Industrial application of a multitooth tool breakage detection system using spindle motor electrical power consumption

  • Aníbal Reñones
  • Javier Rodríguez
  • Luis J. de Miguel


This paper describes a multitooth tool diagnosis application to be used in the car industry. The main focus is on the optimal variable selection (noise, vibration, temperature, and tool drives electrical power consumption) of the tool’s environment, and the signal processing by means of the segmentation of the electrical power consumption signals into groups of inserts according to the type of tools studied. The fault detection algorithms are based on statistical analysis of the spindle tool power consumption. Different statistical parameters are used in a change detection algorithm, while keeping in mind the need for a reliable and low-cost fault diagnosis system. Multitooth tools add an important degree of difficulty to the fault detection problem as opposed to simple tools because of the complexity introduced by the high number of inserts that the workpiece is machining at the same time, for different kinds of finishing and operations.


Tool breakage detection Multitooth tools Change detection Electrical power consumption 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Du R, Elbestawi MA et al (1995) Automated monitoring of manufacturing processes, part 1: monitoring methods. J Eng Ind 117:121–132CrossRefGoogle Scholar
  2. 2.
    Altintas Y (2000) Manufacturing automation: metal cutting mechanics, machine tool vibrations and CNC design. Cambridge University Press, CambridgeGoogle Scholar
  3. 3.
    Astakhov VP (2004) The assessment of cutting tool wear. Int J Mach Tools Manuf (44):637–647Google Scholar
  4. 4.
    Liang SY, Hecker RL, Landers RG (2004) Machining process monitoring and control: the state of the art. J Manuf Sci Eng 126(2):297–310CrossRefGoogle Scholar
  5. 5.
    Frankowiak M et al (2005) A review of the evolution of microcontroller-based machine and process monitoring. Int J Mach Tools Manuf 45:573–582CrossRefGoogle Scholar
  6. 6.
    Jemielniak K (1999) Commercial tool condition monitoring systems. Int J Adv Manuf Technol 15:711–721CrossRefGoogle Scholar
  7. 7.
    Romero-Troncoso RJ et al (2003) Driver current analysis for sensorless tool breakage monitoring of cnc milling machines. Int J Mach Tools Manuf 43:1529–1534CrossRefGoogle Scholar
  8. 8.
    Altintas Y (1992) Prediction of cuttings forces and tool breakage in milling from feed current measurements. J Eng Ind 114:386–292Google Scholar
  9. 9.
    Mesina OS, Langari R (2001) A neuro-fuzzy system for tool condition monitoring in metal cutting. J Manuf Sci Eng 123:312–318CrossRefGoogle Scholar
  10. 10.
    Kamarthi SV, Kumara SRT, Cohen PH (2000) Flank wear estimation in turning through wavelet representation of acoustic emission signals. J Manuf Sci Eng 122:12–19CrossRefGoogle Scholar
  11. 11.
    Scheffer C et al (2003) Development of a tool wear-monitoring system for hard turning. Int J Mach Tools Manuf 43:973–985CrossRefGoogle Scholar
  12. 12.
    Tlusty G (2000) Manufacturing processes and equipment. Prentice Hall, Englewood CliffsGoogle Scholar
  13. 13.
    Altintas Y, Shamoto E et al (1999) Analytical prediction of stability lobes in ball end milling. J Manuf Sci Eng 121:586–592CrossRefGoogle Scholar
  14. 14.
    Stein JL, Huh K (2002) Monitoring cutting forces in turning: a model-based approach. J Manuf Sci Eng 124(1):26–31CrossRefGoogle Scholar
  15. 15.
    Mahfouz IA (2003) Drilling wear detection and classification using vibration signals and artificial neural network. Int J Mach Tools Manuf 43:707–720CrossRefGoogle Scholar
  16. 16.
    Wu Y, Escande P, Du R (2001) A new method for real-time tool condition monitoring in transfer machining stations. J Manuf Sci Eng 123:339–347CrossRefGoogle Scholar
  17. 17.
    Zahra NH, Yu G (2003) Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves. Int J Mach Tools Manuf 43:337–343CrossRefGoogle Scholar
  18. 18.
    Li X et al (2000) Feed cutting force estimation from the current measurement with hybrid learning. Int J Adv Manuf Technol 16:859–862CrossRefGoogle Scholar
  19. 19.
    Wang L et al (2003) A method for sensor selection in reconfigurable process monitoring. J Manuf Sci Eng 125(1):95–99CrossRefGoogle Scholar
  20. 20.
    Basseville M, Nikiforov I (1993) Detection of abrupt changes: theory and application. Prentice Hall, Englewood CliffsGoogle Scholar
  21. 21.
    Gusstafson F (2000) Adaptive filtering and change detection. Wiley, New YorkCrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2009

Authors and Affiliations

  • Aníbal Reñones
    • 1
  • Javier Rodríguez
    • 1
  • Luis J. de Miguel
    • 2
  1. 1.CARTIF Technology CentreValladolidSpain
  2. 2.Department Control and Systems EngineeringUniversity of ValladolidValladolidSpain

Personalised recommendations