Skip to main content

Tool-wear monitoring during micro-end milling using wavelet packet transform and Fisher’s linear discriminant


Tool wear is one of the most important parameters in micro-end milling, and can be used to monitor the condition of the machine and the tool. A micro-end mill has different characteristics from a macro-scale end mill; in particular, shank run-out (which is negligible in the macro-scale tool due to the low aspect ratio) is significant in micro-end milling, inducing excessive tool wear and reduced tool life and leading to sudden, premature failure. In this paper, a novel tool-wear monitoring method is described for determining the state of a micro-end mill using wavelet packet transforms and Fisher’s linear discriminant. Force and torque signals were measured using a dynamometer and were used to reflect geometric changes in the micro-end mill due to wear. Because of the small signal-to-noise ratio, sensor signals measured during the milling process were periodically averaged, and the resulting single-period signals provided improved efficiency of feature extraction using wavelet packet transforms. The extracted features were classified in the wavelet domain and used to determine the tool state employing a hidden Markov model. The recognition results were compared with those of an energy-based monitoring technique, and we found that our method could determine the tool state more accurately for both normal wear and premature failure of micro-end mills.

This is a preview of subscription content, access via your institution.


  1. 1.

    Pham, M.-Q., Yoon, H.-S., Khare, V., and Ahn, S.-H., “Evaluation of Ionic Liquids as Lubricants in Micro Milling-Process Capability and Sustainability,” Journal of Cleaner Production, Vol. 76, pp. 167–173, 2014.

    Article  Google Scholar 

  2. 2.

    Ritou, M., Garnier, S., Furet, B., and Hascoet, J.-Y., “A New Versatile In-Process Monitoring System for Milling,” International Journal of Machine Tools and Manufacture, Vol. 46, No. 15, pp. 2026–2035, 2006.

    Article  Google Scholar 

  3. 3.

    Shao, H., Shi, X., and Li, L., “Power Signal Separation in Milling Process based on Wavelet Transform and Independent Component Analysis,” International Journal of Machine Tools and Manufacture, Vol. 51, No. 9, pp. 701–710, 2011.

    Article  Google Scholar 

  4. 4.

    Yoon, H.-S., Lee, J.-Y., Kim, M.-S., and Ahn, S.-H., “Empirical Power-Consumption Model for Material Removal in Three-Axis Milling,” Journal of Cleaner Production, Vol. 78, pp. 54–62, 2014.

    Article  Google Scholar 

  5. 5.

    Yoon, H.-S., Kim, E.-S., Kim, M.-S., Lee, J.-Y., Lee, G.-B., and Ahn, S.-H., “Towards Greener Machine Tools-A Review on Energy Saving Strategies and Technologies,” Renewable and Sustainable Energy Reviews, Vol. 48, pp. 870–891, 2015.

    Article  Google Scholar 

  6. 6.

    Yoon, H.-S., Wu, R., Lee, T.-M., and Ahn, S.-H., “Geometric Optimization of Micro Drills using Taguchi Methods and Response Surface Methodology,” Int. J. Precis. Eng. Manuf., Vol. 12, No. 5, pp. 871–875, 2011.

    Article  Google Scholar 

  7. 7.

    Yoon, H.-S., Lee, J.-Y., Kim, H.-S., Kim, M.-S., Kim, E.-S., et al., “A Comparison of Energy Consumption in Bulk Forming, Subtractive, and Additive Processes: Review and Case Study,” Int. J. Precis. Eng. Manfu.-Green Tech., Vol. 1, No. 3, pp. 261–279, 2014.

    Article  Google Scholar 

  8. 8.

    Kim, J.-W., Yoon, H.-S., Lee, H.-S., Lee, K.-E., and Ahn, S.-H., “Defects of Wave Patterns from Tungsten Carbide/Stainless Steel Brazed Micro-End-Milling for Printed Circuit Board Machining,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 227, No. 11, pp. 1743–1747, 2013.

    Article  Google Scholar 

  9. 9.

    Yoon, H.-S., Moon, J.-S., Pham, M.-Q., Lee, G.-B., and Ahn, S.-H., “Control of Machining Parameters for Energy and Cost Savings in Micro-Scale Drilling of PCBs,” Journal of Cleaner Production, Vol. 54, pp. 41–48, 2013.

    Article  Google Scholar 

  10. 10.

    Kim, J.-W., Yoon, H.-S., and Ahn, S.-H., “Effect of Repeated Insertions into a Mesoscale Pinhole Assembly: Case of Interference Fit,” Int. J. Precis. Eng. Manuf., Vol. 14, No. 9, pp. 1651–1654, 2013.

    Article  Google Scholar 

  11. 11.

    Marinescu, I. and Axinte, D. A., “A Critical Analysis of Effectiveness of Acoustic Emission Signals to Detect Tool and Workpiece Malfunctions in Milling Operations,” International Journal of Machine Tools and Manufacture, Vol. 48, No. 10, pp. 1148–1160, 2008.

