About this Abstract |
| Meeting |
2026 TMS Annual Meeting & Exhibition
|
| Symposium
|
Additive Manufacturing of Refractory Metallic Materials
|
| Presentation Title |
Comparison of predictive and experimental refractory alloy design enabled by DED |
| Author(s) |
Daniel R. Sinclair, Bryan Webler, Kareem Abdelmaqsoud, Amaranth Karra, John Kitchin, S. Mohadeseh Taheri-Mousavi |
| On-Site Speaker (Planned) |
Daniel R. Sinclair |
| Abstract Scope |
Powder-based additive manufacturing (AM) offers a route to produce complex parts from refractory elements including tungsten, which demonstrates exceptional operating temperatures but is difficult to machine and join using traditional methods. The primary challenge in the AM of tungsten continues to be brittle cracking, initiated by a combination of large thermal stresses, crack-prone microstructures, and a high ductile-to-brittle transition temperature (DBTT). Predictive alloy design, empowered by computational methods such as density functional theory, CALPHAD, and machine learning, may accelerate the search for crack-resistant tungsten alloys. To measure the effectiveness of predictive design, the properties of ternary W-Ta-Nb compositions were probed using a high-throughput production and testing method enabled by powder-blown laser directed energy deposition. Experimental measurements of microstructure, physical properties, and resulting cracking behavior were compared to predictions. This comparison is presented and discussed to identify values which can cross between theory and experimentation to more accurately predict alloy performance. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Additive Manufacturing, High-Temperature Materials, Computational Materials Science & Engineering |