Skip to main content

Advertisement

Springer Nature Link
Log in

Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method

  • Published:
Journal of Intelligent Manufacturing Aims and scope Submit manuscript

Abstract

Induced by flexibility of the industrial robot, cutting tool or the workpiece, chatter in robotic machining process has detrimental effects on the surface quality, tool life and machining productivity. Consequently, accurate detection and timely suppression for such undesirable vibration is desperately needed to achieve high performance robotic machining. This paper presents a novel approach combining the notch filter and local maximum synchrosqueezing transform for the timely chatter identification in robotic drilling. The proposed approach is accomplished through the following steps. In the first step, the optimal matrix notch filter is designed to eliminate the interference of the spindle frequency and corresponding harmonic components to the measured acceleration signal. Subsequently, the high-resolution time–frequency information of the non-stationary filtered acceleration signal is acquired by employing local maximum synchrosqueezing transform (LMSST). On this basis, the filtered acceleration signal is divided into a finite number of equal-width frequency bands, and the corresponding sub-signal for each frequency band is obtained by summing the corresponding coefficient of the LMSST. Finally, to accurately depict the non-uniformity of energy distribution during the chatter incubation process, the statistical energy entropy is calculated and utilized as the indicator to detect chatter online. The effectiveness of the proposed approach is validated by a large number of robot drilling experiments with different cutting tools, workpiece materials and machining parameters. The results show that the presented local maximum synchrosqueezing-based approach can effectively recognize the chatter at an early stage during its incubation and development process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  • Altintas, Y., Stepan, G., Merdol, D., & Dombovari, Z. (2008). Chatter stability of milling in frequency and discrete time domain. CIRP Journal of Manufacturing Science and Technology,1(1), 35–44.

    Article  Google Scholar 

  • Aslan, D., & Altintas, Y. (2018). On-line chatter detection in milling using drive motor current commands extracted from CNC. International Journal of Machine Tools and Manufacture,132, 64–80.

    Article  Google Scholar 

  • Bi, S., & Liang, J. (2011). Robotic drilling system for titanium structures. International Journal of Advanced Manufacturing Technology,54, 767–774.

    Article  Google Scholar 

  • Bu, Y., Liao, W. H., Tian, W., Zhang, L., & Li, D. W. (2017). Modeling and experimental investigation of Cartesian compliance characterization for drilling robot. International Journal of Advanced Manufacturing Technology,91(9–12), 3253–3264.

    Article  Google Scholar 

  • Cao, H., Yue, Y., Chen, X., & Zhang, X. (2017). Chatter detection in milling process based on synchro squeezing transform of sound signals. International Journal of Advanced Manufacturing Technology,89(9–12), 2747–2755.

    Article  Google Scholar 

  • Chen, Y., & Dong, F. (2013). Robot machining: Recent development and future research issues. International Journal of Advanced Manufacturing Technology,66(9–12), 1489–1497.

    Article  Google Scholar 

  • Cordes, M., Hintze, W., & Altintas, Y. (2019). Chatter stability in robotic milling. Robotics and Computer-Integrated Manufacturing,55, 11–18.

    Article  Google Scholar 

  • Frommknecht, A., Kuehnle, J., Effenberger, I., & Pidan, S. (2017). Multi-sensor measurement system for robotic drilling. Robotics and Computer-Integrated Manufacturing,47, 4–10.

    Article  Google Scholar 

  • Fu, Y., Zhang, Y., Gao, H., Mao, T., Zhou, H., Sun, R., et al. (2019). Automatic feature constructing from vibration signals for machining state monitoring. Journal of Intelligent Manufacturing,30(3), 995–1008.

    Article  Google Scholar 

  • Fu, Y., Zhang, Y., Zhou, H., Li, D., Liu, H., Qiao, H., et al. (2016). Timely online chatter detection in end milling process. Mechanical Systems and Signal Processing,75, 668–688.

    Article  Google Scholar 

  • Han, D., & Zhang, X. H. (2010). Optimal matrix filter design with application to filtering short data records. IEEE Signal Processing Letters,17(5), 521–524.

