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Weak signal enhancement for small drill condition monitoring in PCB drilling process by using adaptive multistable stochastic resonance

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Abstract

The online tool condition monitoring is demanded to detect the tool wear and to ensure the hole drilling process of printed circuit boards (PCB) goes on smoothly. However, due to the impact of ambient noise caused by the limited size of the small drill and the laminated material of PCB, the tool wear signal features are too weak to extract. The stochastic resonance (SR) method has been proven to be effective in enhancing weak signals among various weak signal extractions. In this paper, an adaptive multistable stochastic resonance is presented to improve the performance of the SR method and process the tool wear signals for PCB drilling. The differential evolution (DE) algorithm is applied to adaptively optimize potential parameters and compensation factor, which makes the SR method suitable for high-frequency signals. Moreover, tool wear experiments with different drill wear are carried out to verify the effectiveness of the proposed method. The results indicate that the proposed method improves the signal-to-noise ratio and has great potential in enhancing weak signals for small drill condition monitoring in the PCB drilling process.

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Availability of data and material

The datasets used or analyzed during the current study are available from the corresponding author or the first author on reasonable request.

Code availability

The codes used during the current study are available from the corresponding author or the first author on reasonable request.

References

  1. Zheng LJ, Wang CY, Song YX, Yang LP, Qu YP, Ma P, Fu LY (2011) A review on drilling printed circuit boards. Adv Mater Res 188:441–449. https://doi.org/10.4028/www.scientific.net/amr.188.441

    Article  Google Scholar 

  2. Shi H, Liu X, Lou Y (2018) Materials and micro drilling of high frequency and high speed printed circuit board: a review. Int J Adv Manuf Technol 100:827–841. https://doi.org/10.1007/s00170-018-2711-5

    Article  Google Scholar 

  3. Hasan M, Zhao J, Jiang Z (2017) A review of modern advancements in micro drilling techniques. J Manuf Process 29:343–375. https://doi.org/10.1016/j.jmapro.2017.08.006

    Article  Google Scholar 

  4. Gierlak P, Burghardt A, Szybicki D, Szuster M, Muszyńska M (2017) On-line manipulator tool condition monitoring based on vibration analysis. Mech Syst Signal Process 89:14–26. https://doi.org/10.1016/j.ymssp.2016.08.002

    Article  Google Scholar 

  5. Uekita M, Takaya Y, Uekita M, Takaya Y (2017) Tool condition monitoring technique for deep-hole drilling of large components based on chatter identification in time–frequency domain. Measurement 103:199–207. https://doi.org/10.1016/j.measurement.2017.02.035

    Article  Google Scholar 

  6. Yau H, Wang C, Chang J, Su X (2019) A study on the application of synchronized chaotic systems of different fractional orders for cutting tool wear diagnosis and identification. IEEE Access 7:15903–15911. https://doi.org/10.1109/ACCESS.2019.2894815

    Article  Google Scholar 

  7. Huang Y, Huang C, Ding J, Liu Z (2020) Fault diagnosis on railway vehicle bearing based on fast extended singular value decomposition packet. Measurement 152:107277. https://doi.org/10.1016/j.measurement.2019.107277

    Article  Google Scholar 

  8. Tarek K, Abderrazek D, Khemissi BM, Cherif DM, Lilia C, Nouredine O (2020) Comparative study between cyclostationary analysis, EMD, and CEEMDAN for the vibratory diagnosis of rotating machines in industrial environment. Int J Adv Manuf Technol 109:2747–2775. https://doi.org/10.1007/s00170-020-05848-z

    Article  Google Scholar 

  9. An X, Zeng H, Li C (2016) Demodulation analysis based on adaptive local iterative filtering for bearing fault diagnosis. Measurement 94:554–560. https://doi.org/10.1016/j.measurement.2016.08.039

    Article  Google Scholar 

  10. Guo W, Tse PW, Djordjevich A (2012) Faulty bearing signal recovery from large noise using a hybrid method based on spectral kurtosis and ensemble empirical mode decomposition. Measurement 45:1308–1322. https://doi.org/10.1016/j.measurement.2012.01.001

    Article  Google Scholar 

  11. He W, Zi Y, Chen B, Wu F, He Z (2015) Automatic fault feature extraction of mechanical anomaly on induction motor bearing using ensemble super-wavelet transform. Mech Syst Signal Process 54–55:457–480. https://doi.org/10.1016/j.ymssp.2014.09.007

    Article  Google Scholar 

  12. Wang T, Han Q, Chu F, Feng Z (2019) Vibration based condition monitoring and fault diagnosis of wind turbine planetary gearbox: a review. Mech Syst Signal Process 126:662–685. https://doi.org/10.1016/j.ymssp.2019.02.051

