
Proceedings Paper
Automatic visual inspection system for small stampings with free-form surfacesFormat | Member Price | Non-Member Price |
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Paper Abstract
The authors present an application in quality control where a 100% inspection of the surface and shape of small stampings is done. The stampings have a size of approximately 1 inch in diameter and a complex disc-shaped geometry with free-form surfaces. Typical defects are various deformations like cutoffs, incomplete stampings, cracks and scratches, etc. on both sides of the parts. For the inspection of the two sides two independent sensor units with an extreme diffuse IR-LED flash illumination are used. The parts are turned back between the two inspection tasks by a specially developed turning arrangement. The image processing is based on an image subtraction approach and it is guided by an inspection plan to handle the part variants. The difference image between the actually controlled image and a reference image contains the potential defects. The reference image is computed from various master parts in a teaching process. With the help of a so called region image built from a CAD model of the stamping the defect pixels are sorted by the use of a clustering method according to their spatial appearance. The preprocessing and alignment of these images, the segmentation of the defects and the steps of the final decision are described.
Paper Details
Date Published: 4 April 2001
PDF: 11 pages
Proc. SPIE 4301, Machine Vision Applications in Industrial Inspection IX, (4 April 2001); doi: 10.1117/12.420906
Published in SPIE Proceedings Vol. 4301:
Machine Vision Applications in Industrial Inspection IX
Martin A. Hunt, Editor(s)
PDF: 11 pages
Proc. SPIE 4301, Machine Vision Applications in Industrial Inspection IX, (4 April 2001); doi: 10.1117/12.420906
Show Author Affiliations
Ralf Langenbach, Univ. of Siegen (Germany)
Alexander Ohl, Univ. of Siegen (Germany)
Alexander Ohl, Univ. of Siegen (Germany)
Peter Scharf, Univ. of Siegen (Germany)
Joerg Semmler, Univ. of Siegen (Germany)
Joerg Semmler, Univ. of Siegen (Germany)
Published in SPIE Proceedings Vol. 4301:
Machine Vision Applications in Industrial Inspection IX
Martin A. Hunt, Editor(s)
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