The Distribution Pattern of Machining Errors on Woodworking Machine Tools

Author:

Pylypchuk Maria1ORCID,Dziuba Lidiia2ORCID,Mayevskyy Volodymyr1ORCID,Kopynets Zoya1ORCID,Taras V. I.3

Affiliation:

1. Ukrainian National Forestry University, Lviv, Ukraine

2. Lviv State University of Life Safety, Lviv, Ukraine

3. Ukrainian National Forestry University, Lviv, Ukraine

Abstract

The article aims to develop a methodology for calculating and predicting the distribution patterns of wood machining errors to assess the operating conditions of the machine tool according to the technological accuracy criterion. It was analytically proven and experimentally confirmed that Weibull’s law accurately describes the distribution pattern of machining errors on woodworking machines. Based on the results of experimental studies of the accuracy of machining on machines for lengthwise sawing and plano-milling of wood, it was found that the primary indicator of the Weibull distribution law is a shape parameter that takes values within 1.89–3.11. The computational algorithm was developed for statistical modeling of the pattern of the distribution of machining errors according to the Weibull distribution law. It allows for determining the main parameters of the error distribution law and evaluating the operating conditions for the machine tool according to the technological accuracy criterion. The statistical modeling results for the distribution pattern of machining errors are correlated with the experimental data with an accuracy of up to 5 %, which confirms the reliability of the obtained simulation results. The developed approach also minimizes the restoration cost for the machine’s operability.

Publisher

Sumy State University

Subject

Ocean Engineering

Reference24 articles.

1. Kiyko, O. A., Yakuba, M. M., Voytovich, I. G., Shulte, A., Kies, U., Klein, D. (2009). Cluster analysis of Ukrainian forest complex development prospect’s. Scientific Bulletin of UNFU, Vol. 19(9), pp. 20–28.

2. Pylypchuk, M., Mayevskyy, V., Taras, V., Burdiak, М., Kopynets, Z. (2023). Patterns of change in the accuracy of wood machining on plano-milling machines during the period of the cutting tool wear resistance. Acta Facultatis Xylologiae Zvolen, Vol. 65(1), pp. 5–21. https://doi.org/10.17423/afx.2023.65.1.01

3. Quintana, G., Ciurana, J. (2011). Chatter in machining processes: A review. International Journal of Machine Tools and Manufacture, Vol. 51(5), pp. 363–376. https://doi.org/10.1016/j.ijmachtools.2011.01.001

4. Swamidass, P. M. (2020). Encyclopedia of Production and Manufacturing Management. Springer, Boston, MA, USA. https://doi.org/10.1007/1-4020-0612-8_531

5. Fernandes, M., Corchado, J. M., Marreiros, G. (2022). Machine learning techniques applied to mechanical fault diagnosis and fault prognosis in the context of real industrial manufacturing use-cases: a systematic literature review. Appl Intell, Vol. 52, pp. 14246–14280. https://doi.org/10.1007/s10489-022-03344-3

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