| About this Abstract |
| Meeting |
2022 TMS Annual Meeting & Exhibition
|
| Symposium
|
Algorithm Development in Materials Science and Engineering
|
| Presentation Title |
Chemistry and Processing History Prediction from Materials Microstructure by Deep Learning |
| Author(s) |
Amir Abbas Kazemzadeh, Mahmood Mamivand |
| On-Site Speaker (Planned) |
Amir Abbas Kazemzadeh |
| Abstract Scope |
Finding the chemical composition and processing history from a microstructure morphology for heterogeneous materials is desired in many applications. In this work, we develop a framework based on a deep learning approach that enables us to predict the chemistry and processing history just by reading the morphological distribution of one element. We used a dataset from spinodal decomposition simulation of FeCrCo alloy created by the phase-field method as a case study. We develop specific algorithms to manage the mixed dataset, including both images and continuous data. Results show that while shallow networks are efficient in chemistry prediction, deep networks are required to predict the processing temperature accurately. The physical concepts behind these observations will be discussed. The results also show that transfer learning outperforms the in-house trained network when it comes to microstructure feature extraction. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Machine Learning, Computational Materials Science & Engineering, Modeling and Simulation |