Application of backpropagation neural network for spindle vibration-based tool wear monitoring in micro-milling

  • Wan-Hao Hsieh
  • Ming-Chyuan LuEmail author
  • Shean-Juinn Chiou


This study develops a micro-tool condition monitoring system consisting of accelerometers on the spindle, a data acquisition and signal transformation module, and a backpropagation neural network. This study also discusses the effect of the sensor installations, selected features, and the bandwidth size of the features on the classification rate. To collect the vibration signals necessary for training the system model and verifying the system, an experiment was implemented on a micro-milling research platform along with a 700 μm diameter micro-end mill and a SK2 workpiece. A three-axis accelerometer was installed on a sensor plate attached to the spindle housing to collect vibration signals in three directions during cutting. The frequency domain features representing changes in tool wear were selected based on the class mean scatter criteria after transforming signals from the time domain to the frequency domain by fast Fourier transform. Using the appropriate vibration features, this study develops and tests a backpropagation neural network classifier. Results show that proper feature extraction for classification provides a better solution than applying all spectral features into the classifier. Selecting five features for classification provides a better classification rate than the case with four and three features along with the 30 Hz bandwidth size of the spectral feature. Moreover, combining the signals for tool condition from both direction signals provides a better classification rate than determining the tool condition using a one-direction single sensor.


Tool wear Microcutting Monitoring Neural network Vibration 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This research was supported in part by the Taiwan Department of Economics under Grant 97-EC-17-A-05-S1-101.


