| About this Abstract |
| Meeting |
TMS Specialty Congress 2025
|
| Symposium
|
3rd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2025)
|
| Presentation Title |
Out-of-Distribution Surface Anomaly Detection Using Masked Autoencoder Vision Transformers |
| Author(s) |
Pierre Belamri, Henry Proudhon, David Ryckelynck, Damien Texier |
| On-Site Speaker (Planned) |
Pierre Belamri |
| Abstract Scope |
Automated surface-anomaly detection using machine learning has become a promising area of research with high impact on visual inspection. However, traditional supervised models rely on large, labelled datasets, making them difficult to apply in this context. Large Vision Transformers, leveraging masked image modelling, offer a solution by creating modality-agnostic latent spaces that can enhance multimodal materials characterization.
We propose a self-supervised learning approach for out-of-distribution anomaly detection using EBSD orientation maps, a key modality in material science as it provides direct information on grains orientations. A Vision Transformer Masked Autoencoder is trained to reconstruct quaternions maps, learning latent representations that capture grain boundaries and orientation patterns.
Our results show clear differences in Mahalanobis distances distributions for twin-free and twin-containing samples, indicating the model’s ability to detect anomalies linked to sigma-3 grain boundaries and/or twin geometries. This demonstrates the potential of multimodal transformers for defect detection in polycrystalline materials. |
| Proceedings Inclusion? |
Undecided |