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Real-time chatter detection based on fast recursive variational mode decomposition

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Abstract

Real-time chatter detection is crucial for avoiding damage to machine tools and workpieces. Time–frequency analysis methods have been extensively adopted in the feature extraction of chatter detection, especially the variational mode decomposition (VMD) has a solid theoretical basis and outstanding decomposition performance. Nevertheless, the performance of VMD is highly dependent on the decomposition parameters (number of modes and penalty factor). In this article, a novel real-time chatter detection method based on fast recursive variational mode decomposition (FRVMD) is proposed. Unlike the optimization algorithm-based VMD, which is time-consuming, the proposed FRVMD extracts the modes one by one in a recursive framework and adaptively adjusts the penalty factor according to the iterative information. FRVMD exhibits higher computational efficiency and better decomposition performance, which is suitable for real-time chatter detection. After the adaptive extraction of chatter-sensitive component by FRVMD, two chatter indicators, namely energy ratio (ER) and dispersion entropy (DE), are introduced to characterize the machining state. Finally, a chatter identification model is established by utilizing the support vector machine (SVM). The simulation and experimental findings verify the effectiveness of the proposed method.

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Funding

This work was supported by the Natural Science Foundation of China under Grant 52375450, Natural Science Foundation of Shandong Province under Grant ZR2022ZD06 and Shandong Provincial Key Research and Development Program (Major Scientific and Technological Innovation Project) under grant 2020CXGC010204.

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Authors and Affiliations

  1. Key Laboratory of High Efficiency and Clean Mechanical Manufacture, Ministry of Education, School of Mechanical Engineering, Shandong University, Jinan, 250061, China

    Yezhong Lu, Haifeng Ma, Qinghua Song & Zhanqiang Liu

  2. National Demonstration Center for Experimental Mechanical Engineering Education, Shandong University, Jinan, 250061, China

    Yezhong Lu, Haifeng Ma, Qinghua Song & Zhanqiang Liu

  3. Shandong Provincial Key Laboratory of Computer Networks, Shandong Computer Science Center (National Supercomputer Center in Jinan), Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250014, People’s Republic of China

    Zhen Zhang

  4. Shandong Industrial Ceramics Research Design Institute Co.,Ltd, Zibo, 255000, China

    Liping Jiang

  5. School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Yuxin Sun

Authors
  1. Yezhong Lu
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  2. Haifeng Ma
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  3. Zhen Zhang
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  6. Qinghua Song
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  7. Zhanqiang Liu
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Contributions

Yezhong Lu was responsible for the design of the algorithm and the analysis and validation of the experimental data. Haifeng Ma was responsible for conducting all the experiments and collecting the data. Yuxin Sun, Zhen Zhang, and Liping Jiang helped with data analysis and visualization. Qinghua Song and Zhanqiang Liu were responsible for the discussion of ideas and methods, review of the manuscript, and financial support.

Corresponding author

Correspondence to Haifeng Ma.

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Appendix

Appendix

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Fig. 16
figure 16

The architecture of the proposed real-time chatter detection method

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Lu, Y., Ma, H., Zhang, Z. et al. Real-time chatter detection based on fast recursive variational mode decomposition. Int J Adv Manuf Technol 130, 3275–3289 (2024). https://doi.org/10.1007/s00170-023-12832-w

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