Deep learning-based hardness prediction of novel refractory high-entropy alloys with experimental validation

Authors

Uttam Bhandari, Southern University and A&M College
Congyan Zhang, Southern University and A&M College
Congyuan Zeng, LSU College of Engineering
Shengmin Guo, LSU College of Engineering
Aashish Adhikari, College of Engineering
Shizhong Yang, Southern University and A&M College

Document Type

Article

Publication Date

1-1-2021

Abstract

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 C0.1Cr3Mo11.9Nb20 Re15Ta30W20 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.

Publication Source (Journal or Book title)

Crystals

First Page

1

Last Page

14

Recommended Citation

Bhandari, U., Zhang, C., Zeng, C., Guo, S., Adhikari, A., & Yang, S. (2021). Deep learning-based hardness prediction of novel refractory high-entropy alloys with experimental validation. Crystals, 11 (1), 1-14. https://doi.org/10.3390/cryst11010046