  1. 1.
    Masuzawa T (2000) State of the art of micromachining. CIRP Ann 49(2):473–488CrossRefGoogle Scholar
  2. 2.
    Ehmann K, DeVor R, Kapoor S (2002) Micro/meso-scale mechanical manufacturing—opportunities and challenges. Proceedings, JSME/ASME International Conference on Materials and Processing, October 15–18, Honolulu, HI 1:6–13 (Keynote presentation).Google Scholar
  3. 3.
    Chae J, Park S, Freiheit T (2006) Investigation of micro-cutting operations. Int J Mach Tools Manuf 46:313–332CrossRefGoogle Scholar
  4. 4.
    Dornfeld D, Min S, Takeuchi Y (2006) Recent advances in mechanical micromachining. Annals of the CIRP 55(2):745–768CrossRefGoogle Scholar
  5. 5.
    Byrne G, Dornfeld D, Inasaki I, Ketteler G, Konig W, Teti R (1995) Tool condition monitoring (TCM)—the status of research and industrial application. Annals of CIRP 44(2):541–567CrossRefGoogle Scholar
  6. 6.
    Dimla E, Dimla Snr DE (2000) Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tools Manuf 40:1073–1098CrossRefGoogle Scholar
  7. 7.
    Jemielniak K (1999) Commercial tool condition monitoring systems. Int J Adv Manuf Technol 15:711–721CrossRefGoogle Scholar
  8. 8.
    Rehorn AG, Jiang J, Orban PE (2005) State-of-the-art methods and results in tool condition monitoring: a review. Int J Adv Manuf Technol 26:693–710CrossRefGoogle Scholar
  9. 9.
    Abellan-Nebot JV, Subirón FR (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47:237–257CrossRefGoogle Scholar
  10. 10.
    Abu-Mahfouz I (2003) Drilling wear detection and classification using vibration signals and artificial neural network. Int J Mach Tools Manuf 43:707–720CrossRefGoogle Scholar
  11. 11.
    EL-Wardany TI, Gao D, Elbestawi MA (1995) Tool condition monitoring in drilling using vibration signature analysis. Int J Mach Tools Manuf 36(6):687–711CrossRefGoogle Scholar
  12. 12.
    Alonso FJ, Salgado DR (2008) Analysis of the structure of vibration signals for tool wear detection. Mech Syst Signal Process 22:735–748CrossRefGoogle Scholar
  13. 13.
    Yee KW, Blomquist DS (1984) Rotating tool wear monitoring apparatus. USP. 04471444Google Scholar
  14. 14.
    Issam AM (2003) Drilling wear detection and classification using vibration signals and artificial neural network. Int J Mach Tools Manuf 43:707–720CrossRefGoogle Scholar
  15. 15.
    Barker RW, Klutke G, Hinich MJ (1993) Monitoring rotating tool wear using higher-order spectral features. J Eng Ind 115(1):23–29Google Scholar
  16. 16.
    Forsgren R, Garpendahl G, Eriksson H, Wallentin B (1985) Method and apparatus for monitoring the tool status in a tool machine with cyclic machining. USP. 04558311Google Scholar
  17. 17.
    Miyoshi Y (1993) Abnormal cutting state detection using model parameters. J Japan Soc Precis Eng 59(3):467–473CrossRefGoogle Scholar
  18. 18.
    Zhang J, Chen JC (2008) Tool condition monitoring in an end-milling operation based on the vibration signal collected through a microcontroller-based data acquisition system. Int J Adv Manuf Technol 39:118–128CrossRefGoogle Scholar
  19. 19.
    Yan J, Lee J (2007) A hybrid method for on-line performance assessment and life prediction in drilling operations. Proceedings of the IEEE International Conference on Automation and Logistics, Jinan, ChinaGoogle Scholar
  20. 20.
    Kandilli I, Sonmez M, Ertunc HM, Cakir B, and Huseyin (2007) Online monitoring of tool wear in drilling and milling by multi-sensor neural network fusion. Proceedings of the IEEE International Conference on Mechatronics and Automation 1388–1394Google Scholar
  21. 21.
    Kim JD, Choi IH (1996) Development of a tool failure detection system using multi-sensors. INT J MachTools Manufact 36:861–870MathSciNetCrossRefGoogle Scholar
  22. 22.
    Binsaeid S, Asfour S, Cho S, Onar A (2009) Machine ensemble approach for simultaneous detection of transient and gradual abnormalities in end milling using multisensor fusion. J Mater Process Technol 209:4728–4738CrossRefGoogle Scholar
  23. 23.
    Deiab I, Assaleh K, Hammad F (2009) On modeling of tool wear using sensor fusion and polynomial classifiers. Mech Syst Signal Process 23:1719–1729CrossRefGoogle Scholar
  24. 24.
    Aliustaoglu C, Ertunc MH, Ocak H (2009) Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mech SystSignal Process 23:539–546CrossRefGoogle Scholar
  25. 25.
    Ghosh N, Ravi YB, Mukhopadhyay S, Paul K, Mohanty AR, Chattopadhyay AB (2007) Estimation of tool wear during CNC milling using neural network-based sensor fusion. Mech Syst Signal Process 21:446–479CrossRefGoogle Scholar
  26. 26.
    Cho S, Binsaeid S, Asfour S (2010) Design of multi-sensor fusion-based tool condition monitoring system in end milling. Int J Adv Manuf Technol 46:681–694CrossRefGoogle Scholar
  27. 27.
    Malekiana M, Parka SS, Martin BG (2009) Tool wear monitoring of micro-milling operations. J Mater Process Technol 209:4903–4914CrossRefGoogle Scholar
  28. 28.
    Tansel I, Rodriguez O, Trujillo M, Paz E, Li W (1998) Micro-end-milling—I. wear and breakage. Int J Mach Tools Manuf 38:1419–1436CrossRefGoogle Scholar
  29. 29.
    Tansel I, Arkan T, Bao WY, Mahendrakar N, Shisler B, Smith D, McCool M (2000) Tool wear estimation in micro-machining part II: neural-network-based periodic inspector for non-metals. Int J Mach Tools Manuf 40:609–620CrossRefGoogle Scholar
  30. 30.
    Tansel I, Trujillo M, Nedbouyan A, Velez C, Bao WY, Arkan TT (1998) Micro-end-milling––III. Wear estimation and tool breakage detection using acoustic emission signals. Int J Mach Tools Manuf 38:609–620Google Scholar
  31. 31.
    Jemielniak K, Arrazola PJ (2008) Application of AE and cutting force signals in tool condition monitoring in micro-milling. CIRP J Manuf Sci Technol 1:97–102CrossRefGoogle Scholar
  32. 32.
    Zhu K, Wong YS, Hong GS (2009) Multi-category micro-milling tool wear monitoring with continuous hidden Markov models. Mech Syst Signal Process 23:547–560CrossRefGoogle Scholar
  33. 33.
    Emel E, Kannatey-Asibu E Jr (1988) Tool failure monitoring in turning by pattern recognition analysis of AE Signals. ASME J Eng Ind 110:137–145CrossRefGoogle Scholar
  34. 34.
    Bishop CM (2007) Neural networks for pattern recognition. Oxford University Press, OxfordGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2011

Authors and Affiliations

  • Wan-Hao Hsieh
    • 1
  • Ming-Chyuan Lu
    • 1
    Email author
  • Shean-Juinn Chiou
    • 1
  1. 1.National Chung Hsing UniversityTaichungRepublic of China

Personalised recommendations