| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
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| Symposium
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2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Bayesian Data-augmentation of Thermal Models for Design of Nb-Ta-W Alloys |
| Author(s) |
Brent G. Vela, Cafer Acemi, Peter Morcos, Alaa Elwany, Ibrahim Karaman, Raymundo Arróyave |
| On-Site Speaker (Planned) |
Brent G. Vela |
| Abstract Scope |
Refractory alloys are difficult to process via conventional means due to their high strength and brittle nature. Additive manufacturing (AM) is an emerging solution which can bypass these processing difficulties, enabling the fabrication of refractory alloys into complex shapes with reduced material usage. Despite this, there is limited experimental data on the printability of refractory alloys. Both AM simulation and experimentation are prohibitively expensive and are thus not appropriate for high-throughput alloy design. In this work we propose printable alloys can be designed within the Nb-Ta-W chemistry-process space using a combination low-fidelity (Eagar-Tsai model), high-fidelity (Thermo-Calc Additive Manufacturing Module) thermal models, and limited single-track experiments. Specifically, using the Eagar-Tsai model as our prior belief of printability within this chemistry-process space, we leverage Bayesian-updating and networks of Gaussian Process Regressors to 1) fuse information from both thermal models and 2) to correct this fused-model with experimental data. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |