Prediction of the plasticity of high-entropic alloys
DOI:
https://doi.org/10.52575/2687-0959-2022-54-4-271-276Keywords:
Machine Learning, High-Entropy Alloys, Plasticity, CompressionAbstract
Based on a database of 153 alloys, a surrogate model was trained using machine learning approaches to predict compressive strain-to-fracture in high-entropy alloys. As part of the work, the accuracy of the impact of the architecture of a fully connected artificial neural network (the number of hidden layers and the number of neurons in hidden layers) on the prediction accuracy was evaluated. It was shown that with an increase in the number of hidden layers, the absolute error decreases - from 5.4% for a single-connected neural network to 4.8% for a two-layer and 4.7% for a three-layer neural network.
Acknowledgements
The research was carried out with the financial support of the Ministry of Science and Higher Education of the Russian Federation under an agreement dated June 24, 2021. No. 075-11-2021-046 (IGK 000000S407521QLP0002) with JSC "SEZ "VladMiVa"under the complex project "Organization of high-tech production of export-oriented medical devices based on innovative structural materials for the purpose of import substitution based on developed technologies"with the participation of NRU "BelGU"in part performing research, development and technological work using the equipment of the Center for Collective Use "Technologies and Materials of the National Research University"BelSU ". The work was partially performed (computer time) with the support of the Russian Foundation for Basic Research project No. 20-53-56063.
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