Top > JACIII > Machine Vision System for Automatic Inspection of Surface Defe ...

single-jc.php

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

Paper:

Views over last 60 days: 2,341

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
  1. [1] D. Mery, Th. Jaeger, and D. Filbert, “A review of methods for automated recognition of casting defects,” Insight, 44(7), pp. 428-436, 2002.
  2. [2] F. Herold, K. Bavendiek, and R. Grigat, “A third generation automatic defect recognition system,” Proc. 16th World Conference on Non Destructive Testing, Montreal, Canada, Aug. 30-Sep. 3, 2004.
  3. [3] Z. Xu, M. Pietikäinen, and T. Ojala, “Defect classification by texture in steel surface inspection,” Proc. QCAV 97 International Conference on Quality Control by Artificial Vision, Le Creusot, Burgundy, France, pp. 179-184, May 28-30, 1997.
  4. [4] J. Kyllönen, and M. Pietikäinen, “Visual inspection of parquet slabs by combining color and texture,” Proc. IAPR Workshop on Machine Vision Applications (MVA’00), Tokyo, Japan, pp. 187-192, November 28-30, 2000.
  5. [5] E. R. Dougherty, “An Introduction of Morphological Image Processing,” SPIE Press, Bellingham, Washington, USA, 1992.
  6. [6] J. A. Bangham, and S. Marshall, “Image and signal processing with mathematical morphology,” Electronics and Communication Engineering Journal, pp. 117-128, June, 1998.
  7. [7] L. Vincent, “Morphological grayscale reconstruction in image analysis: Applications and efficient algorithms,” IEEE Transactions on Image Processing, 2, pp. 176-201, 1993.
  8. [8] C. Xu, and J. L. Prince, “Snakes, Shapes, and Gradient Vector Flow,” IEEE Transactions on Image Processing, 7(3), pp. 359-369, March, 1998.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 22, 2024