Obrabotka Metallov 2022 Vol. 24 No. 4

OBRABOTKAMETALLOV Vol. 24 No. 4 2022 82 EQUIPMENT. INSTRUMENTS 3. Gholizadeh S., Leman Z., Baharudin B.T.H.T. A review of the application of acoustic emission technique in engineering. Structural Engineering and Mechanics, 2015, vol. 54, iss. 6, pp. 1075–1095. DOI: 10.12989/ sem.2015.54.6.1075. 4. Lu Z.-J., Xiang Q., Xu L. An application case study on multi-sensor data fusion system for intelligent process monitoring. Procedia CIRP, 2014, vol. 17, pp. 721–725. DOI: 10.1016/j.procir.2014.01.122. 5. Gao Z., Lin J., Wang X., Liao Y. Grinding burn detection based on cross wavelet and wavelet coherence analysis by acoustic emission signal. Chinese Journal of Mechanical Engineering, 2019, vol. 32, iss. 68, pp. 1–10. DOI: 10.1186/s10033-019-0384-0. 6. Lee C.H., Jwo J.S., Hsieh H.Y., Lin C.S. An intelligent system for grinding wheel condition monitoring based on machining sound and deep learning. IEEE Access, 2020, vol. 8, pp. 58279–58289. DOI: 10.1109/ ACCESS.2020.2982800. 7. Hosokawa A., Mashimo K., Yamada K., Ueda T. Evaluation of grinding wheel surface by means of grinding sound discrimination. JSME International Journal. Series C, Mechanical Systems, Machine Elements and Manufacturing, 2004, vol. 47, iss. 1, pp. 52–58. 8. Nourizadeh R., Rezaei S.M., Zareinejad M., Adibi H. Comprehensive investigation on sound generation mechanisms during machining for monitoring purpose. The International Journal of Advanced Manufacturing Technology, 2022, vol. 121, iss. 1, pp. 1598–1610. DOI: 10.1007/s00170-022-09333-7. 9. Li X. A brief review: acoustic emission method for tool wear monitoring during turning. International Journal of Machine Tools and Manufacture, 2002, vol. 42, pp. 157–165. 10. Asadi R.,AnahidM.J., Heydarnia H., Mehmanparast H., NiknamS.A. The use of wavelet transform to evaluate the sensitivity of AE attributes to variation of cutting parameters in milling aluminium alloys. The International Journal of Advanced Manufacturing Technology, 2021, vol. 1, pp. 1–14. DOI: 10.21203/rs.3.rs-1054589/v1. 11. Gomes M.C., Brito L.C., Silva M. B. da, Duarte M.A.V. Tool wear monitoring in micromilling using support vector machine with vibration and sound sensors. Precision Engineering, 2021, vol. 67, pp. 137–151. DOI: 10.1016/j. precisioneng.2020.09.025. 12. Klocke F., Dobbeler B., Pullen T., Bergs T. Acoustic emission signal source separation for a fl ank wear estimation of drilling tools. Procedia CIRP, 2019, vol. 79, pp. 57–62. DOI: 10.1016/j.procir.2019.02.011. 13. Liu C.S., Ou Y.J. Grinding wheel loading evaluation by using acoustic emission signals and digital image processing. Sensors, 2020, vol. 20, pp. 1–13. DOI: 10.3390/s20154092. 14. Cheng C., Li J., Liu Y., Nie M., Wang W. Deep convolutional neural network-based in-process tool condition monitoring in abrasive belt grinding. Computers in Industry, 2019, vol. 106, pp. 1–13. DOI: 10.1016/j. compind.2018.12.002. 15. Viera M.A.A., Alexandre F.A., Aguiar P.R., Silva R.B., Bianchi E.C. Correlation between surface roughness and AE signals in ceramic grinding based on spectral analysis. MATEC Web of Conferences, 2018, vol. 249, pp. 1–5. DOI: 10.1051/matecconf/2018249030. 16. Lin B., Wang H., Wei J., Sui T. Diamond wheel grinding characteristics of 3D-orthogonal quartz fi ber reinforced silica ceramic matrix composite. Chinese Journal of Aeronautics, 2020, vol. 34, iss. 5, pp. 404–414. DOI: 10.1016/j.cja.2020.12.026. 17. Nasir V., Dibaji S., Alaswad K., Cool J. Tool wear monitoring by ensemble learning and sensor fusion using power, sound, vibration, and AE signals. Manufacturing Letters, 2021, vol. 30, pp. 32–38. DOI: 10.1016/j. mfglet.2021.10.002. 18. Miao Q., Ding W., Kuang W., Xu J. Tool wear behavior of vitrifi ed microcrystalline alumina wheels in creep feed profi le grinding of turbine blade root of single crystal nickel-based superalloy. Tribology International, 2020, vol. 145, pp. 1–10. DOI: 10.1016/j.triboint.2019.106144. 19. Xu L., Niu M., Zhao D., Xing N., Fan F. Methodology for the immediate detection and treatment of wheel wear in contour grinding. Precision Engineering, 2019, vol. 60, pp. 405–412. DOI: 10.1016/j.precisioneng.2019.09.006. 20. Agnard S., Liu Z., Hazel B. Material removal and wheel wear models for robotic grinding wheel profi ling. Procedia Manufacturing, 2015, vol. 2, pp. 35–40. DOI: 10.1016/j.promfg.2015.07.007. 21. Gur’yanikhinV.F. [Development of tools for current monitoring and control of grinding and dressing processes by the intensity of sound emission]. Voprosy tekhnologii mashinostroeniya [Problems of Mechanical Engineering Technology]. Proceedings of a visiting meeting of the Head Council “Mechanical Engineering” of the Ministry of Education of the Russian Federation. Ulyanovsk, 2003, pp. 67–72. (In Russian). 22. Glagovskii B.A. Nizkochastotnye akusticheskie metody kontrolya v mashinostroenii [Low-frequency acoustic control methods in mechanical engineering]. Leningrad, Mashinostroenie Publ., 1977. 203 p.

RkJQdWJsaXNoZXIy MTk0ODM1