Bauman Moscow State Technical University, Moscow, Russia
E. N. Malyshev, Cand. Eng., Associate Prof., Head of Dept., e-mail: malen@bmstu.ru

Kaluga State University named after K. E. Tsiolkovski, Kaluga, Russia
E. A. Loshkareva, Cand. Eng., Associate Prof.

Abstract: The article presents a statistical analysis of the disc cutters time between failures with cermet teeth when cutting rolled steel 45. It is indicated that the actual operating time between failures tool depends on the parameters of the technological system elements, and the operating time values variability is a characteristic of the parameters values variability and the cutting process values variability. A cumulative approach to data collection and processing is proposed to analyze the tool actual operating time. This approach allows us to quickly draw adequate conclusions about the nature and parameters of these data values distributions, without waiting for the final formation of pre-assigned volumes samples. The proposed approach is based on the phenomenon of statistical stability, when the statistics values become predictable as and during tests that are repeated many times under unchanged conditions. Statistical processing of practical data on the actual operating time between failures is performed on the basis of Gauss and Weibull distributions. It is shown that the values of various distributions characteristics acquire stability with different intensities. The example shows that models based on the Weibull distribution can adequately describe the metal-cutting tool time between failures variability with almost two times less data than models based on the Gauss distribution, even if the normal distribution of the analyzed data general population is known a priori. It is indicated that with this approach, the fastest possible receipt of adequate information about the actual operating tool time allows you to quickly take organizational and technological measures to prevent and/or reduce possible errors and losses.

1. Antsev A. V., Pasko N. I., Antseva N. V. Optimization of cutting speed and tool replacement in the processing of ferrous metals,, taking into account the spread of tool lifetime. Chernye Metally. 2019. No. 5. pp. 41–46.
2. Yang Z., Changfu L., Xinli Y., Yu Q. Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: a review. SN Applied Sciences. 2022. Vol. 4. pp. 1–24.
3. Equeter L., Ducobu F., Dehombreux P. Cutting tools replacement: toward a holistic framework. IFAC-PapersOnLine. 2020. Vol. 53. pp. 227–232.
4. Lu B., Luo Y. A dynamic condition-based maintenance policy for heterogeneous-wearing tools with considering product quality deterioration. International Journal of Production Research. 2024. Vol. 62, Iss. 19. pp. 7096–7113.
5. Protasyev V. B., Plakhotnikova E. V., Litvinova I. V. Assessment technique for the state of production systems for the signal/noise criterion on the example of manufacturing technological processes from bar billets. Chernye Metally. 2018. No. 6. pp. 20–25.
6. Muratov K. R. Influence of rigid and frictional kinematical connection in the tool-part contact on the uniformity of tool wear. STIN. 2015. No. 9. pp. 23–26.
7. Utkin E. F. Evaluation of the influence of deformation processes in contact zones of processed and tool materials on the wear of cutting tools. Izvestiya Volgogradskogo gosudarstvennogo tekhnicheskogo universiteta. 2007. No. 3. Vol. 1. pp. 132–134.
8. Kushner V. S., Zhavnerov A. N., Udodova A. V. Improving the cutting properties of the tool when machining heat-resistant alloys. Omskiy nauchny vestnik. 2011. No. 2. pp. 20–23.
9. Makarenko K. V., Tolstyakov A. N. Study of the durability of indexable throwaway inserts during turning of heat-hardened steel 40Kh2N2MA. Vestnik Bryanskogo gosudarstvennogo tekhnicheskogo universiteta. 2018. No. 6. pp. 11–15.
10. Vereshchaka A. A., Khozhaev O. Improving the performance characteristics of tools made of tungsten-free hard alloys using nanostructured multilayer composite coatings. Vestnik Bryanskogo gosudarstvennogo tekhnicheskogo universiteta. 2014. No. 3. pp. 20–25.
11. Leskov A. V., Vlasov V. V. Mathematical statistics in the technology of production of engineering products: tutorial. Chita : Transbaikal State University, 2023. 119 p.
12. Pasko N. I., Antsev A. V., Antseva N. V., Salnikov S. V. Complex model of cutting tool wear and an example of its application for optimization of the preventive maintenance mode. Izvestiya TulGU. Technicheskie nauki. 2015. No. 11-1. pp. 192–202.
13. Baranov A. N., Baranova E. M. Methodology for accounting for physical wear of equipment in the process of quality control of articles at the stage of their production. Izvestiya TulGU. Technicheskie nauki. 2017. No. 10. pp. 118–126.
14. Said C., Xi K., Kircalial A., Hari R. et al. The application of statistical quality control methods in predictive maintenance 4.0: an unconventional use of statistical process control (SPC) charts in health monitoring and predictive analytics. Advances in Asset Management and Condition Monitoring. Smart Innovation, Systems and Technologies. 2020. Vol. 166. pp. 1–13.
15. Lara de Leon M. A., Kolarik J., Byrtus R. Tool condition monitoring methods applicable in the metalworking process. Archives of Computational Methods in Engineering. 2023. Vol. 31. pp. 221–242.
16. Umerov E. D. Statistical studies of the durability of cutting tools when drilling structural steels. Vestnik sovremennykh tekhnologiy. 2023. No. 3. pp. 8–16.
17. Colantonio L., Equeter L., Dehombreux P., Ducobu F. A systematic literature review of cutting tool wear monitoring in turning by using artificial intelligence techniques. Machines. 2021. Vol. 9. pp. 1–54.
18. Korn G. A., Korn T. M. Mathematical handbook for scientists and engineers: definitions, theorems, and formulas for reference and review. Mineola, New York : Dover Publications, Inc., 2000. 1130 p.
19. Zaretalab A., Sharifi M., Taghipour S. Machining condition-based stochastic modeling of cutting tool’s life. The International Journal of Advanced Manufacturing Technology. 2020. Vol. 111. pp. 1–15.
20. Zaretalab A., Haghighi S., Mansour S., Sajadieh M. An integrated stochastic model to optimize the machining condition and tool maintenance policy in the multi-pass and multi-stage machining. International Journal of Computer Integrated Manufacturing. 2020. Vol. 33. pp. 1–18.
21. GOST R 50779.27–2017. Statistical methods. Weibull distribution. Data analysis. Introduced: 01.12.2018.
22. Albassam M., Ahsan-ul-Haq M., Aslam M. Weibull distribution under indeterminacy with applications. AIMS Mathematics. 2023. Vol. 8, Iss. 5. pp. 10745–10757.