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Feature extraction of milling chatter based on optimized variational mode decomposition and multi-scale permutation entropy

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Abstract

In the milling process, chatter is easy to occur and has a very adverse impact on the quality of the workpiece and the production efficiency. A chatter feature extraction method based on optimized variational mode decomposition (OVMD) and multi-scale permutation entropy (MPE) was proposed to solve the problem that it is difficult to detect the machining chatter state during milling. The methodology presented in this article allows the occurrence of machining chatter to be effectively identified through real-time digital signal processing and analysis. First, in order to solve the problem of variational mode decomposition (VMD) parameter selection, an automatic selection method based on particle swarm optimization (PSO) and the maximum crest factor of the envelope spectrum (CE) was proposed. Then, the decomposed signal was reconstructed based on the energy ratio. In order to solve the problem that the single-scale permutation entropy (PE) cannot detect milling chatter well, the MPE was introduced to detect milling chatter. Finally, experimental verification was carried out, and the MPE of the reconstructed signals at different scales was extracted and analyzed. The results show that using the OVMD algorithm to process the signals can significantly improve the discrimination of MPE. With the increase of the scale factor, the MPE of the milling signals tends to decrease. At the same time, MPE is better than single-scale PE in chatter detection, and the MPE at scale factor of 4 is more conducive to chatter detection.

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The raw/processed data required to reproduce these findings cannot be shared for the time being. Data will be made available upon request.

References

  1. Quintana G, Ciurana J (2011) Chatter in machining processes: a review. Int J Mach Tools Manuf 51(5):363–376. https://doi.org/10.1016/j.ijmachtools.2011.01.001

    Article  Google Scholar 

  2. Wang ZX, Liu XL, Li MY, Liang SY, Wang LH, Li YQ, Meng BY (2020) Intelligent monitoring and control technology of cutting chatter. J Mech Eng 56(24):1–23. https://doi.org/10.3901/JME.2020.24.001

    Article  Google Scholar 

  3. Ye J, Feng PF, Xu C, Ma Y, Huang SG (2018) A novel approach for chatter online monitoring using coefficient of variation in machining process. Int J Adv Manuf Technol 96:287–297. https://doi.org/10.1007/s00170-017-1544-y

    Article  Google Scholar 

  4. Fekrmandi H, Unal M, Baghalian A, Tashakori S, Oyola K, Alsenawi A, Tansel I (2016) A non-contact method for part-based process performance monitoring in end milling operations. Int J Adv Manuf Technol 83(1-4):13–20. https://doi.org/10.1007/s00170-015-7523-2

    Article  Google Scholar 

  5. Sun YX, Zhuang CG, Xiong ZH (2015) A scale factor-based interpolated DFT for chatter frequency estimation. IEEE Trans Instrum Meas 64(10):2666–2678. https://doi.org/10.1109/tim.2015.2421711

    Article  Google Scholar 

  6. Zhang CL, Li B, Chen BQ, Cao HR, Zi YY, He ZJ (2015) Weak fault signature extraction of rotating machinery using flexible analytic wavelet transform. Mech Syst Signal Process 64-65:162–187. https://doi.org/10.1016/j.ymssp.2015.03.030

    Article  Google Scholar 

  7. Wang YX, Markert R, Xiang JW, Zheng WG (2015) Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system. Mech Syst Signal Process 60-61:243–251. https://doi.org/10.1016/j.ymssp.2015.02.020

    Article  Google Scholar 

  8. Liu J, Hu YM, Wu B, Jin C (2017) A hybrid health condition monitoring method in milling operations. Int J Adv Manuf Technol 92:2069–2080. https://doi.org/10.1007/s00170-017-0252-y

    Article  Google Scholar 

  9. Yang K, Wang GF, Dong Y, Zhang QB, Sang LL (2019) Early chatter identification based on an optimized variational mode decomposition. Mech Syst Signal Process 115:238–254. https://doi.org/10.1016/j.ymssp.2018.05.052

    Article  Google Scholar 

  10. Liu CF, Zhu LD, Ni CB (2018) Chatter detection in milling process based on VMD and energy entropy. Mech Syst Signal Process 105:69–182. https://doi.org/10.1016/j.ymssp.2017.11.046

    Article  Google Scholar 

  11. Xu W, Hu JF (2021) A novel parameter-adaptive vmd method based on grey wolf optimization with minimum average mutual information for incipient fault detection. Shock Vib 2021(2):1–14. https://doi.org/10.1155/2021/6640387

