Online Monitoring of Micro-Hole Drilling Based on Data-Driven Force Analysis

  • YanHong Sun
  • Yaxin Cui


Due to the high quality, high efficiency, and low-processing cost, micro-hole drilling is widely used, but it is difficult to solve the problem that the micro-drill is low in strength, poor in rigidity, and is easy to break. Based on a large number of drilling experimental data, it was found that the main indictor for micro-drill fracture was increased drilling force as drill wear progressed. Since the drilling force signals can accurately represent the degree of wear of the micro-drill. It is proposed to establish a wavelet fuzzy neural network monitoring model based on data-driven drilling force analysis. The model integrates wavelet analysis technology, neural network decision technology, and fuzzy control technology, so that the monitoring system can simultaneously have low-level learning, computing power, fuzzy system’s high-level reasoning, and decision-making ability of the neural network. Through the offline network training and testing of a large number of experimental data, the robust monitoring model is obtained, and the online monitoring of micro-hole drilling is realized. The results show that it is feasible to use the wavelet fuzzy neural network for the online monitoring and interpretation of micro-hole drilling force, and by the appropriate selection of the monitoring threshold, micro-drill fracture can more effectively be avoided.


Micro-hole drilling Data-driven force analysis Wavelet analysis Fuzzy control Neural network Online monitoring 


  1. 1.
    K. Mori, N. Kasashima, J.C. Fu, K. Muto, Prediction of small drill bit breakage by wavelet transforms and linear discriminant functions. Int. J. Mach. Tools Manuf. 39(9), 1471–1484 (1999)CrossRefGoogle Scholar
  2. 2.
    L. Fu, S.-F. Ling, Neural network based on-line detection of drill breakage in micro drilling process. Neural Information Processing, 2002. ICONIP’02. Proceedings of the 9th International Conference on, vol. 4, pp. 2054–2058. IEEE, 2002Google Scholar
  3. 3.
    P. Bandyopadhyay, E.M. Gonzalez, R. Huang, S.M. Wu, A feasibility study of on-line drill wear monitoring by DDS methodology. Int. J. Mach. Tool Des. Res. 26(3), 245–257 (1986)CrossRefGoogle Scholar
  4. 4.
    S.S. Panda, D. Chakraborty, S.K. Pal, Monitoring of drill flank wear using fuzzy back-propagation network. Int. J. Adv. Manuf. Technol. 34, 227–235 (2007)CrossRefGoogle Scholar
  5. 5.
    M.S. Cheong, D.-W. Cho, K.F. Ehmann, Identification and control for micro-drilling productivity enhancement. Int. J. Mach. Tools Manuf. 39(10), 1539–1561 (1999)CrossRefGoogle Scholar
  6. 6.
    H.M. Ertunc, C. Oysu, Drill wear monitoring using cutting force signals. Mechatronics 14(5), 533–548 (2004)CrossRefGoogle Scholar
  7. 7.
    B. Brophy, K. Kelly, G. Byrne, AI-based condition monitoring of the drilling process. J. Mater. Process. Technol. 124(3), 305–310 (2002)CrossRefGoogle Scholar
  8. 8.
    D.E. Dimla Jr., P.M. Lister, N.J. Leighton, Neural network solutions to the tool condition monitoring problem in metal cutting—a critical review of methods. Int. J. Mach. Tools Manuf. 37(9), 1219–1241 (1997)CrossRefGoogle Scholar
  9. 9.
    I.N. Tansel, C. Mekdeci, O. Rodriguez, B. Uragun, Monitoring drill conditions with wavelet based encoding and neural networks. Int. J. Mach. Tools Manuf. 33(4), 559–575 (1993)CrossRefGoogle Scholar
  10. 10.
    I.N. Tansel, Monitoring microdrilling operations with an intelligent diagnostic system. J. Acoust. Soc. Am. 91(4), 2358–2358 (1992)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.College of Mechanical EngineeringJilin Engineering Normal UniversityChangchunChina

Personalised recommendations