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):
¹Ukrainian National Forestry University, 103, Chuprynky St., 79057 Lviv, Ukraine;
²Lviv 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.

References:

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.
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
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
Swamidass, P. M. (2020). Encyclopedia of Production and Manufacturing Management. Springer, Boston, MA, USA. https://doi.org/10.1007/1-4020-0612-8_531
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
Jardine, A. K. S., Lin, D., Banjevic, D. (2006). A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mechanical Systems and Signal Processing, Vol. 20(7), pp. 1483–1510. https://doi.org/10.1016/j.ymssp.2005.09.012
Jazwinski, A. H., Mineola, N. Y. (2007). Stochastic Processes and Filtering Theory. Dover Publications, Mineola, New York, United States.
Pylypchuk, M. I. (2010). Development of the research methodology of technological accuracy of woodworking machines. Agricultural Machines, Vol. 20, pp. 259–265.
Mysyk, M., Matsyshyn, Y., Mayevskyy, V., Ray, C., Kurka, R., Sopushynskyy, I. (2017). Identification of the length distribution of lumber defect-free areas. Wood and Fiber Science, Vol. 49(4), pp. 396–406.
Yusubov, N. D., Abbasova, H. M. (2020). Models for machining accuracy in multi-tool adjustment. International Journal of Automotive and Mechanical Engineering, Vol. 17(3), pp. 8067–8085. https://doi.org/10.15282/ijame.17.3.2020.01.0605
Li, G., Zhu, H., He, J., Wu, K., Jia, Y. (2019). Application of power law model in reliability evaluation of machine tools by considering working condition difference. Quality and Reliability Engineering International, Vol. 35(1), pp. 136–145. https://doi.org/10.1002/qre.2386
Pylypchuk, M., Dziuba, L., Rebezniuk, I., Chmyr, O., Burdiak, M. (2022). Modeling parametric failures of woodworking machines according to the technological precision criterion. In: Tonkonogyi, V., Ivanov, V., Trojanowska, J., Oborskyi, G., Pavlenko, I. (eds) Advanced Manufacturing Processes III. InterPartner 2021. Lecture Notes in Mechanical Engineering, pp. 119–126. Springer, Cham. https://doi.org/10.1007/978-3-030-91327-4_12
Dai, W., Chi, Y., Zhiyuan L., Wang, M., Zhao, Y. (2018). Research on reliability assessment of mechanical equipment based on the performance–feature model. Appl. Sci., Vol. 8(9), 1619. https://doi.org/10.3390/app8091619
Rajko, Ľ., Koleda, P., Barcík, Š., Koleda, P. (2021) Technical and technological factors’ effects on quality of the machined surface and energetic efficiency when planar milling heat-treated meranti wood. BioResources, Vol. 16(4), 7884. https://doi.org/10.15376/biores.16.4.7884-7900
Li, R.,Yang, F.,Wang, X. (2022). Modeling and predicting the machined surface roughness and milling power in Scot’s pine helical milling process. Machines, Vol. 10(5), 331. https://doi.org/10.3390/machines10050331
Aykac, E., Sofuoglu, S. D. (2021). Investigation of the effect of machining parameters on surface quality in bamboo. Tehnički Vjesnik, Vol. 28(2), pp. 684–688. https://doi.org/10.17559/TV-20200102202928
Djurković, M., Milosavljević, M., Mihailović, V., Danon, G. (2019). Tool wear impacts on cutting power and surface quality in peripheral wood milling. Wood Design and Technology, Vol. 8, pp. 9–17.
Keturakis, G., Bendikiene, R., Baltrusaitis, A. (2017). Tool wear evolution and surface formation in milling various wood species. BioResources, Vol. 12(4), pp. 7943–7954.
Bendikiene, R., Keturakis, G. (2016). The effect of tool wear and planning parameters on birch wood surface roughness. Wood Research, Vol. 61(5), pp. 791–798.
Kazlauskas, D., Jankauskas, V., Keturakis, G. (2019). Research on durability of AlCrN coated tungsten carbide (WC–Co) cutters during oak wood milling. Proceedings of BALTTRIB’2019, pp. 243-249. https://doi.org/10.15544/balttrib.2019.38
Koleda, P., Barcík, Š., Svoreň, J., Naščák, Ľ., Dobrík, A. (2019). Influence of cutting wedge treatment on cutting power, machined surface quality, and cutting edge wear when plane milling oak wood. BioResources, Vol. 14(4), pp. 9271–9286. https://doi.org/10.15376/biores.14.4.9271-9286
Skliarov, R. A., Prykhodai, D. A. (2021). Methods for increasing the machining accuracy of metal cutting machines. In: Proceedings of the 10th International Scientific and Practical Conference “Current Issues in Modern Technologies”, pp. 24–25. Ternopil Ivan Puluj National Technical University, Ukraine.
Rudenko, T. H., Ermolaev, Y. O., Shalimov, V. O. (2012). Development of an automated control system for wood machining on plano-milling machines. Technology in Agricultural Production, Industrial Machine Building, and Automation, Vol. 25(2), pp. 272–275.
Fang, Q., Boated, D. (2009). Monte Carlo simulation of photon migration in 3D turbid media accelerated by graphics processing units. Optics Express, Vol. 17(22), рр. 20178–20190. https://doi.org/10.1364/OE.17.020178

Full Text

This work is licensed under Creative Commons Attribution-Noncommercial 4.0 International License