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A. S. Smirnov, A. V. Konovalov, V. S. Kanakin


DOI: 10.17804/2410-9908.2020.6.010-026

The paper deals with a neural network to model the flow stress of the AlMg6 alloy at temperatures ranging between 300 and 500 °C and strain rates from 1 to 25 s−1. In this temperature–strain-rate range, the movement of free dislocations is blocked and dynamic relaxation processes are inhibited. The results of training the neural network and its verification at a temperature not used in the training show that neural networks with a single hidden layer can correctly approximate and predict the rheological behavior of the AlMg6 alloy for the studied temperature–strain-rate range of deformation.

Acknowledgments: The work was financially supported by the RFBR, grant 19-08-00765 (modeling the rheological behavior of materials); it was also performed as part of the research program of the Institute of Engineering Science, UB RAS, project AAAA-A18-118020790140-5, (studying the rheological behavior of the alMg6 alloy).

Keywords: neural network, flow stress, high temperature, aluminum alloy, AlMg6, barrier effect


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Article reference

Smirnov A. S., Konovalov A. V., Kanakin V. S. Neural Network Modeling of the Rheology of the Almg6 Alloy under the Dispersoid Barrier Effect and the Inhibition of Dynamic Relaxation Processes // Diagnostics, Resource and Mechanics of materials and structures. - 2020. - Iss. 6. - P. 10-26. -
DOI: 10.17804/2410-9908.2020.6.010-026. -
URL: http://eng.dream-journal.org/issues/content/article_309.html
(accessed: 05/22/2024).


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Founder:  Institute of Engineering Science, Russian Academy of Sciences (Ural Branch)
Chief Editor:  S.V. Smirnov
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