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
Link To Document :
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