Machine Learning-Based Strength Prediction for Refractory High-Entropy Alloys of the Al-Cr-Nb-Ti-V-Zr System
1
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
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
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 StyleKlimenko, 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
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
