Journal
CRYSTALS
Volume 11, Issue 1, Pages -Publisher
MDPI
DOI: 10.3390/cryst11010046
Keywords
high entropy alloys; neural networks; hardness-prediction; microstructure
Funding
- NSF EPSCoR CIMM project [1541079]
- DOE award [DE-NA0003979]
- DoD [W911NF1910005]
- US National Science Foundation [OIA-1946231]
- Louisiana Board of Regent
- Louisiana Optical Network Infrastructure (LONI)
- U.S. Department of Defense (DOD) [W911NF1910005] Funding Source: U.S. Department of Defense (DOD)
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A neural network model is used for the first time to predict the hardness of refractory high entropy alloys (RHEAs), showing good consistency with experimental results and providing an alternative route to determine the Vickers hardness of alloys.
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.
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