The Distribution Pattern of Machining Errors on Woodworking Machine Tools

Author(s): Pylypchuk M. I.1, Dziuba L. F.2, Mayevskyy V. O.1, Kopynets Z. P.1, Taras V. I.1

Affiliation(s):
1 Ukrainian National Forestry University, 103, Chuprynky St., 79057 Lviv, Ukraine;
2 Lviv State University of Life Safety, 35, Kleparivska St., 79000 Lviv, Ukraine

*Corresponding Author’s Address: [email protected]

Issue: Volume 10, Issue 2 (2023)

Dates:
Submitted: June 8, 2023
Received in revised form: September 29, 2023
Accepted for publication: October 9, 2023
Available online: October 12, 2023

Citation:
Pylypchuk M. I., Dziuba L. F., Mayevskyy V. O., Kopynets Z. P., Taras V. I. (2023). The distribution pattern of machining errors on woodworking machine tools. Journal of Engineering Sciences (Ukraine), Vol. 10(2), pp. A34–A42. DOI: 10.21272/jes.2023.10(2).a5

DOI: 10.21272/jes.2023.10(2).a5

Research Area:  MANUFACTURING ENGINEERING: Machines and Tools

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.

Keywords: machining accuracy, process innovation, statistical approach, distribution law, machine operability.

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