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Diagnostics of the Tool Condition in Metal-Cutting Machines by Means of Recurrent Neural Networks

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Abstract

A method is proposed for diagnostics of the tool condition in metal-cutting machines. This method permits determination of the tool wear on the basis of information from force and vibration sensors. To determine the dependence of the wear on the vibration and cutting force, a diagnostic model based on bidirectional recurrent neural networks with long short-term memory (bidirectional LSTM networks) is employed. The architecture of such neural networks is outlined. Several diagnostic models are developed on the basis of different network architecture, and their accuracy is compared.

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Funding

Financial support was provided by the Ministry of Education of the Russian Federation, project 0838-2020-0006 “Fundamental study of new principles for the creation of promising electromechanical energy converters with characteristics above the world level, with increased efficiency and minimum specific indicators, using new highly efficient electrotechnical materials.”

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Authors and Affiliations

  1. Ufa State Aviation Technical University, Ufa, Russia

    K. A. Masalimov, R. A. Munasypov, S. I. Fetsak & R. G. Kudoyarov

Authors
  1. K. A. Masalimov
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  2. R. A. Munasypov
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  3. S. I. Fetsak
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  4. R. G. Kudoyarov
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Corresponding authors

Correspondence to K. A. Masalimov, R. A. Munasypov, S. I. Fetsak or R. G. Kudoyarov.

Additional information

Translated by B. Gilbert

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Masalimov, K.A., Munasypov, R.A., Fetsak, S.I. et al. Diagnostics of the Tool Condition in Metal-Cutting Machines by Means of Recurrent Neural Networks. Russ. Engin. Res. 41, 252–256 (2021). https://doi.org/10.3103/S1068798X21030102

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  • DOI: https://doi.org/10.3103/S1068798X21030102

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