JACIII Vol.10 No.3 pp. 281-286
doi: 10.20965/jaciii.2006.p0281


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

February 22, 2005
December 21, 2005
May 20, 2006
aluminum die casting, automatic vision inspection, genetic algorithms, surface defect recognition

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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Last updated on Aug. 31, 2016