Multi-label phase-prediction in high-entropy-alloys using Artificial-Neural-Network

[Display omitted] •Artificial Neural Network predicts all the phases in a given HEA and processing route.•Alloy composition and processing route of HEA are sufficient inputs for phase-predictions.•A python programme calculates ΔSmix, ΔHmix, δ, VEC, Ω, χ and ANN uses these parameters for phase-predic...

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Bibliographic Details
Published in:Materials letters 2020-06, Vol.268, p.127606, Article 127606
Main Authors: Dixit, Shrey, Singhal, Vineet, Agarwal, Abhishek, Prasada Rao, A.K.
Format: Article
Language:English
Subjects:
Alloy development
Alloys
Computer architecture
Entropy
High entropy alloys
Materials science
Mathematical models
Model accuracy
Neural networks
Parameters
Phase-transformation
Simulation and modeling
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Summary:[Display omitted] •Artificial Neural Network predicts all the phases in a given HEA and processing route.•Alloy composition and processing route of HEA are sufficient inputs for phase-predictions.•A python programme calculates ΔSmix, ΔHmix, δ, VEC, Ω, χ and ANN uses these parameters for phase-predictions.•Present work can be used in alloy design. A novel Artificial-Neural-Network architecture has been developed, for the first time, which predicts eight coexisting-phases present in high-entropy-alloys. The model considers composition and processing-route to compute physical and thermodynamic-parameters of the alloy. The Artificial-Neural-Network uses these parameters and predicts the phases in a given alloy. Validation reveals prediction accuracy of 87.083%. This modeling technique can find potential application in designing new high-entropy-alloys and choosing their processing-routes.
ISSN:0167-577X
1873-4979
DOI:10.1016/j.matlet.2020.127606