About this Abstract |
Meeting |
2021 TMS Annual Meeting & Exhibition
|
Symposium
|
Algorithm Development in Materials Science and Engineering
|
Presentation Title |
Deep Learning for Characterization of Deformation Induced Damage |
Author(s) |
Ulrich Kerzel, Setareh Medghalchi, Carl Kusche, Talal Al-Samman, Sandra Korte-Kerzel |
On-Site Speaker (Planned) |
Ulrich Kerzel |
Abstract Scope |
Machine learning and artificial intelligence have made enormous progress in recent years, in particular in computer vision, opening new frontiers in materials research.
One of the main challenges is the analysis of the statistical effects, requiring the automated analysis of many thousands of large scale images at high resolution.
Using the example of DP800 steel, we show that a combination of multiple deep neural networks can reliably identify the most prevalent damage mechanisms at a level comparable to human experts. Using a range of data augmentation as well as regularization techniques, we also demonstrate that this approach can be made more general and flexible to allow the analysis of specimens that have been subjected to different damage and strain paths, without having to obtain a large sample of manually labelled ground truth data to re-train the networks for each new use-case. |
Proceedings Inclusion? |
Planned: |