Skip to main content
Log in

Online detection of run out in microdrilling of tungsten and titanium alloys

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

    We’re sorry, something doesn't seem to be working properly.

    Please try refreshing the page. If that doesn't work, please contact support so we can address the problem.

Abstract

Run out is one of the major problems in microdrilling processes, causing unexpectedly short tool life, sudden breakage, and dimensional inaccuracy. In this study, a two-step monitoring system for run out detection is proposed. The first step uses the fast Fourier transform for extracting features from the online measured force signals. In the second step, a neural network-based model predicts the process condition from the previously obtained features. The model was trained and tested by using force signals obtained from tungsten and titanium alloys, which are widely applied in electronic and aerospace industries. A 0.1-mm-diameter microdrill was used in the experimental study, and three different feed rates were applied for each material. The trained model was validated with data that was not used in the training process. In this validation, the system was able to detect more than 70 % of the run out conditions with less than 10 % of false detections. For microdrills, detecting and reducing run out can yield considerable gains in tool life and productivity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Krimpenis AA, Fountas NA, Ntalianis I, Vaxevanidis NM (2014) CNC micromilling properties and optimization using genetic algorithms. Int J Adv Manuf Technol 70:157–171. doi:10.1007/s00170-013-5248-7

    Article  Google Scholar 

  2. Watanabe H, Tsuzaka H, Masuda M (2008) Microdrilling for printed circuit boards (PCBs)—influence of radial run out of microdrills on hole quality. Precis Eng 32:329–335. doi:10.1016/j.precisioneng.2008.02.004

    Article  Google Scholar 

  3. Kudla LA (2006) Deformations and strength of miniature drills. Proc Inst Mech Eng B J Eng 220:389–396. doi:10.1243/095440505X69346

    Article  Google Scholar 

  4. Beruvides G, Quiza R, del Toro R, Haber RE (2013) Sensoring systems and signal analysis to monitor tool wear in microdrilling operations on a sintered tungsten–copper composite material. Sens Actuators A Phys 199:165–175. doi:10.1016/j.sna.2013.05.021

    Article  Google Scholar 

  5. Imran M, Mativenga PT, Gholinia A, Withers PJ (2011) Evaluation of surface integrity in micro drilling process for nickel-based superalloy. Int J Adv Manuf Technol 55:465–476. doi:10.1007/s00170-010-3062-z

    Article  Google Scholar 

  6. Tran NK, Lam YC, Yue CY, Tan M-J (2012) Evaluation of roughness, hardness, and strength of AA 6061 molds for manufacturing polymeric microdevices. Int J Adv Manuf Technol 60:1215–1221. doi:10.1007/s00170-011-3673-z

    Article  Google Scholar 

  7. Yao X, Zhang Y, Li B, Zhang Z, Shen X (2013) Machining force control with intelligent compensation. Int J Adv Manuf Technol 69:1701–1715. doi:10.1007/s00170-013-5136-1

    Article  Google Scholar 

  8. Malekian M, Park SS, Jun MB (2009) Tool wear monitoring of micro-milling operations. J Mater Process Technol 209:4903–4914. doi:10.1016/j.jmatprotec.2009.01.013

    Article  Google Scholar 

  9. Fu L, Ling SF, Tseng CH (2007) On-line breakage monitoring of small drills with input impedance of driving motor. Mech Syst Signal Process 21:457–465. doi:10.1016/j.ymssp.2005.04.004

    Article  Google Scholar 

  10. Sick B (2002) On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mech Syst Signal Process 16:487–546. doi:10.1006/mssp.2001.1460

    Article  Google Scholar 

  11. Zhu K, Wong YS, Hong GS (2009) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results. Int J Mach Tool Manuf 49:537–553. doi:10.1016/j.ijmachtools.2009.02.003

    Article  Google Scholar 

  12. Li X (2001) Detection of tool flute breakage in end milling using feed-motor current signatures. IEEE-ASME Trans Mech 6:491–498

    Article  Google Scholar 

  13. Kim DW, Lee YS, Park MS, Chu CN (2009) Tool life improvement by peck drilling and thrust force monitoring during deep-micro-hole drilling of steel. Int J Mach Tool Manuf 49:246–255. doi:10.1016/j.ijmachtools.2008.11.005

