Abstract
The paper considers the possibilities of artificial neural networks and deep machine learning in the problem of predicting the physicomechanical properties of functional materials. It is shown that the popular deep neural network VGG with high accuracy solves the problem of hardness classification of metal alloy on the basis of iron. The prospects of building a generative adversarial network that is able to predict the structure of the alloy with predetermined physicomechanical characteristics are discussed.
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Acknowledgements
The reported study was funded by the Ministry of Education and Science of the Russian Federation (the unique identifier RFMEFI58617X0055) and by the EC Horizon 2020 is MSCA-RISE-2016 FRAMED Fracture across Scales and Materials, Processes and Disciplines. The authors express their thanks to the staff of the Institute of Nanosteels of Nosov Magnitogorsk State Technical University for providing the experimental data allowed to learn the neural network with a given accuracy.
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Kliuev, A., Klestov, R., Bartolomey, M., Rogozhnikov, A. (2019). Recommendation System for Material Scientists Based on Deep Learn Neural Network. In: Antipova, T., Rocha, A. (eds) Digital Science. DSIC18 2018. Advances in Intelligent Systems and Computing, vol 850. Springer, Cham. https://doi.org/10.1007/978-3-030-02351-5_26
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DOI: https://doi.org/10.1007/978-3-030-02351-5_26
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