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Machine vision system for automatic inspection of surface defects in aluminum die casting

Frayman, Yakov, Zheng, Hong and Nahavandi, Saeid 2006, Machine vision system for automatic inspection of surface defects in aluminum die casting, Journal of advanced computational intelligence and intelligent informatics, vol. 10, no. 3, pp. 281-286.

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Title Machine vision system for automatic inspection of surface defects in aluminum die casting
Author(s) Frayman, Yakov
Zheng, Hong
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Journal of advanced computational intelligence and intelligent informatics
Volume number 10
Issue number 3
Start page 281
End page 286
Publisher Fuji Technology Press Ltd
Place of publication Tokyo, Japan
Publication date 2006
ISSN 1343-0130
Keyword(s) aluminum die casting
automatic vision inspection
genetic algorithms
surface defect recognition
Summary A camera based machine vision system for the automatic inspection of surface defects in aluminum die casting is presented. The system uses a hybrid image processing algorithm based on mathematic morphology to detect defects with different sizes and shapes. The defect inspection algorithm consists of two parts. One is a parameter learning algorithm, in which a genetic algorithm is used to extract optimal structuring element parameters, and segmentation and noise removal thresholds. The second part is a defect detection algorithm, in which the parameters obtained by a genetic algorithm are used for morphological operations. The machine vision system has been applied in an industrial setting to detect two types of casting defects: parts mix-up and any defects on the surface of castings. The system performs with a 99% or higher accuracy for both part mix-up and defect detection and is currently used in industry as part of normal production.
Language eng
Indigenous content off
Field of Research 080104 Computer Vision
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30004038

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.