| About this Abstract |
| Meeting |
Materials Science & Technology 2020
|
| Symposium
|
Materials Informatics and Modeling for 21st Century Ceramics Research
|
| Presentation Title |
Predicting Stress Hotspots in Polycrystalline Materials from Microstructural Features Using Deep Learning |
| Author(s) |
Ankit Shrivastava, Hae Young Noh, Kaushik Dayal |
| On-Site Speaker (Planned) |
Ankit Shrivastava |
| Abstract Scope |
In polycrystalline materials, induced high stress in certain regions, so-called stress hotspots influence material strength. It is observed that local microstructural features such as grain boundaries and junctions, heavily influence these hotspots. In current work, we propose an algorithm to predict hotspots from microstructural features using a convolutional encoder-decoder model. We use images of 128X128 dimensions containing grain misorientation information of a single-phase cubic microstructure as our input. Output to the model is normalized values of Frobenius-norm of von mises stress obtained by solving linear elastic equations under prescribed strains. Since the input is a very high dimensional image, it makes the convolutional encoder-decoder a suitable choice to capture features and predict the hotspots. Using the output from the model, the hotspot location is estimated by thresholding the values in the outputs. The primary result shows that the model can reconstruct the output image with average accuracy with 77%. |