    Article  Google Scholar 

  • Huang, P., Li, J., Sun, J., & Zhou, J. (2013). Vibration analysis in milling titanium alloy based on signal processing of cutting force. International Journal of Advanced Manufacturing Technology,64(5–8), 613–621.

    Article  Google Scholar 

  • Iglesias, I., Sebastián, M. A., & Ares, J. E. (2015). Overview of the state of robotic machining: Current situation and future potential. Procedia Engineering,132, 911–917.

    Article  Google Scholar 

  • Insperger, T., & Stepan, G. (2004). Updated semi-discretization method for periodic delay-differential equations with discrete delay. International Journal for Numerical Methods in Biomedical Engineering,61(1), 117–141.

    Article  Google Scholar 

  • Ji, Y., Wang, X., Liu, Z., Wang, H., Jiao, L., Wang, D., et al. (2018). Early milling chatter identification by improved empirical mode decomposition and multi-indicator synthetic evaluation. Journal of Sound and Vibration,433, 138–159.

    Article  Google Scholar 

  • Ji, Y., Wang, X., Liu, Z., Yan, Z., Jiao, L., Wang, D., et al. (2017). EEMD-based online milling chatter detection by fractal dimension and power spectral entropy. International Journal of Advanced Manufacturing Technology,92(1–4), 1185–1200.

    Article  Google Scholar 

  • Kuljanic, E., Totis, G., & Sortino, M. (2009). Development of an intelligent multisensor chatter detection system in milling. Mechanical Systems and Signal Processing,23(5), 1704–1718.

    Article  Google Scholar 

  • Lamraoui, M., Thomas, M., & El Badaoui, M. (2014a). Cyclostationarity approach for monitoring chatter and tool wear in high speed milling. Mechanical Systems and Signal Processing,44(1–2), 177–198.

    Article  Google Scholar 

  • Lamraoui, M., Thomas, M., El Badaoui, M., & Girardin, F. (2014b). Indicators for monitoring chatter in milling based on instantaneous angular speeds. Mechanical Systems and Signal Processing,44(1–2), 72–85.

    Article  Google Scholar 

  • Li, Z. Q., & Liu, Q. (2008). Solution and analysis of chatter stability for end milling in the time-domain. Chinese Journal of Aeronautics,21, 169–178.

    Article  Google Scholar 

  • Lin, Y., Zhao, H., & Ding, H. (2017). Posture optimization methodology of 6R industrial robots for machining using performance evaluation indexes. Robotics and Computer-Integrated Manufacturing,48, 59–72.

    Article  Google Scholar 

  • Liu, H., Chen, Q., Li, B., Mao, X., Mao, K., & Peng, F. (2011). On-line chatter detection using servo motor current signal in turning. Science China Technological Sciences,54(12), 3119–3129.

    Article  Google Scholar 

  • Liu, Y., Wang, X., Lin, J., & Zhao, W. (2016). Early chatter detection in gear grinding process using servo feed motor current. International Journal of Advanced Manufacturing Technology,83(9–12), 1801–1810.

    Article  Google Scholar 

  • Liu, C., Zhu, L., & Ni, C. (2017). The chatter identification in end milling based on combining EMD and WPD. International Journal of Advanced Manufacturing Technology,91(9–12), 3339–3348.

    Article  Google Scholar 

  • Liu, C., Zhu, L., & Ni, C. (2018). Chatter detection in milling process based on VMD and energy entropy. Mechanical Systems and Signal Processing,105, 169–182.

    Article  Google Scholar 

  • Lu, K., Jing, M., Zhang, X., Dong, G., & Liu, H. (2015). An effective optimization algorithm for multipass turning of flexible workpieces. Journal of Intelligent Manufacturing,26, 831–840.

    Article  Google Scholar 

  • Mei, B., Zhu, W., Yuan, K., & Ke, Y. (2015). Robot base frame calibration with a 2D vision system for mobile robotic drilling. International Journal of Advanced Manufacturing Technology,80(9–12), 1903–1917.

