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

Frayman, Yakov, Zheng, Hong and Nahavandi, Saeid 2004, Machine vision system for automatic inspection of surface defects in aluminium die casting, in InTech'04 : Proceedings of the 5th International Conference on Intelligent Technologies, University of Houston-Downtown, Houston, Tex., pp. 1-5.

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Title Machine vision system for automatic inspection of surface defects in aluminium die casting
Author(s) Frayman, Yakov
Zheng, Hong
Nahavandi, Saeid
Conference name International Conference on Intelligent Technologies (5th : 2004 : Houston, Texas)
Conference location Houston, Texas
Conference dates 2-4 December 2004
Title of proceedings InTech'04 : Proceedings of the 5th International Conference on Intelligent Technologies
Editor(s) Alo, Richard
Publication date 2004
Start page 1
End page 5
Publisher University of Houston-Downtown
Place of publication Houston, Tex.
Keyword(s) aluminium die casting
automatic vision inspection
genetic algorithms
surface defect recognition
Summary A machine vision system is presented for the automatic inspection of surface defects in aluminium die casting. 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
Field of Research 090699 Electrical and Electronic Engineering not elsewhere classified
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©Reproduced with the specific permission of the copyright owner.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30005553

Document type: Conference Paper
Collection: School of Engineering and Technology
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