    Article  Google Scholar 

  12. 12.

    Jemielniak, K., Kossakowska, J., and Urbañski, T., “Application of Wavelet Transform of Acoustic Emission and Cutting Force Signals for Tool Condition Monitoring in Rough Turning of Inconel 625,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 225, No. 1, pp. 123–129, 2011.

    Google Scholar 

  13. 13.

    Yun, H. T., Heo, S., Lee, M. K., Min, B.-K., and Lee, S. J., “Ploughing Detection in Micromilling Processes using the Cutting Force Signal,” International Journal of Machine Tools and Manufacture, Vol. 51, No. 5, pp. 377–382, 2011.

    Article  Google Scholar 

  14. 14.

    Wang, L., Mehrabi, M. G., and Kannatey-Asibu, E., “Hidden Markov Model-based Tool Wear Monitoring in Turning,” Journal of Manufacturing Science and Engineering, Vol. 124, No. 3, pp. 651–658, 2002.

    Article  Google Scholar 

  15. 15.

    Bai, Q. S., Yang, K., Liang, Y. C., Yang, C. L., and Wang, B., “Tool Runout Effects on Wear and Mechanics Behavior in Microend Milling,” Journal of Vacuum Science & Technology B, Vol. 27, No. 3, pp. 1566–1572, 2009.

    Article  Google Scholar 

  16. 16.

    Miyaguchi, T., Masuda, M., Takeoka, E., and Iwabe, H., “Effect of Tool Stiffness Upon Tool Wear in High Spindle Speed Milling using Small Ball End Mill,” Precision engineering, Vol. 25, No. 2, pp. 145–154, 2001.

    Article  Google Scholar 

  17. 17.

    Stephenson, D. A. and Agapiou, J. S., “Metal Cutting Theory and Practice,” CRC Press, pp. 313–330, 2006.

    Google Scholar 

  18. 18.

    Lee, K., Ahn, S. H., Dornfeld, D. A., and Wright, P. K., “The Effect of Run-Out on Design for Manufacturing in Micro-Machining Process,” Proc. of the ASME International Mechanical Engineering Congress and Exposition, New York, USA, 2001.

    Google Scholar 

  19. 19.

    Mallat, S. G., “A Wavelet Tour of Signal Processing,” Academic Press, pp. 13–15, 1999.

    Google Scholar 

  20. 20.

    Zhu, K., Wong, Y. S., and Hong, G. S., “Wavelet Analysis of Sensor Signals for Tool Condition Monitoring: A Review and Some New Results,” International Journal of Machine Tools and Manufacture, Vol. 49, No. 7, pp. 537–553, 2009.

    Article  Google Scholar 

  21. 21.

    Rabiner, L. R. and Juang, B.-H., “An Introduction to Hidden Markov Models,” IEEE ASSP Magazine, Vol. 3, No. 1, pp. 4–16, 1986.

    Article  Google Scholar 

  22. 22.

    Zhu, K., San Wong, Y. S., and Hong, G. S., “Multi-Category Micro-Milling Tool Wear Monitoring with Continuous Hidden Markov Models,” Mechanical Systems and Signal Processing, Vol. 23, No. 2, pp. 547–560, 2009.

    Article  Google Scholar 

  23. 23.

    Rabiner, L. R., “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proceedings of the IEEE, Vol. 77, No. 2, pp. 257–286, 1989.

    Article  Google Scholar 

  24. 24.

    Bicego, M., Pe, E., Tax, D. M. J., and Duin, R. P. W., “Componentbased Discriminative Classification for Hidden Markov Models,” Pattern Recognition, Vol. 42, No. 11, pp. 2637–2648, 2009.

    Article  MATH  Google Scholar 

  25. 25.

    Ertunc, H. M., Loparo, K. A., and Ocak, H., “Tool Wear Condition Monitoring in Drilling Operations using Hidden Markov Models (HMMs),” International Journal of Machine Tools and Manufacture, Vol. 41, No. 9, pp. 1363–1384, 2001.

    Article  Google Scholar 

  26. 26.

    Hong, Y.-S., Ahn, S.-H., Song, C.-K., and Cho, Y.-M., “Component-Level Fault Diagnostics of a Bevel Gear using a Wavelet Packet Transform,” Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering, Vol. 225, No. 1, pp. 1–12, 2011.

    Article  Google Scholar 

Download references

Author information



Corresponding authors

Correspondence to Young-Man Cho or Sung-Hoon Ahn.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hong, YS., Yoon, HS., Moon, JS. et al. Tool-wear monitoring during micro-end milling using wavelet packet transform and Fisher’s linear discriminant. Int. J. Precis. Eng. Manuf. 17, 845–855 (2016).

Download citation


  • Condition monitoring
  • Hidden Markov model
  • Micro end mill
  • Single-period signal
  • Tool lifespan
  • Wavelet packet transform