    Article  Google Scholar 

  • Mousavi, S., Gagnol, V., Bouzgarrou, B. C., & Ray, P. (2017). Dynamic modeling and stability prediction in robotic machining. International Journal of Advanced Manufacturing Technology,88(9–12), 3053–3065.

    Article  Google Scholar 

  • Munoa, J., Beudaert, X., Dombovari, Z., Altintas, Y., Budak, E., Brecher, C., et al. (2016). Chatter suppression techniques in metal cutting. CIRP Annals—Manufacturing Technology,65(2), 785–808.

    Article  Google Scholar 

  • Piskorowski, J. (2010). Digital q-varying notch IIR filter with transient suppression. IEEE Transactions on Instrumentation and Measurement,59(4), 866–872.

    Article  Google Scholar 

  • Piskorowski, J. (2012). Suppressing harmonic powerline interference using multiple-notch filtering methods with improved transient behavior. Measurement,45(6), 1350–1361.

    Article  Google Scholar 

  • Pour, M., & Torabizadeh, M. A. (2016). Improved prediction of stability lobes in milling process using time series analysis. Journal of Intelligent Manufacturing,27(3), 665–677.

    Article  Google Scholar 

  • Qin, C. J., Tao, J. F., Li, L., & Liu, C. L. (2017a). An Adams-Moulton-based method for stability prediction of milling processes. International Journal of Advanced Manufacturing Technology,89(9–12), 3049–3058.

    Article  Google Scholar 

  • Qin, C. J., Tao, J. F., & Liu, C. L. (2017b). Stability analysis for milling operations using an Adams-Simpson-based method. International Journal of Advanced Manufacturing Technology,92(1–4), 969–979.

    Article  Google Scholar 

  • Qin, C. J., Tao, J. F., & Liu, C. L. (2018). A predictor-corrector-based holistic-discretization method for accurate and efficient milling stability analysis. International Journal of Advanced Manufacturing Technology,96(5–8), 2043–2054.

    Article  Google Scholar 

  • Qin, C. J., Tao, J. F., & Liu, C. L. (2019). A novel stability prediction method for milling operations using the holistic-interpolation scheme. Proceedings—IMechE Part C, Journal of Mechanical Engineering Science,233(13), 4463–4475.

    Article  Google Scholar 

  • Somkiat, T. (2011). Advanced in detection system to improve the stability and capability of CNC turning process. Journal of Intelligent Manufacturing,22, 843–852.

    Article  Google Scholar 

  • Sun, Y. X., & Xiong, Z. H. (2016). An optimal weighted wavelet packet entropy method with application to real-time chatter detection. IEEE-ASME Transactions on Mechatronics,21(4), 2004–2014.

    Article  Google Scholar 

  • Tangjitsitcharoen, S., Saksri, T., & Ratanakuakangwan, S. (2015). Advance in chatter detection in ball end milling process by utilizing wavelet transform. Journal of Intelligent Manufacturing,26(3), 485–499.

    Article  Google Scholar 

  • Tao, J. F., Qin, C. J., & Liu, C. L. (2019a). A synchroextracting-based method for early chatter identification of robotic drilling process. International Journal of Advanced Manufacturing Technology,100(1–4), 273–285.

    Article  Google Scholar 

  • Tao, J., Qin, C., Xiao, D., Shi, H., & Liu, C. (2019b). A pre-generated matrix-based method for real-time robotic drilling chatter monitoring. Chinese Journal of Aeronautics. https://doi.org/10.1016/j.cja.2019.09.001.

    Article  Google Scholar 

  • Thaler, T., Potočnik, P., Bric, I., & Govekar, E. (2014). Chatter detection in band sawing based on discriminant analysis of sound features. Applied Acoustics,77, 114–121.

    Article  Google Scholar 

  • Tong, X., Liu, Q., Pi, S., & Xiao, Y. (2019). Real-time machining data application and service based on IMT digital twin. Journal of Intelligent Manufacturing. https://doi.org/10.1007/s10845-019-01500-0.

