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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,Tbreak, which serves as an upper boundary for operating conditions, ensuring acceptable mechanical performance. In contrast to conventional black‐box approaches, our model is based on the underlying fundamental physics built directly into the model. A technique of global optimization, one allowing the concurrent optimization of model parameters over the low‐ and high‐temperature regimes, is presented. The results presented extend previous work on high‐entropy alloys (HEAs) and offer further support for the bilinear log model and its applicability for modeling the temperature‐dependent strength behavior of superalloys as well as HEAs.
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Predicting temperature-dependent ultimate strengths of body-centered-cubic (BCC) high-entropy alloys
Abstract 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,Tbreak, introduced in the model, an important parameter for design of materials with attractive high-temperature properties, one warranting inclusion in alloy specifications. For reliable operation, the operating temperature of alloys may need to stay belowTbreak. We introduce a technique of global optimization, one enabling concurrent optimization of model parameters over low-temperature and high-temperature regimes. Furthermore, we suggest a general framework for joint optimization of alloy properties, capable of accounting for physics-based dependencies, and show how a special case can be formulated to address the identification of HEAs offering attractive ultimate strength. We advocate for the selection of an optimization technique suitable for the problem at hand and the data available, and for properly accounting for the underlying sources of variations.
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- Award ID(s):
- 1809640
- PAR ID:
- 10305336
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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