| About this Abstract |
| Meeting |
2023 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advances in Multi-Principal Element Alloys II
|
| Presentation Title |
J-18: Crystal Plasticity Modeling and Machine Learning for High-Strength, High-Temperature Alloys |
| Author(s) |
Stephanie Taylor, Jaime Marian, Amartya Banerjee |
| On-Site Speaker (Planned) |
Stephanie Taylor |
| Abstract Scope |
Materials that can withstand extremely high temperature environments are desirable to identify for application in the next generation of energy technologies. Such environments are difficult to study experimentally, so computational techniques find a relevant niche here. To this end, we have pursued a novel computational approach by coupling crystal plasticity (CP) simulations and machine learning (ML) to explore unique multi-principle metal alloy compositions capable of high temperature strength. Results from these CP simulations and implemented ML algorithms are presented. Special focus is paid to discussing the properties that are well-captured by the combination of these techniques. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Modeling and Simulation, High-Temperature Materials, Machine Learning |