Advertisement

Drill Bit Flank Wear Monitoring System in Composite Drilling Process Using Image Processing

  • Raiminor Ramzi
  • Elmi Abu BakarEmail author
  • M. F. Mahmod
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 547)

Abstract

Composite drilling is a hole making operation that is mainly involved in aircraft manufacturing industry. The poor machinability of the composite materials causes the cutting tool to wear faster and increasing the production cost. Ignoring the tool condition would be a bad idea for the production as worn cutting tool tends to damage the highly expensive composite panel of the aircraft. Tool condition monitoring (TCM) is required to keep the process in balance between cost and quality. This paper presents a system to perform tool condition monitoring of a drill bit flank wear using image processing approach. The real industrial sample of carbide drill bit which was used to drill carbon fibre composite panel is obtained directly from the manufacturing assembly line. The images of the drill bit are acquired from the top view for every 100 holes using the developed hardware system. Edge detection is used to detect the boundary of the cutting lips and the images are compared with the reference image of the brand new drill bit using image registration method. The wear rate of the erosion flank wear measured is recorded at average rate of 0.0198% per hole and considered worn at maximum amount of 25.48% wear.

Keywords

Tool condition monitoring (TCM) Drill bit wear measurement Edge detection Image registration 

References

  1. 1.
    Waydande, P., Ambhore, N., Chinchanikar, S.: A review on tool wear monitoring system. J. Mech. Eng Autom. 6(5A), 49–53 (2016)Google Scholar
  2. 2.
    Siddhpura, A., Paurobally, R.: A review of flank wear prediction methods for tool condition monitoring in a turning process. Int. J. Adv. Manuf. Technol. 65(1–4), 371–393 (2013)CrossRefGoogle Scholar
  3. 3.
    Snr, D.D.E.: Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int. J. Mach. Tools Manuf. 40(8), 1073–1098 (2000)Google Scholar
  4. 4.
    Garg, S., Patra, K., Pal, S.K.: Particle swarm optimization of a neural network model. Sadhana 39(3), 533–548 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Huang, C.K., Liao, C.W., Huang, A.P., Tarng, Y.S.: An automatic optical inspection of drill point defects for micro-drilling. Int. J. Adv. Manuf. Technol. 37(11–12), 1133–1145 (2008)CrossRefGoogle Scholar
  6. 6.
    Zhang, W.J., Li, D., Ye, F., Sun, H.: Automatic optical defect inspection and dimension measurement of drill bit. In: 2006 IEEE International Conference on Mechatronics and Automation, pp. 95–100, China (2006)Google Scholar
  7. 7.
    Duan, G., Chen, Y.-W., Sakekawa, T.: Automatic optical inspection of micro drill bit in printed circuit board manufacturing based on pattern classification. In: 2008 IEEE Instrumentation and Measurement Technology Conference, pp. 279–283, Canada (2008)Google Scholar
  8. 8.
    Duan, G., Chen, Y., Sukekawa, T.: Automatic optical phase identification of microdrill bits using active shape models. In: 2009 IEEE Instrumentation and Measurement Technology Conference, pp. 1642–1646, Singapore (2009)Google Scholar
  9. 9.
    Duan, G., Wang, H., Liu, Z., Chen, Y.-W.: A machine learning-based framework for automatic visual inspection of microdrill bits in PCB production. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 42(6), 1679–1689 (2012)CrossRefGoogle Scholar
  10. 10.
    Duan, G., Wang, H., Liu, Z., Tan, J., Chen, Y.-W.: Automatic optical phase identification of micro-drill bits based on improved ASM and bag of shape segment in PCB production. Mach. Vis. Appl. 25(6), 1411–1422 (2014)CrossRefGoogle Scholar
  11. 11.
    Duan, G., Chen, Y.-W., Sukegawa, T.: Automatic optical flank wear measurement of microdrills using level set for cutting plane segmentation. Mach. Vis. Appl. 21(5), 667–676 (2010)CrossRefGoogle Scholar
  12. 12.
    Ramzi, R., Bakar, E.A.: Optical wear inspection of countersink drill bit for drilling operation in aircraft manufacturing and assembly industry: a method. In: IOP Conference Series: Materials Science and Engineering, vol. 370 (2018)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Raiminor Ramzi
    • 1
  • Elmi Abu Bakar
    • 2
    Email author
  • M. F. Mahmod
    • 3
  1. 1.School of Mechanical Engineering, Engineering CampusUniversiti Sains MalaysiaNibong TebalMalaysia
  2. 2.School of Aerospace Engineering, Engineering CampusUniversiti Sains MalaysiaNibong TebalMalaysia
  3. 3.Faculty of Mechanical Engineering and ManufacturingUniversiti Tun Hussein Onn MalaysiaParity Raja, Batu PahatMalaysia

Personalised recommendations