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A chatter detection method in milling based on gray wolf optimization VMD and multi-entropy features

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Abstract

In metal cutting processing, especially in the processing of low-rigidity workpieces, chatter is a crucial factor affecting many aspects such as surface quality, processing efficiency, and tool life. In this paper, a novel online chatter detection method for milling processes is proposed. In this method, firstly, periodic signal and noise parts are filtered by a comb filter and empirical mode decomposition (EMD), respectively. Then, signal reconstruction is performed on the intrinsic mode functions (IMFs) based on the Pearson correlation coefficient. GWO is applied to reconstruct the signal to obtain optimized parameters. Subsequently, the reconstructed signal is decomposed by VMD with the optimal parameters. To obtain rich chatter information frequency bands, the energy entropy characteristics of each order IMF are calculated, and the two-order IMFs with larger energy entropy are selected for reconstruction. Finally, the multi-scale permutation entropy (MPE) and multi-scale fuzzy entropy (MFE) of the reconstructed signal are calculated. According to the value range of entropy in each processing state, the optimal scale feature is selected. The analysis results show that the proposed method can effectively detect the milling processing state based on the optimal scale.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Funding

This work was supported by the Startup Research Fund of Liaoning Petrochemical University(2021XJJL-005).

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Authors and Affiliations

  1. School of Mechanical Engineering, Liaoning Petrochemical University, Fushun, 113001, China

    Bo Liu, Changfu Liu, Yang Zhou & Daohai Wang

Authors
  1. Bo Liu
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  2. Changfu Liu
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  3. Yang Zhou
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  4. Daohai Wang
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Contributions

Bo Liu and Changfu Liu conceived the idea. Daohai Wang and Yang Zhou performed all the experiments. Bo Liu drafted the manuscript, and Bo Liu, Changfu Liu, Daohai Wang, and Yang Zhou interpreted, discussed, and edited the manuscript. Bo Liu finalized the manuscript, including preparing the detailed response letter. Changfu Liu supervised the work.

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Correspondence to Changfu Liu.

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This manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was raw research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

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Liu, B., Liu, C., Zhou, Y. et al. A chatter detection method in milling based on gray wolf optimization VMD and multi-entropy features. Int J Adv Manuf Technol 125, 831–854 (2023). https://doi.org/10.1007/s00170-022-10672-8

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  • DOI: https://doi.org/10.1007/s00170-022-10672-8

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