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Condition monitoring of deep-hole drilling process based on improved empirical wavelet de-noising and high multiple frequency components of rotation frequency

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Abstract

The machining condition monitoring is an important task to improve product quality and avoid economic losses in deep-hole drilling. Out-of-roundness-tolerance is one of major defects in deep-hole drilling process. In this work, the spindle vibration signals generated in deep-hole drilling process are studied to seek the characteristics which can reveal the roundness error change. Considering the influence of background noise on monitoring effect and the sensitivity of high multiple frequency components of spindle vibration signal towards the roundness error change, an improved empirical wavelet de-noising method is proposed to remove the noise of the spindle vibration signal. The high multiple frequency components of the rotation frequency are extracted from the de-noised spindle vibration signal. Firstly, the spindle vibration signal is decomposed by empirical wavelet transform into a set of wavelet coefficients, and then an improved thresholding function is proposed to process the Fourier spectrum of wavelet coefficients for removing the noise from the wavelet coefficients. Secondly, the wavelet coefficients are reconstructed based on the de-noised wavelet coefficients, and the wavelet coefficients which contain high multiple components of rotation frequency are selected. Finally, the energy entropy of the selected wavelet coefficients is calculated as a roundness error monitoring indicator to detect the roundness error of deep hole. The proposed method is verified with well-designed deep-hole drilling tests. The test results show that the monitoring indicator can effectively reflect the roundness error change in deep-hole drilling process.

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Availability of data and materials

The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.

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Funding

This research is supported financially by the National Natural Science Foundation of China (Grant No. 51905421) and China Postdoctoral Science Foundation (Grant No. 2019M653880XB) and 2019 Natural Science Project of Shaanxi Provincial Department of Education (19JK0586).

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Authors and Affiliations

  1. School of Mechanical and Instrumental Engineering, Xi’an University of Technology, Xi’an, 710048, Shaanxi, People’s Republic of China

    Yue Si, Lingfei Kong & Jianming Zheng

  2. State Key Laboratory for Manufacturing and Systems Engineering, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China

    Zhousuo Zhang

Authors
  1. Yue Si
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  2. Zhousuo Zhang
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  3. Lingfei Kong
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  4. Jianming Zheng
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Contributions

Yue Si: methodology; investigation; formal analysis; writing, original draft; writing, review and editing. Zhousuo Zhang: conceptualization, methodology. Lingfei Kong: supervision, resources, project administration, funding acquisition. Jianming Zheng: supervision, resources, data curation.

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Correspondence to Yue Si.

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Si, Y., Zhang, Z., Kong, L. et al. Condition monitoring of deep-hole drilling process based on improved empirical wavelet de-noising and high multiple frequency components of rotation frequency. Int J Adv Manuf Technol 114, 2201–2214 (2021). https://doi.org/10.1007/s00170-021-06965-z

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  • DOI: https://doi.org/10.1007/s00170-021-06965-z

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