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

NEURAL NETWORK MODELING OF THE RHEOLOGY OF THE AlMg6 ALLOY UNDER THE DISPERSOID BARRIER EFFECT AND THE INHIBITION OF DYNAMIC RELAXATION PROCESSES

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.

Acknowledgment: 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).

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