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
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.
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eng
Field of Research
090699 Electrical and Electronic Engineering not elsewhere classified
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