    Article  Google Scholar 

  • Tseng, C. C., & Pei, S. C. (2001). Stable IIR notch filter design with optimal pole placement. IEEE Transactions on Signal Processing,49(11), 2673–2681.

    Article  Google Scholar 

  • Vaccaro, R. J., & Harrison, B. F. (1996). Optimal matrix-filter design. IEEE Transactions on Signal Processing,44(3), 705–709.

    Article  Google Scholar 

  • Wan, S., Li, X., Chen, W., & Hong, J. (2018). Investigation on milling chatter identification at early stage with variance ratio and Hilbert-Huang transform. International Journal of Advanced Manufacturing Technology,95, 3563–3573.

    Article  Google Scholar 

  • Wang, G., Dong, H., Guo, Y., & Ke, Y. (2017). Chatter mechanism and stability analysis of robotic boring. International Journal of Advanced Manufacturing Technology,91, 411–421.

    Article  Google Scholar 

  • Wang, G., Dong, H., Guo, Y., & Ke, Y. (2018). Early chatter identification of robotic boring process using measured force of dynamometer. International Journal of Advanced Manufacturing Technology,94(1–4), 1243–1252.

    Article  Google Scholar 

  • Yang, K., Wang, G., Dong, Y., Zhang, Q., & Sang, L. (2019). Early chatter identification based on an optimized variational mode decomposition. Mechanical Systems and Signal Processing,115, 238–254.

    Article  Google Scholar 

  • Ye, J., Feng, P., Xu, C., Ma, Y., & Huang, S. (2018). A novel approach for chatter online monitoring using coefficient of variation in machining process. International Journal of Advanced Manufacturing Technology,96(1–4), 287–297.

    Article  Google Scholar 

  • Yu, G., Wang, Z. H., Zhao, P., & Li, Z. (2019). Local maximum synchrosqueezing transform: An energy-concentrated time-frequency analysis tool. Mechanical Systems and Signal Processing,117, 537–552.

    Article  Google Scholar 

  • Yu, G., Yu, M., & Xu, C. (2017). Synchroextracting transform. IEEE Transactions on Industrial Electronics,64(10), 8042–8054.

    Article  Google Scholar 

  • Yuan, L., Pan, Z., Ding, D., Sun, S., & Li, W. (2018). A review on chatter in robotic machining process regarding both regenerative and mode coupling mechanism. IEEE-ASME Transactions on Mechatronics,23(5), 2240–2251.

    Article  Google Scholar 

  • Yuan, L., Sun, S., Pan, Z., Ding, D., Gienke, O., & Li, W. (2019). Mode coupling chatter suppression for robotic machining using semi-active magnetorheological elastomers absorber. Mechanical Systems and Signal Processing,117, 221–237.

    Article  Google Scholar 

  • Zeng, Y., Tian, W., Li, D., He, X., & Liao, W. (2017). An error-similarity-based robot positional accuracy improvement method for a robotic drilling and riveting system. International Journal of Advanced Manufacturing Technology,88(9–12), 2745–2755.

    Article  Google Scholar 

  • Zhang, Z., Li, H., Meng, G., Tu, X., & Cheng, C. (2016). Chatter detection in milling process based on the energy entropy of VMD and WPD. International Journal of Machine Tools and Manufacture,108, 106–112.

    Article  Google Scholar 

Download references

Acknowledgements

This work was partially supported by the National Key Research and Development Program of China (Grant Nos. 2017YFB1302601 and 2018YFB1306703).

Author information

Authors and Affiliations

  1. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China

    Jianfeng Tao, Chengjin Qin, Dengyu Xiao, Haotian Shi, Xiao Ling & Chengliang Liu

  2. School of Mechanical Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China

    Bingchu Li

Authors
  1. Jianfeng Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chengjin Qin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Dengyu Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haotian Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xiao Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Bingchu Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chengliang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Chengjin Qin.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Tao, J., Qin, C., Xiao, D. et al. Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method. J Intell Manuf 31, 1243–1255 (2020). https://doi.org/10.1007/s10845-019-01509-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10845-019-01509-5

Keywords

Search

Navigation