| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advances in Multi-Principal Element Alloys IV: Mechanical Behavior
|
| Presentation Title |
A Machine Learning Approach for the Prediction of Formability and Thermodynamic Stability of Refractory Compositionally Complex Alloy Containing Mo and W |
| Author(s) |
Carla Joyce C. Nocheseda, Tao Liang, Haixuan Xu, Eric A. Lass |
| On-Site Speaker (Planned) |
Carla Joyce C. Nocheseda |
| Abstract Scope |
Body-centered cubic (BCC) refractory compositionally complex alloys (R-CCAs) offer considerable promise as a next generation high-temperature material, but their elevated temperature strength retention is not well understood, and they often suffer from poor room temperature ductility. Here, a machine learning algorithm is employed to screen for R-CCAs containing Molybdenum and Tungsten that have a promising balance of room temperature strength and ductility as well as elevated temperature strength retention. The resulting quaternary systems MoNbTiW and MoTaTiW are comprised of elements that combines previously reportedly or known ductile R-CCAs and those exhibiting high-temperature strength retention. These R-CCAs are then characterized by scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), nanoindentation, elevated temperature Profilometry-based Indentation Plastometry (PIP), and uniaxial tensile test. Their room and elevated temperature mechanical properties and deformation behavior are quantified and compared with previously reported alloys such as HfNbTaTiZr and MoNbTaW. |
| Proceedings Inclusion? |
Planned: |