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Effectiveness of Neural Networks in Predicting Machine-Tool Temperatures

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Abstract

The effectiveness of neural networks in predicting the temperature of key heat sources and various machine tool components is investigated. The accuracy of prediction is assessed experimentally. Two network architectures are compared. The results of a machine experiment confirm that thermal models are effective in terms of accurate forecasts.

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Funding

The work was supported by the Russian Foundation for Basic Research, project 20-38-90045.

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Authors and Affiliations

  1. Orenburg State University, 460018, Orenburg, Russia

    V. V. Pozevalkin & A. N. Polyakov

Authors
  1. V. V. Pozevalkin
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  2. A. N. Polyakov
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Corresponding authors

Correspondence to V. V. Pozevalkin or A. N. Polyakov.

Additional information

Translated by B. Gilbert

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Pozevalkin, V.V., Polyakov, A.N. Effectiveness of Neural Networks in Predicting Machine-Tool Temperatures. Russ. Engin. Res. 41, 1260–1262 (2021). https://doi.org/10.3103/S1068798X21120340

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  • DOI: https://doi.org/10.3103/S1068798X21120340

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