| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Advances in Multi-Principal Element Alloys IV: Mechanical Behavior
|
| Presentation Title |
Unveiling Yield Strength of High Entropy Alloys Using Physics-Enhanced Machine Learning Under Diverse Experimental Conditions |
| Author(s) |
Jeong Ah Lee, Roberto B. Figueiredo, Hyojin Park, Jae Hoon Kim, Hyoungseop Kim |
| On-Site Speaker (Planned) |
Jeong Ah Lee |
| Abstract Scope |
The accurate prediction of the yield strength of metallic materials with diverse compositions often necessitates extensive experimental endeavors, leading to inefficiencies in both time and resources. Here, we introduce an innovative approach to predict yield strength, applicable to a variety of metallic materials ranging from the simplest pure metals to compositionally complex alloys or high/medium entropy alloys under varying temperatures and strain rates. The fusion of deformation mechanisms with cutting-edge machine-learning algorithms creates an extensive framework that identifies the critical factors influencing yield strength. The validity and wide applicability of the proposed framework were rigorously confirmed through experimental evaluations conducted on selected Fe-based alloys, such as Fe60Ni25Cr15, Fe60Ni30Cr10, and Fe64Ni15Co8Mn8Cu5. This breakthrough study significantly streamlines experimental design processes, optimizes resource utilization, and marks a significant leap forward in creating a reliable predictive framework for realizing material properties of emerging alloys. |
| Proceedings Inclusion? |
Planned: |
| Keywords |
Mechanical Properties, High-Entropy Alloys, Computational Materials Science & Engineering |