Tool Insert Wear Classification Using Statistical Descriptors and Neuronal Networks

  • E. Alegre
  • R. Aláiz
  • J. Barreiro
  • M. Viñuela
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


The goal of this work is to automatically determine the level of tool insert wear based on images acquired using a vision system. Experimental wear was carried out by machining AISI SAE 1045 and 4140 steel bars in a precision CNC lathe and using Sandvik inserts of tungsten carbide. A Pulnix PE2015 B/W with an optic composed by an industrial zoom 70 XL to 1.5X and a diffuse lighting system was used for acquisition. After images were pre-processed and wear area segmented, several patterns of the wear area were obtained using a set of descriptors based on statistical moments. Two sets of experiments were carried out, the first one considering two classes, low wear level and high wear level, respectively; the second one considering three classes. Performance of three classifiers was evaluated: Lp2, k-nearest neighbours and neural networks. Zernike and Legendre descriptors show the lowest error rates using a MLP neuronal network for classifying.


Learning Rate Neuronal Network Tungsten Carbide Zernike Moment Training Cycle 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Sick, B.: On-Line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mechanical Systems and Signal Processing, 487–546 (2002)Google Scholar
  2. 2.
    Reilly, G.A., McCormacka, B.A.O., Taylor, D.: Cutting sharpness measurement: a critical review. Journal of Materials Processing Technology 153, 261–267 (2004)CrossRefGoogle Scholar
  3. 3.
    Byrne, G., Dornfeld, D., Inasaki, I., Ketteler, G., Onig, W.K., Teti, R.: Tool condition monitoring (TCM)—the status of research and industrial application. Annals of the CIRP 44, 541–567 (1995)CrossRefGoogle Scholar
  4. 4.
    Jurkovic, J., Korosec, M., Kopac, J.: New approach in tool wear measuring technique using CCD vision system. International Journal of Machine Tools and Manufacture 45(9), 1023–1030 (2005)CrossRefGoogle Scholar
  5. 5.
    Pfeifer, T., Wiegers, L.: Realiable tool wear monitoring by optimised image and illumination control in machine vision. Measurement 28, 209–218 (2000)CrossRefGoogle Scholar
  6. 6.
    Scheffer, C., Heyns, P.S.: An industrial tool wear monitoring system for interrupted turning. Mechanical Systems and Signal Processing 18, 1219–1242 (2004)CrossRefGoogle Scholar
  7. 7.
    Hernández, L.K., Cáceres, H., Barreiro, J., Alegre, E., Castejón, M., Fernández, R.A.: Monitorización del desgaste de plaquitas de corte usando visión artificial. In: Proc. VII Congreso Iberoamericano de Ingeniería Mecánica, México (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • E. Alegre
    • 1
  • R. Aláiz
    • 1
  • J. Barreiro
    • 1
  • M. Viñuela
    • 1
  1. 1.Escuela de Ingenierías Industrial e InformáticaUniversidad de LeónLeónEspaña

Personalised recommendations