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Machine Vision System for Automatic Inspection of Surface Defects in Aluminum Die Casting

Yakov Frayman*, Hong Zheng**, and Saeid Nahavandi*

*Intelligent Systems Research Group, School of Engineering and Information Technology, Deakin University, Waurn Ponds, Geelong VIC 3217, Australia
**School of Electronic Information, Wuhan University, 129 Luoyu Road, Wuhan 430079, P.R.China

Received: February 22, 2005

Accepted: December 21, 2005

Keywords: aluminum die casting, automatic vision inspection, genetic algorithms, surface defect recognition

Journal ref: Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.10, No.3 pp. 281-286, 2006


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.
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