Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation
Deep Learning-Based Hardness Prediction of Novel Refractory High-Entropy Alloys with Experimental Validation
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DOI：
10.3390/cryst11010046
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发表时间：
2021-01-01
期刊：
影响因子：
2.7
通讯作者：
Yang, Shizhong
中科院分区：
文献类型：
--
作者：
Bhandari, Uttam;Zhang, Congyan;Yang, Shizhong
硬度是耐火高熵合金（RHEAs）设计中的一个基本特性。这项研究显示了如何神经网络（NN）模型可以用来预测的RHEA的硬度，为第一次。我们预测了几种合金的硬度，包括新的C0.1Cr3Mo11.9Nb20Re15Ta30W20使用NN模型。神经网络模型预测的硬度与实验结果相吻合。通过对C0.1Cr3Mo11.9Nb20Re15Ta30W20钢的实验合成和组织性能及硬度的研究，验证了神经网络模型预测的正确性。该模型提供了一种替代途径来确定RHEAs的维氏硬度。
Hardness is an essential property in the design of refractory high entropy alloys (RHEAs). This study shows how a neural network (NN) model can be used to predict the hardness of a RHEA, for the first time. We predicted the hardness of several alloys, including the novel C0.1Cr3Mo11.9Nb20Re15Ta30W20 using the NN model. The hardness predicted from the NN model was consistent with the available experimental results. The NN model prediction of C0.1Cr3Mo11.9Nb20Re15Ta30W20 was verified by experimentally synthesizing and investigating its microstructure properties and hardness. This model provides an alternative route to determine the Vickers hardness of RHEAs.