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Online grinding chatter detection based on minimum entropy deconvolution and autocorrelation function

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Abstract

On-line detection of chatter is one of the key techniques to avoid the harmful effects caused by chatter in grinding process. The key to chatter detection is to capture reliable chatter features and thresholds. To achieve this, it is important to make clear and extract the essential characteristics of the grinding chatter signal, which has not yet been well studied. In this paper, we are going to investigate the essential characteristics of the grinding chatter signal and propose a new approach for on-line detection of grinding chatter. The proposed approach for on-line detection of grinding chatter is based on minimum entropy deconvolution and autocorrelation function, in which the minimum entropy deconvolution is employed to deconvolve the effect of transmission path, and further to restore the essential characteristics of the chatter signals. To eliminate the interference of the non-periodic impulse signals in the measured vibration signals, an autocorrelation function is introduced. Kurtosis is employed to indicate chatter according to the changes of the processed signal. The validity of the proposed method is demonstrated through the measured vibration signals obtained from grinding processes and the presented chatter detection index is independent from the grinding conditions with excellent detection accuracy and permissible computational efficiency. This demonstrates the effectiveness of proposed method in on-line implementation.

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Data availability

Our data was acquired from Qinchuan Machine Tool Group Co., Ltd. China. We signed a confidentiality agreement, so we are very sorry that the test data cannot be shared.

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Funding

This research is supported by Xi’an Key Laboratory of Modern Intelligent Textile Equipment (Grant No. 2019220614SYS021CG043), and the Natural Science Foundation of Shaanxi Province, China (Grant No. 2020JQ-820).

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Authors and Affiliations

  1. College of Mechanical and Electrical Engineering, Xi’an Key Laboratory of Modern Intelligent Textile Equipment, Xi’an Polytechnic University, Xi’an, People’s Republic of China

    Dan He

  2. School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, People’s Republic of China

    Zexing Ni & Xiufeng Wang

Authors
  1. Dan He
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  2. Zexing Ni
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  3. Xiufeng Wang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Dan He, Zexing Ni, and Xiufeng Wang. The first draft of the manuscript was written by Dan He and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Dan He.

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He, D., Ni, Z. & Wang, X. Online grinding chatter detection based on minimum entropy deconvolution and autocorrelation function. Int J Adv Manuf Technol 120, 6175–6185 (2022). https://doi.org/10.1007/s00170-022-09137-9

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  • DOI: https://doi.org/10.1007/s00170-022-09137-9

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