About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
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Symposium
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Materials Genome, CALPHAD, and a Career over the Span of 20, 50, and 60 Years: An FMD/SMD Symposium in Honor of Zi-Kui Liu
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Presentation Title |
Computational Design of Novel High-Entropy Alloys: Multi-Strengthening Mechanisms vs Neural Network Model |
Author(s) |
Jaemin Wang, Hyeon-Seok Do, Byeong-Joo Lee |
On-Site Speaker (Planned) |
Byeong-Joo Lee |
Abstract Scope |
Efforts to design novel HEAs have been made using two different approaches, one based on multi-strengthening mechanisms and the other using neural network modeling. In the first approach, the strength of Co-Cr-Fe-Mn-Ni based HEA was improved via various strengthening mechanisms, such as solid solution strengthening, TRIP and precipitation hardening. We used CALPHAD-type thermodynamic calculations: calculation of phase equilibria, free energy difference between FCC and metastable solid solution phases, and precipitation kinetics. In the second approach, first we suggested a novel and robust neural network model to relieve the burden of searching vast compositional space. Then, we inverse-predicted the process condition to obtain HEAs with good mechanical properties. Finally, we conducted experimental verification on the designed HEAs to prove the validity of the model and alloy design method. The strengthening mechanism of the designed HEAs is discussed by analyzing microstructure and calculating the lattice distortion effect using a molecular dynamics simulation. |
Proceedings Inclusion? |
Planned: |
Keywords |
Computational Materials Science & Engineering, High-Entropy Alloys, Machine Learning |