• DocumentCode
    2405240
  • Title

    Fuzzy machine vision based porosity detection

  • Author

    Mehran, Pejman ; Demirli, Kudret ; Bone, Gary ; Surgenor, Brian

  • Author_Institution
    Dept. of Mech. & Ind. Eng., Concordia Univ., Montreal, QC, Canada
  • fYear
    2009
  • fDate
    14-17 June 2009
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    In this paper an objective fuzzy approach for fast and accurate porosity vision based inspection is presented. An automated methodology of detection of pores, which are formed in aluminum alloys during production of water-pumps for car engines with the die casting method, is described. The proposed method is based on the correlation of the core of pore candidates with twelve developed matrices resulted in five novel features. The fuzzy decision making on porosity detection adopted and presented in this paper, adds great value to the whole production system, by increasing the confidence of the inspectors in the machine performing real-time verification. The fuzzy porosity detection was carried out on a database of 105 gray level images. The proposed model properly identifies 93.36% of the pores in the entire database.
  • Keywords
    automotive components; computer vision; die casting; fuzzy set theory; porosity; aluminum alloys; die casting method; fuzzy decision making; fuzzy machine vision; porosity detection; water-pumps; Aluminum alloys; Decision making; Die casting; Engines; Fuzzy systems; Image databases; Inspection; Machine vision; Production systems; Real time systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Information Processing Society, 2009. NAFIPS 2009. Annual Meeting of the North American
  • Conference_Location
    Cincinnati, OH
  • Print_ISBN
    978-1-4244-4575-2
  • Electronic_ISBN
    978-1-4244-4577-6
  • Type

    conf

  • DOI
    10.1109/NAFIPS.2009.5156423
  • Filename
    5156423