http://dspace.bsu.edu.ru/handle/123456789/62461| Title: | Machine learning-based strength prediction for refractory high-entropy alloys of the Al-Cr-Nb-Ti-V-Zr system |
| Authors: | Klimenko, D. Stepanov, N. Jia Li Qihong Fang Zherebtsov, S. V. |
| Keywords: | technique metal science alloys high entropy alloys machine learning prediction strength structure |
| Issue Date: | 2021 |
| Citation: | Machine learning-based strength prediction for refractory high-entropy alloys of the Al-Cr-Nb-Ti-V-Zr system / D. Klimenko, N. Stepanov, Jia Li [et al.] // Materials. - 2021. - Vol.14, №3.-Art. 7213. |
| Abstract: | The aim of this work was to provide a guidance to the prediction and design of high-entropy alloys with good performance. New promising compositions of refractory high-entropy alloys with the desired phase composition and mechanical properties (yield strength) have been predicted using a combination of machine learning, phenomenological rules and CALPHAD modeling |
| URI: | http://dspace.bsu.edu.ru/handle/123456789/62461 |
| Appears in Collections: | Статьи из периодических изданий и сборников (на иностранных языках) = Articles from periodicals and collections (in foreign languages) |
| File | Description | Size | Format | |
|---|---|---|---|---|
| Klimenko_Machine Learning-Based_2021.pdf | 1.02 MB | Adobe PDF | View/Open |
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