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Operational modal identification of ultra-precision fly-cutting machine tools based on least-squares complex frequency-domain method

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Abstract

Identification of modal parameters of ultra-precision machine tools is an important method to study the dynamic characteristics of structures. Compared with theoretical modeling method, modal testing is a more concise and efficient way. Traditional experimental methods such as an impact hammer testing are unsuitable for use under operational conditions. This paper demonstrates the principle of a complex frequency-domain method for modal identification of ultra-precision fly-cutting machine tools. Modal testing is performed while cutting a circular copper workpiece, which means that all structural modes are excited by cutting force, rather than external vibration sources. Before extracting modal parameters, the raw data is preprocessed with a Kalman filter to reduce the impact of spindle frequency and its harmonics. The time-domain data is then transformed into power spectral density data, and the least-squares method is used to fit modal parameters in the frequency domain. Then, both the single-reference and poly-reference modal experiments are implemented. The steady-state criterion is established to evaluate the quality of calculating results. Finally, contrastive experiments between the proposed and experimental methods validate the effectiveness of the least-squares complex frequency-domain method, and the dominant natural frequencies are thoroughly investigated.

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References

  1. Vu V, Liu Z, Thomas M, Hazel B (2017) Modal analysis of a light-weight robot with a rotating tool installed at the end effector. Proc Inst Mech Eng, Part C 231:1664–1676. https://doi.org/10.1177/0954406215619451

    Article  Google Scholar 

  2. Fuellekrug U, Goege D (2012) Identification of weak non-linearities within complex aerospace structures. Aerosp Sci Technol 23:53–62. https://doi.org/10.1016/j.ast.2011.04.012

    Article  Google Scholar 

  3. Li J, Li X, Wei W, Liu P (2020) Relative vibration identification of cutter and workpiece based on improved bidimensional empirical mode decomposition. Front Mech Eng 15:227–239. https://doi.org/10.1007/s11465-020-0587-1

    Article  Google Scholar 

  4. Chen W, Lu L, Yang K, Su H (2016) An experimental and theoretical investigation into multimode machine tool vibration with surface generation in flycutting. Proc Inst Mech Eng, Part B 230:381–386. https://doi.org/10.1177/0954405415584961

    Article  Google Scholar 

  5. Li J, Huang M, Wei W (2021) Study on traceability and suppression method of medium-frequency error for ultra-precision machining optical crystals. Opt Express 29:22252–22265. https://doi.org/10.1364/OE.432500

    Article  Google Scholar 

  6. Zahid FB, Ong ZC, Khoo SY (2020) A review of operational modal analysis techniques for in-service modal identification. J Braz Soc Mech Sci Eng 42:398. https://doi.org/10.1007/s40430-020-02470-8

    Article  Google Scholar 

  7. Altintas Y, Cao Y (2005) Virtual design and optimization of machine tool spindles. CIRP Ann 54:379–382. https://doi.org/10.1016/S0007-8506(07)60127-9

    Article  Google Scholar 

  8. Huynh HN, Altintas Y (2020) Modeling the dynamics of five-axis machine tool using the multibody approach. J Manuf Sci Eng 143:021012. https://doi.org/10.1115/1.4048854

    Article  Google Scholar 

  9. Chang Y, Ding J, He Z (2020) Effect of joint interfacial contact stiffness on structural dynamics of ultra-precision machine tool. Int J Mach Tools Manuf 158:103609. https://doi.org/10.1016/j.ijmachtools.2020.103609

    Article  Google Scholar 

  10. Chen W, Sun Y, An C, Sun H, Yang K, Zhang Q (2015) Modeling and simulation of the interaction of manufacturing process and machine tool structure in flycutting machining. Proc Inst Mech Eng, Part C 229:2730–2736. https://doi.org/10.1177/0954406214563962

    Article  Google Scholar 

  11. Tuysuz O, Altintas Y (2019) Analytical modeling of process damping in machining. J Manuf Sci Eng 141:061006. https://doi.org/10.1115/1.4043310

    Article  Google Scholar 

  12. Ma W, Yang Y, Jin X (2021) Chatter suppression in micro-milling using shank-mounted two-DOF tuned mass damper. Precis Eng 72:144–157. https://doi.org/10.1016/j.precisioneng.2021.04.017

    Article  Google Scholar 

  13. Reynders E (2012) System identification methods for (operational) modal analysis: review and comparison. Arch Comput Methods Eng 19:51–124. https://doi.org/10.1007/s11831-012-9069-x

    Article  MathSciNet  MATH  Google Scholar 

  14. Li B, Li L, He H, Mao X, Jiang X, Peng Y (2019) Research on modal analysis method of CNC machine tool based on operational impact excitation. Int J Adv Manuf Technol 103:1155–1174. https://doi.org/10.1007/s00170-019-03510-x

    Article  Google Scholar 

  15. Li B, Luo B, Mao X, Cai H, Peng F, Liu H (2013) A new approach to identifying the dynamic behavior of CNC machine tools with respect to different worktable feed speeds. Int J Mach Tools Manuf 72:73–84. https://doi.org/10.1016/j.ijmachtools.2013.06.004

