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

Paper:

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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
Published:
May 20, 2006
Creative Commons License
Keywords:
aluminum die casting, automatic vision inspection, genetic algorithms, surface defect recognition
Abstract

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.

Cite this article as:
Y. Frayman, H. Zheng, and S. Nahavandi, “Machine Vision System for Automatic Inspection of Surface Defects in Aluminum Die Casting,” J. Adv. Comput. Intell. Intell. Inform., Vol.10, No.3, pp. 281-286, 2006.
Data files:
References
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Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

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Last updated on Mar. 31, 2023