    Article  Google Scholar 

  12. Liu C, Xu WW, Gao L (2020) Identification of milling chatter based on a novel frequency-domain search algorithm. Int J Adv Manuf Technol 109:2393–2407. https://doi.org/10.1007/s00170-020-05789-7

    Article  Google Scholar 

  13. Chen Y, Li HZ, Hou L, Bu XJ, Ye SG, Chen D (2020) Chatter detection for milling using novel p-leader multifractal features. J Intel Manuf. https://doi.org/10.1007/s10845-020-01651-5

  14. Tran MQ, Liu MK, Tran QV (2020) Milling chatter detection using scalogram and deep convolutional neural network. Int J Adv Manuf Technol 107:1505–1516. https://doi.org/10.1007/s00170-019-04807-7

    Article  Google Scholar 

  15. Ji YJ, Wang XB, Liu ZB, Yan ZG, Jiao L, Wang DQ, Wang JQ (2017) EEMD-based online milling chatter detection by fractal dimension and power spectral entropy. Int J Adv Manuf Technol 92(1-4):1185–1200. https://doi.org/10.1007/s00170-017-0183-7

    Article  Google Scholar 

  16. Zhang Z, Li HG, Meng G, Tu XT, Cheng CM (2016) Chatter detection in milling process based on the energy entropy of VMD and WPD. Int J Mach Tools Manuf 108:106–112. https://doi.org/10.1016/j.ijmachtools.2016.06.002

    Article  Google Scholar 

  17. Nair U, Krishna BM, Namboothiri VNN, Nampoori VPN (2010) Permutation entropy based real-time chatter detection using audio signal in turning process. Int J Adv Manuf Technol 46(1-4):61–68. https://doi.org/10.1007/s00170-009-2075-y

    Article  Google Scholar 

  18. Ren JB, Sun GZ, Chen B, Luo M (2015) Multi-scale permutation entropy based on-line milling chatter detection method. J Mech Eng 51(9):206–212. https://doi.org/10.3901/JME.2015.09.206

    Article  Google Scholar 

  19. Li XL, Ouyang GX, Liang ZH (2008) Complexity measure of motor current signals for tool flute breakage detection in end milling. Int J Mach Tools Manuf 48(3):371–379. https://doi.org/10.1016/j.ijmachtools.2007.09.008

    Article  Google Scholar 

  20. Rusinek R, Borowiec M (2015) Stability analysis of titanium alloy milling by multiscale entropy and Hurst exponent. Eur Phys J Plus 130(10):194. https://doi.org/10.1140/epjp/i2015-15194-1

    Article  Google Scholar 

  21. Li K, He SP, Luo B, Li B, Liu HQ, Mao XY (2019) Online chatter detection in milling process based on VMD and multiscale entropy. Int J Adv Manuf Technol 105(5):5009–5022. https://doi.org/10.1007/s00170-019-04478-4

    Article  Google Scholar 

  22. Li K, He SP, Li B, Liu HQ, Mao XY, Shi CM (2020) A novel online chatter detection method in milling process based on multiscale entropy and gradient tree boosting. Mech Syst Signal Process 135:106385. https://doi.org/10.1016/j.ymssp.2019.106385

    Article  Google Scholar 

  23. Yi CC, Lv Y, Dang Z (2016) A fault diagnosis scheme for rolling bearing based on particle swarm optimization in variational mode decomposition. Shock Vib 2016:1–10. https://doi.org/10.1155/2016/9372691

    Article  Google Scholar 

  24. Zhang L, Xiong GL, Huang WY (2015) New procedure and index for the parameter optimization of complex wavelet based resonance demodulation. J Mech Eng 51(3):129–138. https://doi.org/10.3901/JME.2015.03.129

    Article  Google Scholar 

  25. Liu C, ChengG CXH, Pang YS (2018) Planetary gears feature extraction and fault diagnosis method based on VMD and CNN. Sensors 18(5):1523. https://doi.org/10.3390/s18051523

    Article  Google Scholar 

  26. Dragomiretskiy K, Zosso D (2014) Variational mode decomposition. IEEE Trans Signal Process 62(3):531–544. https://doi.org/10.1109/TSP.2013.2288675

    Article  MathSciNet  MATH  Google Scholar 

  27. Bandt C, Pompe B (2002) Permutation entropy: a natural complexity measure for time series. Phys Rev Lett 88(17):174102. https://doi.org/10.1103/physrevlett.88.174102

