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Diagnostics of 3D Printing on a CNC Machine by Machine Learning

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Russian Engineering Research Aims and scope

Abstract

Diagnostics and dynamic optimization of the electrical arc in 3D printing on a CNC machine are considered. The applicability of nonlinear dynamic methods in assessing the stability of 3D printing is assessed. Artificial neural networks are used in classifying and optimizing the process parameters.

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Funding

Financial support was provided by the Russian President as encouragement to young scientists (grant 075-15-2020-098).

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Authors and Affiliations

  1. Alekseev Nizhny Novgorod State Technical University, Nizhny Novgorod, Russia

    Yu. G. Kabaldin, D. A. Shatagin, M. S. Anosov, P. V. Kolchin & A. V. Kiselev

Authors
  1. Yu. G. Kabaldin
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  2. D. A. Shatagin
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  3. M. S. Anosov
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  4. P. V. Kolchin
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  5. A. V. Kiselev
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Corresponding author

Correspondence to Yu. G. Kabaldin.

Additional information

Translated by B. Gilbert

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Cite this article

Kabaldin, Y.G., Shatagin, D.A., Anosov, M.S. et al. Diagnostics of 3D Printing on a CNC Machine by Machine Learning. Russ. Engin. Res. 41, 320–324 (2021). https://doi.org/10.3103/S1068798X21040109

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  • DOI: https://doi.org/10.3103/S1068798X21040109

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