representative evaluation metric, we benchmark MLOgraphy++ against the state-of-the-art MLOgraphy on the Texture Boundary in Metallography (TBM) dataset, showing it achieves comparable results while enhancing generalizability and eliminating post-processing requirements. |
| Proceedings Inclusion? |
Undecided |