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Article

Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System

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Laboratory of Bulk Nanostructured Materials, Belgorod State University, 308015 Belgorod, Russia
2
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China
*
Author to whom correspondence should be addressed.
Academic Editor: Adam Grajcar
Materials 2021, 14(23), 7213; https://doi.org/10.3390/ma14237213
Received: 4 November 2021 / Revised: 19 November 2021 / Accepted: 22 November 2021 / Published: 26 November 2021
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. The yield strength prediction in a wide range of temperatures (20–800 °C) was made using a surrogate model based on a support-vector machine algorithm. The yield strength at 20 °C and 600 °C was predicted quite precisely (the average prediction error was 11% and 13.5%, respectively) with a decrease in the precision to slightly higher than 20% at 800 °C. An Al13Cr12Nb20Ti20V35 alloy with an excellent combination of ductility and yield strength at 20 °C (16.6% and 1295 MPa, respectively) and at 800 °C (more 50% and 898 MPa, respectively) was produced based on the prediction. View Full-Text
Keywords: high-entropy alloys; machine learning; prediction; strength; structure high-entropy alloys; machine learning; prediction; strength; structure
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MDPI and ACS Style

Klimenko, D.; Stepanov, N.; Li, J.; Fang, Q.; Zherebtsov, S. Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System. Materials 2021, 14, 7213. https://doi.org/10.3390/ma14237213

AMA Style

Klimenko D, Stepanov N, Li J, Fang Q, Zherebtsov S. Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System. Materials. 2021; 14(23):7213. https://doi.org/10.3390/ma14237213

Chicago/Turabian Style

Klimenko, Denis, Nikita Stepanov, Jia Li, Qihong Fang, and Sergey Zherebtsov. 2021. "Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System" Materials 14, no. 23: 7213. https://doi.org/10.3390/ma14237213

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