    Article  Google Scholar 

  28. Yan RQ, Liu YB, Gao RX (2012) Permutation entropy: a nonlinear statistical measure for status characterization of rotary machines. Mech Syst Signal Process 29:474–484. https://doi.org/10.1016/j.ymssp.2011.11.022

    Article  Google Scholar 

  29. Costa M, Goldberger AL, Peng CK (2002) Multiscale entropy to distinguish physiologic and synthetic RR time series. Comput Cardiol 2002:137–140. https://doi.org/10.1109/cic.2002.1166726

    Article  Google Scholar 

  30. Zhang L, Mao ZD, Xiong GL, Cui LY (2019) Adaptive fault diagnosis of rolling bearings based on crest factor of envelope spectrum. Mech Sci Technol Aerosp Eng 38(4):507–514. https://doi.org/10.13433/j.cnki.1003-8728.20180244

    Article  Google Scholar 

  31. Bao WJ, Tu XT, Hu Y, Li FC (2019) Envelope spectrum l-kurtosis and its application for fault detection of rolling element bearings. IEEE Trans Instrum Meas 69(5):1993–2002. https://doi.org/10.1109/TIM.2019.2917982

    Article  Google Scholar 

  32. Appana DK, Alexander P, Jong-Myon K (2018) Reliable fault diagnosis of bearings with varying rotational speeds using envelope spectrum and convolution neural networks. Soft Comput 22:6719–6729. https://doi.org/10.1007/s00500-018-3256-0

    Article  Google Scholar 

  33. Cabrera CG, Araujo AC, Castello DA (2017) On the wavelet analysis of cutting forces for chatter identification in milling. Adv Manuf 5(2):130–142. https://doi.org/10.1007/s40436-017-0179-4

    Article  Google Scholar 

  34. Liu XL, Gao HN, Yue CX, Li RY, Jiang N, Yang L (2018) Investigation of the milling stability based on modified variable cutting force coefficients. Int J Adv Manuf Technol 96:2991–3002. https://doi.org/10.1007/s00170-018-1780-9

    Article  Google Scholar 

  35. Zheng XX, Zhou GW, Ren HH, Fu Y (2017) A rolling bearing fault diagnosis method based on variational mode decomposition and permutation entropy. J Vib Shock 36(22):22–28. https://doi.org/10.13465/j.cnki.jvs.2017.22.004

    Article  Google Scholar 

  36. Zhang J, Zhao Y, Liu M, Kong L (2019) Bearings fault diagnosis based on adaptive local iterative filtering–multiscale permutation entropy and multinomial logistic model with group-lasso. Adv Mech Eng 11(3):168781401983631. https://doi.org/10.1177/1687814019836311

    Article  Google Scholar 

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Funding

This work was financially supported by:

(1) International (regional) cooperation and exchange program of national Natural Science Foundation of China under Grant No. 51720105009.

(2) National key R&D plan. Network collaborative manufacturing and smart factory special project: “Complex Tool Monitoring and Full Life Cycle Intelligent Management and Control Technology” under Grant No. 2019YFB1704800.

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Authors and Affiliations

  1. Key Laboratory of Advanced Manufacturing and Intelligent Technology, Ministry of Education, Harbin University of Science and Technology, Harbin, 150080, China

    Xianli Liu, Zhixue Wang, Maoyue Li & Caixu Yue

  2. Harbin Vocational & Technical College, Harbin, 150076, China

    Zhixue Wang

  3. George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA

    Steven Y. Liang

  4. KTH Royal Institute of Technology, 25175, Stockholm, Sweden

    Lihui Wang

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  1. Xianli Liu
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Contributions

Xianli Liu has designed the experiments, analyzed and arranged data, and wrote the manuscript; Zhixue Wang has organized the project, analyzed and arranged data, and wrote the manuscript; Maoyue Li has conducted the experiments and collected and analyzed data; Caixu Yue has conducted the experiments and collected and analyzed data; Steven Y. Liang has reviewed the manuscript; Lihui Wang has reviewed the manuscript.

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Correspondence to Zhixue Wang.

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Liu, X., Wang, Z., Li, M. et al. Feature extraction of milling chatter based on optimized variational mode decomposition and multi-scale permutation entropy. Int J Adv Manuf Technol 114, 2849–2862 (2021). https://doi.org/10.1007/s00170-021-07027-0

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