OBRABOTKAMETALLOV technology Vol. 26 No. 3 2024 8) perform additional analysis of the filtered signal (Compare filtered signals with reference signals to assess the condition of the tool. Identify changes in the sound and vibration spectrum, indicating wear or damage to the tool). Conclusions Based on the current state of research described in this paper, as well as an evaluation of the results of the experiments, the following conclusions can be drawn. 1. An algorithm is developed for the operation of an Online Monitoring system for monitoring the condition of a cutting tool during milling, filtering interference and noise in real time based on the formation of DS obtained during vibroacoustic analysis. This conclusion stimulates the formulation of new tasks for research in this area. 2. Analysis of the frequency response in the range of 20÷200 Hz made it possible to establish the difference in the spectral density of the acoustic signal over time, an increase in which makes it possible to record the degree of tool wear. 3. The Hamming window function is determined to be optimal in terms of computational resources and accuracy of the acoustic signal from the standpoint of its use in the analysis of the machining process. 4. The presence of an acoustic signal correlation is confirmed by measurements of wear on the cutter radius, roughness and the results of vibration diagnostics. At the same time, the Online Monitoring system makes it possible to determine earlier signs of changes in the condition of the tool’s cutting edge than measurements recorded by a cycle in the CNC control program or measurements of roughness parameters. References 1. GOST R 56136–2014. Upravlenie zhiznennym tsiklom produktsii. Terminy i opredeleniya [State Standard R 56136–2014. Life cycle management for military products. Terms and definitions]. Moscow, Standartinform Publ., 2016. 24 p. 2. Grieves M. Digital twin: manufacturing excellence through virtual factory replication: whitepaper. Melbourne, FL, LLC, 2014, pp. 1–7. 3. GOST R 57700.37–2021. Komp’yuternye modeli i modelirovanie. Tsifrovye dvoiniki izdelii. Obshchie polozheniya [State Standard R 57700.37–2021. Computer models and simulation. Digital twins of products. General provisions]. Moscow, Russian Institute of Standardization Publ., 2021. 15 p. 4. Ingemansson A.R. Sovremennaya nauchnaya problema povysheniya effektivnosti mekhanoobrabatyvayushchego proizvodstva putem vnedreniya kiberfizicheskikh sistem v ramkakh kontseptsii «Industriya 4.0» [Current scientific problem of efficiency increase in mechanical operation by cyber-physical systems introduction within “industry 4.0” concept]. Naukoemkie tekhnologii v mashinostroenii = Science Intensive Technologies in Mechanical Engineering, 2016, no. 12, pp. 40–44. DOI: 10.12737/23487. 5. Kabaldin Y.G., Shatagin D.A., Kuzmishina A.M. Razrabotka tsifrovogo dvoinika rezhushchego instrumenta dlya mekhanoobrabatyvayushchego proizvodstva [The development of a digital twin of a cutting tool for mechanical production]. Izvestiya vysshikh uchebnykh zavedenii. Mashinostroenie = Proceedings of Higher Educational Institutions. Маchine Building, 2019, no. 4 (709), pp. 11–17. DOI: 10.18698/0536-1044-2019-4-11-17. 6. Uhlemann T.H.J., Lehmann C., Steinhilper R. The digital twin: realizing the cyber-physical production system for Industry 4.0. Procedia CIRP, 2017, vol. 61, pp. 335–340. DOI: 10.1016/j.procir.2016.11.152. 7. Lu Y., Liu C., Wang K.I.-K., Huang H., Xu X. Digital Twin–driven smart manufacturing: Connotation, reference model, applications and research issues. Robotics and Computer-Integrated Manufacturing, 2020, vol. 61, p. 101837. DOI: 10.1016/j.rcim.2019.101837. 8. Schleich B., Anwer N., Mathieu L., Wartzack S. Shaping the digital twin for design and production engineering. CIRP Annals, 2017, vol. 66, pp. 141–144. DOI: 10.1016/j.cirp.2017.04.040. 9. Padovano A., Longo F., Nicoletti L., Mirabelli G. A Digital Twin based service oriented application for a 4.0 knowledge navigation in the smart factory. IFAC-PapersOnLine, 2018, vol. 51 (11), pp. 631–636. DOI: 10.1016/ j.ifacol.2018.08.389. 10. Tao F., Anwer N., Liu A., Wang L., Nee A.Y.C., Li L., Zhang M. Digital twin towards smart manufacturing and industry 4.0. Journal of Manufacturing Systems, 2021, vol. 58 (B), pp. 1–2. DOI: 10.1016/j.jmsy.2020.12.005.
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