About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Application of Vision Transformers in Tomography Image Segmentations of AM Parts |
Author(s) |
Saber Nemati, Les G Butler, Shengmin Guo |
On-Site Speaker (Planned) |
Saber Nemati |
Abstract Scope |
Constructing intelligent networks for automatic and fast image segmentation of tomography images has gained a lot of attention during the last few years. Different variants of Convolutional Neural Networks (CNNs) have shown great performance in supervised segmentation using very limited training data. However, the challenge of improving accuracy and speed especially during the training phase is still ongoing for real-time purposes. In this research, Vision Transformers (ViT) are investigated for improving prediction accuracy and computational cost. The effectiveness of ViT is demonstrated using two different sets of tomography images. The results show improvement in the training speed and attention to global features in Vision Transformers, which makes them a possible candidate for in-situ process monitoring in materials engineering applications. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Additive Manufacturing, Other |