    Article  Google Scholar 

  13. Qiao Z, Lei Y, Li N (2019) Applications of stochastic resonance to machinery fault detection: a review and tutorial. Mech Syst Signal Process 122:502–536. https://doi.org/10.1016/j.ymssp.2018.12.032

    Article  Google Scholar 

  14. Wang H, Chen J, Zhou Y, Ni G (2020) Early fault diagnosis of rolling bearing based on noise-assisted signal feature enhancement and stochastic resonance for intelligent manufacturing. Int J Adv Manuf Technol 107:1017–1023. https://doi.org/10.1007/s00170-019-04333-6

    Article  Google Scholar 

  15. Lu S, Zheng P, Liu Y, Cao Z, Yang H, Wang Q (2019) Sound-aided vibration weak signal enhancement for bearing fault detection by using adaptive stochastic resonance. J Sound Vib 449:18–29. https://doi.org/10.1016/j.jsv.2019.02.028

    Article  Google Scholar 

  16. Lei Y, Han D, Lin J, He Z (2013) Planetary gearbox fault diagnosis using an adaptive stochastic resonance method. Mech Syst Signal Process 38:113–124. https://doi.org/10.1016/j.ymssp.2012.06.021

    Article  Google Scholar 

  17. Zhang H, He Q, Lu S, Kong F (2014) Stochastic resonance with a joint Woods-Saxon and gaussian potential for bearing fault diagnosis. Math Probl Eng 2014:1–17. https://doi.org/10.1155/2014/315901

    Article  Google Scholar 

  18. Qiao Z, Lei Y, Lin J, Jia F (2017) An adaptive unsaturated bistable stochastic resonance method and its application in mechanical fault diagnosis. Mech Syst Signal Process 84:731–746. https://doi.org/10.1016/j.ymssp.2016.08.030

    Article  Google Scholar 

  19. Li J, Chen X, He Z (2013) Multi-stable stochastic resonance and its application research on mechanical fault diagnosis. J Sound Vib 332:5999–6015. https://doi.org/10.1016/j.jsv.2013.06.017

    Article  Google Scholar 

  20. Zheng Y, Huang M, Lu Y, Li W (2020) Fractional stochastic resonance multi-parameter adaptive optimization algorithm based on genetic algorithm. Neural Comput Appl 32:16807–16818. https://doi.org/10.1007/s00521-018-3910-6

    Article  Google Scholar 

  21. Zhang X, Miao Q, Liu Z, He Z (2017) An adaptive stochastic resonance method based on grey wolf optimizer algorithm and its application to machinery fault diagnosis. ISA Trans 71:206–214. https://doi.org/10.1016/j.isatra.2017.08.009

    Article  Google Scholar 

  22. Jiao SB, Jie K, Zhang Q (2016) Weak impact signal detection based on adaptive stochastic resonance with knowledge-based particle swarm optimization. 2015 11th International Conference on Natural Computation (ICNC). IEEE. https://doi.org/10.1109/ICNC.2015.7378008

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Funding

This research is supported by the Innovation Research Fund of State Key Laboratory of Tribology, Tsinghua University [grant number SKLT2020C12].

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Authors and Affiliations

  1. Beijing Key Lab of Precision/Ultra-Precision Manufacturing Equipments and Control, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China

    Qifeng Tan, Guodong Liu, Yong Li & Hao Tong

  2. State Key Laboratory of Tribology, Tsinghua University, Beijing, 100084, China

    Qifeng Tan, Guodong Liu, Yong Li & Hao Tong

Authors
  1. Qifeng Tan
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  2. Guodong Liu
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  3. Yong Li
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  4. Hao Tong
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Contributions

Qifeng Tan: investigation, conceptualization, methodology, formal analysis, data curation, validation, software, writing – original draft. Guodong Liu: investigation, conceptualization, funding acquisition, project administration, writing – editing. Yong Li: funding acquisition, project administration, writing – reviewing and editing. Hao Tong: investigation, formal analysis, validation, software.

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Correspondence to Yong Li.

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The authors declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work. This manuscript has not been published or presented elsewhere in part or in entirety and is not under consideration by another journal. I have read and understood your journal’s policies, and I believe that neither the manuscript nor the study violates any of these.

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Tan, Q., Liu, G., Li, Y. et al. Weak signal enhancement for small drill condition monitoring in PCB drilling process by using adaptive multistable stochastic resonance. Int J Adv Manuf Technol 120, 2075–2087 (2022). https://doi.org/10.1007/s00170-022-08915-9

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  • DOI: https://doi.org/10.1007/s00170-022-08915-9

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