    Article  Google Scholar 

  14. Suprock CA, Roth JT (2007) Methods for on-line directionally independent failure prediction of end milling cutting tools. Mach Sci Technol 11:1–43. doi:10.1080/10910340601174806

    Article  Google Scholar 

  15. Kondo E, Shimana K (2012) Monitoring of prefailure phase and detection of tool breakage in micro-drilling operations. Procedia CIRP 1:581–586. doi:10.1016/j.procir.2012.05.003

    Article  Google Scholar 

  16. Patra K, Pal SK, Bhattacharyya K (2007) Application of wavelet packet analysis in drill wear monitoring. Mach Sci Technol 11:413–432. doi:10.1080/10910340701539908

    Google Scholar 

  17. Shi D, Gindy NN (2007) Development of an online machining process monitoring system: application in hard turning. Sensors Actuators A Phys 135:405–414. doi:10.1016/j.sna.2006.08.011

    Article  Google Scholar 

  18. Ganesan R (2008) Real-time monitoring of complex sensor data using wavelet-based multiresolution analysis. Int J Adv Manuf Technol 39:543–558. doi:10.1007/s00170-007-1237-z

    Article  Google Scholar 

  19. Chen Z, Zhang X (2005) Monitoring of tool wear using feature vector selection and linear regression. 1st ICNC, Springer, Changsha, China. doi: 10.1007/11539117_1

  20. Palanisamy P, Rajendran I, Shanmugasundaram S (2007) Prediction of tool wear using regression and ANN models in end-milling operation. Int J Adv Manuf Technol 37:29–41. doi:10.1007/s00170-008-1758-0

    Article  Google Scholar 

  21. Kuo RJ (2000) Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network. Eng Appl Artif Intell 13:29–261. doi:10.1016/S0952-1976(00)00008-7

    Article  Google Scholar 

  22. Ren Q, Balazinski M, Baron L, Jemielniak K (2011) TSK fuzzy modeling for tool wear condition in turning processes: an experimental study. Eng Appl Artif Intell 24:260–265. doi:10.1016/j.engappai.2010.10.016

    Article  Google Scholar 

  23. Sokolowski A (2004) On some aspects of fuzzy logic application in machine monitoring and diagnostics. Eng Appl Artif Intell 17:429–437. doi:10.1007/s10845-012-0623-z

    Article  Google Scholar 

  24. Kassim AA, Mian Z, Mannan MA (2006) Tool condition classification using hidden Markov model based on fractal analysis of machined surface textures. Mach Vis Appl 17:327–336. doi:10.1007/s00138-007-0085-z

    Article  Google Scholar 

  25. Liao TW, Hua G, Qu J, Blau PJ (2006) Grinding wheel condition monitoring with hidden Markov model-based clustering methods. Mach Sci Technol 10:511–538. doi:10.1080/10910340600996175

    Article  Google Scholar 

  26. Zhu K, Wong YS, Hong GS (2009) Multi-category micro-milling tool wear monitoring with continuous hidden Markov models. Mech Syst Signal Process 23:547–560. doi:10.1016/j.ymssp.2008.04.010

    Article  Google Scholar 

  27. Wang G, Feng X (2013) Tool wear state recognition based on linear chain conditional random field model. Eng Appl Artif Intell 26:1421–1427. doi:10.1016/j.engappai.2012.10.015

    Article  Google Scholar 

  28. Panda SS, Chakraborty D, Pa SK (2007) Monitoring of drill flank wear using fuzzy back-propagation neural network. Int J Adv Manuf Technol 34:227–235. doi:10.1007/s00170-006-0589-0

    Article  Google Scholar 

  29. Huang CK, Wang LG, Tang HC, Tarng YS (2006) Automatic laser inspection of outer diameter, run out and taper of micro-drills. J Mater Process Technol 171:306–313. doi:10.1016/j.jmatprotec.2005.06.085

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Gerardo Beruvides.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Beruvides, G., Quiza, R., Rivas, M. et al. Online detection of run out in microdrilling of tungsten and titanium alloys. Int J Adv Manuf Technol 74, 1567–1575 (2014). https://doi.org/10.1007/s00170-014-6091-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-014-6091-1

Keywords

Navigation