| About this Abstract |
| Meeting |
2024 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing Modeling, Simulation and Machine Learning
|
| Presentation Title |
Multi-Information Source Thermal Modeling for Design of Printable Refractory Alloys |
| Author(s) |
Brent G. Vela, Peter Morcos, Cafer Acemi, Ibrahim Karaman, Alaa Elwany, Raymundo Arroyave |
| On-Site Speaker (Planned) |
Brent G. Vela |
| Abstract Scope |
Refractory alloys pose challenges in conventional processing methods due to the high strength and brittle nature of these materials. However, additive manufacturing (AM) can potentially circumvent these issues, enabling the fabrication of complex shapes with less material. Despite this potential, there is a lack of experimental data regarding printing of refractory alloys. Conducting AM simulation and experiments is not suitable for high throughput (HTP) alloy design. In this study we propose a design approach for printable alloys within the Nb-Ta-W chemistry-process space. We combine the HTP but low-fidelity (Eagar-Tsai model) and high-fidelity (Thermo-Calc Additive Manufacturing Module) thermal models, along with limited single-track experiments. By leveraging Bayesian updating and networks of gaussian processes, we integrate information from two thermal models and experimental data. This approach enables us to improve the accuracy of printability predictions within this chemistry-process space. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, ICME, Machine Learning |