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Chatter detection for milling using novel p-leader multifractal features

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Abstract

Chatter in machining results in poor workpiece surface quality and short tool life. An accurate and reliable chatter detection method is needed before its complete development. This paper applies a novel p-leader multifractal formalism for chatter detection in milling processes. This novel formalism can discover internal singularities rising on unstable signals due to chatter without prior knowledge of the natural frequencies of the machining system. The p-leader multifractal features are selected by using a multivariate filter method for feature selection, and verified by both numerical simulations and experimental studies with detailed parameter selection discussions when applying this formalism. The proposed method is assessed in terms of their dynamic monitoring abilities and classification accuracies under wide cutting conditions. The results show that the multifractal features can successfully detect chatter with high accuracies and short computation time. For further verification, the proposed method is compared with two commonly-used methods, which indicates that the proposed method gives better classification accuracies, especially when identifying unstable tests.

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References

  • Abry, P., Jaffard, S., & Wendt, H. (2015). Irregularities and scaling in signal and image processing: multifractal analysis. In Benoit Mandelbrot: A life in many dimensions (pp. 31–116). World Scientific.

  • Abry, P., Wendt, H., Jaffard, S., Helgason, H., Gonçalves, P., Pereira, E., et al. (2010). Methodology for multifractal analysis of heart rate variability: From LF/HF ratio to wavelet leaders. In Paper presented at the 2010 annual international conference of the IEEE engineering in medicine and biology, Buenos Aires, Argentina.

  • Albertelli, P., Braghieri, L., Torta, M., & Monno, M. (2019). Development of a generalized chatter detection methodology for variable speed machining. Mechanical Systems and Signal Processing, 123, 26–42.

    Article  Google Scholar 

  • Altintas, Y., & Budak, E. (1995). Analytical prediction of stability lobes in milling. CIRP Annals—Manufacturing Technology, 44(1), 357–362.

    Article  Google Scholar 

  • Benkedjouh, T., Medjaher, K., Zerhouni, N., & Rechak, S. (2013). Health assessment and life prediction of cutting tools based on support vector regression. Journal of Intelligent Manufacturing, 26(2), 213–223.

    Article  Google Scholar 

  • Bolon-Canedo, V., Sanchez-Marono, N., & Alonso-Betanzos, A. (2013). A review of feature selection methods on synthetic data (review). Knowledge and Information Systems, 34(3), 483–519.

    Article  Google Scholar 

  • Cao, H., Lei, Y., & He, Z. (2013). Chatter identification in end milling process using wavelet packets and Hilbert-Huang transform. International Journal of Machine Tools and Manufacture, 69, 11–19.

    Article  Google Scholar 

  • Chen, Y., Li, H., Hou, L., & Bu, X. (2019). Feature extraction using dominant frequency bands and time-frequency image analysis for chatter detection in milling. Precision Engineering, 56, 235–245.

    Article  Google Scholar 

  • Chen, Y., Li, H., Hou, L., Wang, J., & Bu, X. (2018). An intelligent chatter detection method based on EEMD and feature selection with multi-channel vibration signals. Measurement, 127, 356–365.

    Article  Google Scholar 

  • Chhabra, A. B., Meneveau, C., Jensen, R. V., & Sreenivasan, K. (1989). Direct determination of the f(α) singularity spectrum and its application to fully developed turbulence. Physical Review A, 40(9), 5284.

    Article  Google Scholar 

  • Du, W., Kang, M., & Pecht, M. (2019). Fault diagnosis using adaptive multifractal detrended fluctuation analysis. IEEE Transactions on Industrial Electronics, 67(3), 2272–2282.

    Article  Google Scholar 

  • Du, W., Tao, J., Li, Y., & Liu, C. (2014). Wavelet leaders multifractal features based fault diagnosis of rotating mechanism. Mechanical Systems and Signal Processing, 43(1–2), 57–75.

    Article  Google Scholar 

  • Fu, Y., Zhang, Y., Gao, H., Mao, T., Zhou, H., Sun, R., et al. (2017). 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 

  • Ihlen, E. A. (2012). Introduction to multifractal detrended fluctuation analysis in matlab. Frontiers in Physiology, 3, 141–159.

    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 Engineering, 61(1), 117–141.

    Article  Google Scholar 

  • Jaffard, S., Melot, C., Leonarduzzi, R., Wendt, H., Abry, P., Roux, S. G., et al. (2016). p-exponent and p-leaders, part I: Negative pointwise regularity. Physica A-Statistical Mechanics and its Applications, 448, 300–318.

