About this Abstract |
Meeting |
6th International Congress on 3D Materials Science (3DMS 2022)
|
Symposium
|
6th International Congress on 3D Materials Science (3DMS 2022)
|
Presentation Title |
Automated 4D Reconstruction and Data-mining of Dislocation
structures From In-situ TEM Experiments: Synthetic Training Data
generation and Customized Deep Learning Strategies |
Author(s) |
Kishan Govind, Marc Legros, Stefan Sandfeld |
On-Site Speaker (Planned) |
Kishan Govind |
Abstract Scope |
Quantitative TEM imagingof dislocations helps to understand the
structure-property relations directly from experimental data. Such studies are, up to now, typically based
on coarse, geometrical estimates of the curvature of dislocations for a few snapshots in time. Generalizing
this to thousands of frames and/or many dislocation is currently not possible. In this presentation we show
how Deep Learning can be an important tool for data mining of TEM images of dislocation. We
demonstrate how TEM images can be automatically segmented, and how individual dislocations can be
tracked during time. For the training we generate artificial images of dislocation microstructure using a para-
metric model. We demonstrate how such a new, physical data augmentation approach can be used in order
to overcome the common problem of “never enough training data”. This approach is used for the automated data-mining of dislocation motion in a high-entropy alloy. |
Proceedings Inclusion? |
Definite: Other |