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Neural Network Application for Phasechronometric Measurement Information Processing

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Measurement Techniques Aims and scope

This paper reports the application of neural networks in various fields of activity. In specific, it describes the use of neural networks to process phasechronometric measurement information. The novelty of the proposed approach lies in the choice of a classification attribute and the use of a perceptron algorithm for binary classification. The simplest binary classification of the lathe operating modes (idle or cutting) is presented.

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References

  1. I. S. Kabak, N. V. Sukhanova, and A. M. Gadelev, “Application of neural networks in diagnostics of the state of the cutting tool,” Izv. Kabard.-Balk. Gos. Univ., 2, No. 4, 77–79 (2012).

    Google Scholar 

  2. A. I. Azmi, Adv. Eng. Softw., 82, 53–64 (2015), https://doi.org/10.1016/j.advengsoft.2014.12.010.

    Article  Google Scholar 

  3. M. Rizal et al., Appl. Soft Comput., 13, 1960–1968 (2013), https://doi.org/10.1016/j.asoc.2012.11.043.

    Article  Google Scholar 

  4. R. G. Silva, R. L. Reuben, K. J. Baker, and S. J. Wilcox, Mech. Sys. Signal Proc., 12, 319–332 (1998), https://doi.org/10.1006/mssp.1997.0123.

    Article  Google Scholar 

  5. A. Proteau et al., Int. J. Adv. Manuf. Tech., 103, 101–110 (2019), https://doi.org/10.1007/s00170-019-03533-4.

    Article  Google Scholar 

  6. A. B. Syritskiy, “Measurement of wear of the cutting tool by the phasechronometric method during processing,” Izmer. Tekhn., No. 6, 30–32 (2016).

  7. M. I. Kiselev, “Phase-chronometry: problems and prospects,” Pribory, No. 10 (196), 51–54 (2016).

  8. D. D. Boldasov, A. S. Komshin, and A. B. Syritskii, “Method of lathe tool condition monitoring based on the phasechronometric approach,” in: Advances in Automation. RusAutoCon 2019, A. Radionov and A. Karandaev (eds.), Lecture Notes in Electrical Engineering, Springer, Cham (2020), Vol. 641, https://doi.org/10.1007/978-3-030-39225-3_82.

  9. P. Geurts and L. Wehenkel, Knowledge Discovery in Databases: PKDD 2005, Springer (2005), pp. 478–485, https://doi.org/10.1007/11564126_48.

  10. Scikit-Learn User Guide. Chapter 3. Supervised Learning. Release 0.20.3, March, 2019, https://scikit-learn.org/stable/_downloads/scikit-learn-docs.pdf, acc. 03/06/2019.

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Correspondence to D. D. Boldasov.

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Translated from Izmeritel’naya Tekhnika, No. 9, pp. 31–35, September, 2020.

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Boldasov, D.D., Drozdova, J.V., Komshin, A.S. et al. Neural Network Application for Phasechronometric Measurement Information Processing. Meas Tech 63, 708–712 (2020). https://doi.org/10.1007/s11018-021-01843-2

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  • DOI: https://doi.org/10.1007/s11018-021-01843-2

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