This paper presents a bilinear log model, for predicting temperature-dependent ultimate strength of high-entropy alloys (HEAs) based on 21 HEA compositions. We consider the break temperature,
In the pursuit of developing high‐temperature alloys with improved properties for meeting the performance requirements of next‐generation energy and aerospace demands, integrated computational materials engineering has played a crucial role. Herein, a machine learning approach is presented, capable of predicting the temperature‐dependent yield strengths of superalloys utilizing a bilinear log model. Importantly, the model introduces the parameter break temperature,
- Award ID(s):
- 2226495
- NSF-PAR ID:
- 10432921
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Advanced Engineering Materials
- Volume:
- 25
- Issue:
- 14
- ISSN:
- 1438-1656
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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