Total quality control for automotive raw foundry brake disks

  • P.M. LeronesEmail author
  • J.L. Fernández
  • J.G. García-Bermejo
  • E. Zalama
Original Article


In this paper, a solution for the automatic raw foundry brake disk dimensional characterisation and visual inspection for the automotive industry is presented. Three different computer vision techniques are used: a calibrated 3D structured-light vision technique, for dimensional characterisation and inspection; a 3D uncalibrated structured-light vision technique for local fault detection; and a common 2D-vision technique for a further local fault recognition. A new and fully automated 3D-calibration procedure for piece dimensional characterisation is also described. The whole system is an accurately synchronised blending of mechanics, automation, computer vision and robotics. Results from industrial implementation are presented.


3D computer vision Automatic calibration   Automotive industry  Automatic surface inspection  Brake disk Robotics  


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Martín Lerones P, Llamas Fernández J, Gómez García-Bermejo J, Zalama Casanova E (2002) Automatic foundry brake disk inspection through different computer vision techniques, using a new 3D calibration approach. In: Proceedings of 9th IEEE International Conference on Mechatronics and Machine Vision in Practice. Research Studies Press, Baldock, pp 105–113Google Scholar
  2. 2.
    Rodrigues Martins FA, Gómez García-Bermejo J, Zalama Casanova E, Perán González JR (2001) A system for automatic surface scanning. In: 8th IEEE Conference on Mechatronics and Machine Vision in Practice, Hong Kong, 27–29 August 2001, pp 124–130Google Scholar
  3. 3.
    http://www.tmstechnology.comGoogle Scholar
  4. 4.
    http://www.marposs.comGoogle Scholar
  5. 5.
    http://www.hottinger-systems.deGoogle Scholar
  6. 6.
    Tsai RY (1987) A versatile camera calibration technique for high-accuracy 3D machine vision metrology using off-the-shelf TV cameras and lenses. IEEE J Robot Automat 3(4):323–344CrossRefGoogle Scholar
  7. 7.
    Saint-Marc P, Jezouin JL, Medioni G (1991) A versatile PC-based range finding system. IEEE Trans Robot Automat 7(2):250–256CrossRefGoogle Scholar
  8. 8.
    Jain R, Kasturi R, Schunck BG (1995) Machine vision. McGraw-Hill, New YorkGoogle Scholar
  9. 9.
    McIvor AM (1999) Calibration of a laser stripe profiler. In: 3rd International Conference on 3D Digital Imaging and Modelling, Ottawa, Canada, 4–8 October 1999, pp 92–98Google Scholar
  10. 10.
    Valkenburg RJ, McIvor AM (1998) Accurate 3D measurement using structured light system. Image Vision Comput 16(2):99–110CrossRefGoogle Scholar
  11. 11.
    Masmoudi L, López Coronado JL, Gómez García-Bermejo J (1995) Calibración precisa de cámaras con modelo de distorsión y reconstrucción robusta de coordenadas tridimensionales. Informática y Automática 28(3):34–41Google Scholar
  12. 12.
    Ullman S (1997) High-level vision: object recognition and visual cognition. MIT Press, CambridgeGoogle Scholar
  13. 13.
    Medioni G, Lee MS, Tang CK (2000) A computational framework for segmentation and grouping. Elsevier, AmsterdamGoogle Scholar
  14. 14.
    Blais F, Rioux M (1986) Real time numerical peak detector. Signal Process 11:145–155CrossRefGoogle Scholar
  15. 15.
    Weng J, Cohen P, Herniou M (1992) Camera calibration with distortion models and accuracy evaluation. IEEE Trans Pattern Anal Mach Intell 14(10):965–980Google Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  • P.M. Lerones
    • 1
    Email author
  • J.L. Fernández
    • 1
  • J.G. García-Bermejo
    • 2
  • E. Zalama
    • 2
  1. 1.CARTIF, Parque Tecnológico de BoecilloBoecillo (Valladolid)Spain
  2. 2.ETSII; Dept. of Automatic ControlUniversity of ValladolidValladolidSpain

Personalised recommendations