    Article  Google Scholar 

  16. Li B, Cai H, Mao X, Huang J, Luo B (2013) Estimation of CNC machine–tool dynamic parameters based on random cutting excitation through operational modal analysis. Int J Mach Tools Manuf 71:26–40. https://doi.org/10.1016/j.ijmachtools.2013.04.001

    Article  Google Scholar 

  17. Huang Q, Liao J, Zhou J, Li J (2020) Research on dominant vibration mode analysis of machining process of machine tools. Int J Adv Manuf Technol 109:275–287. https://doi.org/10.1007/s00170-020-05654-7

    Article  Google Scholar 

  18. Feng Z, Qin S, Liang M (2016) Time–frequency analysis based on Vold-Kalman filter and higher order energy separation for fault diagnosis of wind turbine planetary gearbox under nonstationary conditions. Renewable Energy 85:45–56. https://doi.org/10.1016/j.renene.2015.06.041

    Article  Google Scholar 

  19. Chen X, Feng Z (2021) Order spectrum analysis enhanced by surrogate test and Vold-Kalman filtering for rotating machinery fault diagnosis under time-varying speed conditions. Mech Syst Signal Process 154:107585. https://doi.org/10.1016/j.ymssp.2020.107585

    Article  Google Scholar 

  20. Zaghbani I, Songmene V (2009) Estimation of machine-tool dynamic parameters during machining operation through operational modal analysis. Int J Mach Tools Manuf 49:947–957. https://doi.org/10.1016/j.ijmachtools.2009.06.010

    Article  Google Scholar 

  21. Maamar A, Le TP, Gagnol V, Sabourin L (2019) Modal identification of a machine tool structure during machining operations. Int J Adv Manuf Technol 102:253–264. https://doi.org/10.1007/s00170-018-3172-6

    Article  Google Scholar 

  22. Guillaume P, Verboven P, Vanlanduit S (1998) Frequency-domain maximum likelihood estimation of modal parameters with confidence intervals. In: Proceedings of the 23rd international seminar on modal analysis, ISMA23. Leuven, Belgium, pp 359–366

  23. De Troyer T, Guillaume P, Pintelon R, Vanlanduit S (2009) Fast calculation of confidence intervals on parameter estimates of least-squares frequency-domain estimators. Mech Syst Signal Process 23:261–273. https://doi.org/10.1016/j.ymssp.2008.04.009

    Article  Google Scholar 

  24. Huang Y, Dimitriadis G, Kielb RE, Li J (2017) System eigenvalue identification of mistuned bladed disks using least-squares complex frequency-domain method. ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition, GT 2017, V7B-2017. https://doi.org/10.1115/GT2017-63008

  25. Canales G, Mevel L, Basseville M (2008) A polyreference least square complex frequency domain based statistical test for damage detection. IFAC Proceedings Volumes 41:4511–4516. https://doi.org/10.3182/20080706-5-KR-1001.00759

    Article  Google Scholar 

  26. Storti G, Machado T (2021) The use of operational modal analysis in the process of modal parameters identification in a rotating machine supported by roller bearings. J Mech Sci Technol 35:471–480. https://doi.org/10.1007/s12206-021-0105-3

    Article  Google Scholar 

  27. Liu Y-P, Altintas Y (2021) In-process identification of machine tool dynamics. CIRP J Manuf Sci Technol 32:322–337. https://doi.org/10.1016/j.cirpj.2021.01.007

    Article  Google Scholar 

  28. Favarelli E, Giorgetti A (2021) Machine learning for automatic processing of modal analysis in damage detection of bridges. IEEE Trans Instrum Meas 70. https://doi.org/10.1109/TIM.2020.3038288

  29. Jia P, Rong Y, Huang Y (2019) Condition monitoring of the feed drive system of a machine tool based on long-term operational modal analysis. Int J Mach Tools Manuf 146–103454 https://doi.org/10.1016/j.ijmachtools.2019.103454

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Funding

This work was supported in part by China Postdoctoral Science Foundation under Grant 2021M693906, in part by National Natural Science Foundation of China under Grant 52005460, and in part by the Science Challenge Project under Grant JCKY2016212A506-0105.

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Authors and Affiliations

  1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jinchun Yuan & Pinkuan Liu

  2. Institute of Machinery Manufacturing Technology, China Academy of Engineering Physics, 610200, Sichuan, China

    Jinchun Yuan, Jiasheng Li & Wei Wei

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  1. Jinchun Yuan
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  2. Jiasheng Li
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  3. Wei Wei
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  4. Pinkuan Liu
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Contributions

Jinchun Yuan performed analysis and writing work; Jiasheng Li provided theoretical guidance; Wei Wei provided experimental guidance; and Pinkuan Liu helped revise the manuscript.

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Correspondence to Jiasheng Li.

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Yuan, J., Li, J., Wei, W. et al. Operational modal identification of ultra-precision fly-cutting machine tools based on least-squares complex frequency-domain method. Int J Adv Manuf Technol 119, 4385–4394 (2022). https://doi.org/10.1007/s00170-021-08469-2

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  • DOI: https://doi.org/10.1007/s00170-021-08469-2

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