OBRABOTKAMETALLOV Vol. 26 No. 3 2024 133 EQUIPMENT. INSTRUMENTS 25. Zakovorotnyi V.L., Gvindjiliya V.E. Evolution of the dynamic cutting system with irreversible energy transformation in the machining zone. Russian Engineering Research, 2019, vol. 39 (5), pp. 423–430. DOI: 10.3103/ S1068798X19050204. 26. Lee D.E., Hwang I., Valente C.M.O., Oliveira J.F.G., Dornfeld D.A. Precision manufacturing process monitoring with acoustic emission. International Journal of Machine Tools and Manufacture, 2006, vol. 46 (2), pp. 176–188. DOI: 10.1016/ j.ijmachtools.2005.04.001. 27. Liu Z., Lang Z.-Q., Gui Y., Zhu Y.-P., Laalej H. Digital twin-based anomaly detection for real-time tool condition monitoring in machining. Journal of Manufacturing Systems, 1995, vol. 75, pp. 163–173. DOI: 10.1016/j. jmsy.2024.06.004. 28. Dimla D.E. Sensor signals for tool-wear monitoring in metal cutting operations – a review of methods. International Journal of Machine Tools and Manufacture, 2000, vol. 40 (8), pp. 1073–1098. DOI: 10.1016/S08906955(99)00122-4. 29. Choi Y., Narayanaswami R., Chandra A. Tool wear monitoring in ramp cuts in end milling using the wavelet transform. International Journal of Advanced Manufacturing Technology, 2004, vol. 23 (5–6), pp. 419–428. DOI: 10.1007/s00170-003-1898-1. 30. Grigoriev A.S. Instrumentarii sistemy ChPU dlya diagnostiki i prognozirovaniya iznosa rezhushchego instrumenta v real’nom vremeni pri tokarnoi obrabotke [CNC tool for diagnostic and prediction of cutting tool wear in real time for turning processing]. Vestnik MGTU «Stankin» = Vestnik MSUT “Stankin”, 2012, no. 1 (18), pp. 39–43. 31. Zakovorotny V.L., Bordachev E.V. Informatsionnoe obespechenie sistemy dinamicheskoi diagnostiki iznosa rezhushchego instrumenta na primere tokarnoi obrabotki [Information support for the system of dynamic diagnostics of cutting tool wear using the example of turning]. Problemy mashinostroeniya i nadezhnosti mashin = Journal of Machinery Manufacture and Reliability, 1995, no. 3, pp. 95–103. 32. Dolinšek S., Kopac J. Acoustic emission signals for tool wear identifi cation. Wear, 1999, vol. 225, pp. 295–303. 33. Chiou R.Y., Liang S.Y. Analysis of acoustic emission in chatter vibration with tool wear eff ect in turning. International Journal of Machine Tools and Manufacture, 2000, vol. 40 (7), pp. 927–941. DOI: 10.1016/S08906955(99)00093-0. 34. Bhuiyan M.S.H., Choudhury I.A., Dahari M., Nukman Y., Dawal S.Z. Application of acoustic emission sensor to investigate the frequency of tool wear and plastic deformation in tool condition monitoring. Measurement, 2016, vol. 92, pp. 208–217. DOI: 10.1016/ j.measurement.2016.06.006. 35. Scheff er C., Kratz H., Heyns P., Klocke F. Development of a tool wear-monitoring system for hard turning. International Journal of Machine Tools and Manufacture, 2003, vol. 43 (10), pp. 973–985. DOI: 10.1016/S08906955(03)00110-X. 36. Siddhpura A., Paurobally R. A review of fl ank wear prediction methods for tool condition monitoring in a turning process. International Journal of Advanced Manufacturing Technology, 2013, vol. 65, pp. 371–393. DOI: 10.1007/s00170-012-4177-1. 37. Karandikar J., McLeay T., Turner S., Schmitz T. Tool wear monitoring using naive Bayes classifi ers. International Journal of Advanced Manufacturing Technology, 2014, vol. 77, pp. 1613–1626. DOI: 10.1007/s00170014-6560-6. 38. Azmi A. Monitoring of tool wear using measured machining forces and neuro-fuzzy modeling approaches during machining of GFRP composites. Advances in Engineering Software, 2015, vol. 82, pp. 53–64. DOI: 10.1016/ j.advengsoft.2014.12.010. 39. Kene A.P., Choudhury S.K. Analytical modeling of tool health monitoring system using multiple sensor data fusion approach in hard machining. Measurement, 2019, vol. 145, pp. 118–129. DOI: 10.1016/j. measurement.2019.05.062. 40. Chethan Y., Ravindra H., Krishnegowda Y. Optimization of machining parameters in turning Nimonic-75 using machine vision and acoustic emission signals by Taguchi technique. Measurement, 2019, vol. 144, pp. 144– 154. DOI: 10.1016/j.measurement.2019.05.035. 41. AslanA. Optimization and analysis of process parameters for fl ank wear, cutting forces and vibration in turning of AISI 5140: A comprehensive study. Measurement, 2020, vol. 163. DOI: 10.1016/j.measurement.2020.107959. 42. Mohanraj T., Shankar S., Rajasekar R., Sakthivel N., Pramanik A. Tool condition monitoring techniques in milling process – a review. Journal of Materials Research and Technology, 2019, vol. 9 (1), pp. 1032–1042. DOI: 10.1016/ j.jmrt.2019.10.031.
RkJQdWJsaXNoZXIy MTk0ODM1