    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 

  • Lashermes, B., Jaffard, S., & Abry, P. (2005). Wavelet leader based multifractal analysis. In Paper presented at the IEEE international conference on acoustics, speech, and signal processing, Philadelphia, Pennsylvania, USA.

  • Leonarduzzi, R. F., Spilka, J., Wendt, H., Jaffard, S., Torres, M. E., Abry, P., et al. (2015). p-leader based classification of first stage intrapartum fetal HRV. In Paper presented at the VI Latin American congress on biomedical engineering CLAIB 2014, Paraná, Argentina.

  • Leonarduzzi, R. F., Torres, M. E., & Abry, P. (2014). Scaling range automated selection for wavelet leader multifractal analysis. Signal Processing, 105, 243–257.

    Article  Google Scholar 

  • Leonarduzzi, R., Wendt, H., Abry, P., Jaffard, S., Melot, C., Roux, S. G., et al. (2016). p-exponent and p-leaders, part II: Multifractal analysis. Relations to detrended fluctuation analysis. Physica A-Statistical Mechanics and its Applications, 448, 319–339.

    Article  Google Scholar 

  • Li, K., He, S., Li, B., Liu, H., Mao, X., & Shi, C. (2020). A novel online chatter detection method in milling process based on multiscale entropy and gradient tree boosting. Mechanical Systems and Signal Processing, 135, 1–19.

    Google Scholar 

  • Liu, J., Hu, Y., Wu, B., Wang, Y., & Xie, F. (2017). A hybrid generalized hidden Markov model-based condition monitoring approach for rolling bearings. Sensors (Basel), 17(5), 1143–1162.

    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 

  • MathWorks. (2020). Bayesian optimization algorithm. Retrieved June 24, 2020, from https://www.mathworks.com/help/stats/bayesian-optimization-algorithm.html#bvaz8tr-1.

  • Ren, M., Zhang, Q., & Zhang, J. (2019). An introductory survey of probability density function control. Systems Science and Control Engineering, 7(1), 158–170.

    Article  Google Scholar 

  • Tao, J., Qin, C., Xiao, D., Shi, H., Ling, X., Li, B., et al. (2019). Timely chatter identification for robotic drilling using a local maximum synchrosqueezing-based method. Journal of Intelligent Manufacturing, 31, 1243–1255.

    Article  Google Scholar 

  • Wendt, H., Kiyono, K., Abry, P., Hayano, J., Watanabe, E., & Yamamoto, Y. (2014). Multiscale wavelet p-leader based heart rate variability analysis for survival probability assessment in CHF patients. In Paper presented at the 2014 36th annual international conference of the IEEE engineering in medicine and biology society, Chicago, IL, USA.

  • Wendt, H., Roux, S. G., Jaffard, S., & Abry, P. (2009). Wavelet leaders and bootstrap for multifractal analysis of images. Signal Processing, 89(6), 1100–1114.

    Article  Google Scholar 

  • Yao, Z., Mei, D., & Chen, Z. (2010). On-line chatter detection and identification based on wavelet and support vector machine. Journal of Materials Processing Technology, 210(5), 713–719.

    Article  Google Scholar 

  • Yesilli, M. C., Khasawneh, F. A., & Otto, A. (2020). On transfer learning for chatter detection in turning using wavelet packet transform and ensemble empirical mode decomposition. CIRP Journal of Manufacturing Science and Technology, 28, 118–135.

    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 

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Acknowledgements

This study was jointly supported by Natural Science Foundation Council of China (No. 51905461), National Key Research and Development Program of China (No. 2018YFB2001101 and No. 2019YFB2005101). The authors would like to express their acknowledgments to the Advanced Manufacturing Laboratory, UNSW, for the support of the experimental work. Comments and suggestions from reviewers are greatly appreciated.

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Authors and Affiliations

  1. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361005, China

    Yun Chen, Liang Hou, Xiangjian Bu & Shaogan Ye

  2. School of Engineering and Built Environment, Griffith University, Gold Coast Campus, Gold Coast, QLD, 4222, Australia

    Huaizhong Li

  3. School of Electrical Engineering and Automation, Xiamen University of Technology, Xiamen, 361024, China

    Ding Chen

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  1. Yun Chen
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  2. Huaizhong Li
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  3. Liang Hou
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  4. Xiangjian Bu
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  5. Shaogan Ye
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  6. Ding Chen
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Correspondence to Liang Hou.

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Chen, Y., Li, H., Hou, L. et al. Chatter detection for milling using novel p-leader multifractal features. J Intell Manuf 33, 121–135 (2022). https://doi.org/10.1007/s10845-020-01651-5

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