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Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach

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Abstract

Tool wear monitoring is deemed as an essential technology of the intelligent manufacturing to guarantee the processing quality and improve the machining efficiency. In this paper, a prediction model based on adaptive particle swarm optimization (APSO) algorithm and least squares support vector machine (LS-SVM) algorithm is proposed for the recognition of drill wear. Cutting force signal and vibration signal are used for tool wear monitoring. And these signals are preprocessed through wavelet threshold de-noising algorithm. Multiple signal feature extraction methods are carried out to process the sample data related to drill wear status. The mean absolute error of the tool wear recognition model is 0.91%, better than the standard LS-SVM algorithm under the same condition.

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Funding

This work was supported by the NSFC of China (No. 51975288, No. 51905270) and NSF of Jiangsu Province (No. BK20180435).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Precision and Micro-Manufacturing Technology, Nanjing University of Aeronautics and Astronautics, Yudao Str. 29, Nanjing, 210016, People’s Republic of China

    Ni Chen, Bijun Hao, Yuelong Guo, Liang Li, M. Aqib Khan & Ning He

Authors
  1. Ni Chen
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  2. Bijun Hao
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  3. Yuelong Guo
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  4. Liang Li
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  5. M. Aqib Khan
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  6. Ning He
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Correspondence to Liang Li.

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Highlights

• The model based on APSO-LS-SVM is established to recognize the tool wear.

• Cutting force signal and vibration signal were used for tool wear detection.

• Wavelet threshold de-noising method was selected to preprocess the signals.

• Time, frequency domain and harmonic wavelet packet analysis are implemented.

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Chen, N., Hao, B., Guo, Y. et al. Research on tool wear monitoring in drilling process based on APSO-LS-SVM approach. Int J Adv Manuf Technol 108, 2091–2101 (2020). https://doi.org/10.1007/s00170-020-05549-7

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  • DOI: https://doi.org/10.1007/s00170-020-05549-7

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