About this Abstract |
Meeting |
2nd World Congress on High Entropy Alloys (HEA 2021)
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Symposium
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2nd World Congress on High Entropy Alloys (HEA 2021)
|
Presentation Title |
Accelerated Development of Refractory Multi-principal Element Alloys via Machine Learning |
Author(s) |
Carolina Frey, Christopher Borg, James Saal, Bryce Meredig, Noah Philips, Tresa Pollock |
On-Site Speaker (Planned) |
Carolina Frey |
Abstract Scope |
Refractory Multi-principal Element Alloys (RMPEAs) present an opportunity for new high temperature alloys that can operate at temperatures above 1200°C, but they remain relatively underexplored compared to other MPEA categories due to processing challenges. Machine learning methods have the potential to reduce the number of needed experiments and more efficiently discover interesting materials demonstrating both high temperature strength and room temperature ductility. This presentation will discuss the use of random forest machine learning algorithms in concert with CALPHAD and rapid processing techniques to guide sequential alloy design. Predictive models for room temperature, 1000°C and 1200°C yield strengths are presented. Hf-Mo-Nb-Ta-Ti was identified as a potentially high performing system. Compressive mechanical properties of as-cast alloys at room and high temperature in this system are reported, and the effect of iteration on model fidelity is discussed. Splat quenched foils were utilized to reduce segregation and grain size to probe potential tensile ductility. |
Proceedings Inclusion? |
Undecided |