Klimenko Denis, Stepanov Nikita, Li Jia, Fang Qihong, Zherebtsov Sergey
Laboratory of Bulk Nanostructured Materials, Belgorod State University, 308015 Belgorod, Russia.
State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082, China.
Materials (Basel). 2021 Nov 26;14(23):7213. doi: 10.3390/ma14237213.
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 AlCrNbTiV 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.
这项工作的目的是为高性能高熵合金的预测和设计提供指导。结合机器学习、唯象规则和CALPHAD建模，预测了具有所需相组成和力学性能（屈服强度）的新型难熔高熵合金的有前景的成分。使用基于支持向量机算法的替代模型对宽温度范围（20 - 800°C）内的屈服强度进行了预测。20°C和600°C时的屈服强度预测相当精确（平均预测误差分别为11%和13.5%），800°C时精度有所下降，略高于20%。基于该预测制备了一种AlCrNbTiV合金，其在20°C时具有优异的延展性和屈服强度组合（分别为16.6%和1295 MPa），在800°C时也具有良好表现（分别为超过50%和898 MPa）。
