Real-Time Diagnostics of Metal-Cutting Machines by Means of Recurrent LSTM Neural Networks

Real-time diagnostics of modules in metal-cutting machines may be based on neural-network algorithms for simulation of the standard process, identification of defects, and the introduction of corrections in the cutting machine’s control system. The machining conditions in normal operation of the mac...

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Bibliographic Details
Published inRussian engineering research Vol. 40; no. 5; pp. 416 - 421
Main Authors Munasypov, R. A., Idrisova, Yu. V., Masalimov, K. A., Kudoyarov, R. G., Fetsak, S. I.
Format Journal Article
LanguageEnglish
Published Moscow Pleiades Publishing 2020
Springer Nature B.V
Subjects
Algorithms
Computer simulation
Cutting equipment
Engineering
Engineering Design
Machine tools
Machining
Modules
Neural networks
Real time operation
real-time diagnostics
short-term memory
metal-cutting machines
LSTM networks
neural networks
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Summary:Real-time diagnostics of modules in metal-cutting machines may be based on neural-network algorithms for simulation of the standard process, identification of defects, and the introduction of corrections in the cutting machine’s control system. The machining conditions in normal operation of the machine are recorded by means of a trained neural network with long short-term memory (LSTM network). In real-time operation, the difference between the standard neural-network model and the actual process characteristics is used to determine the type of defect and the module of the machine where it occurs on the basis of a second neural network, the classification unit.
ISSN:1068-798X
1934-8088
DOI:10.3103/S1068798X20050160