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Proceedings Paper

Automated inspection of ground metallic components
Author(s): F. D. Schroeder; Horst-Artur Crostack
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Paper Abstract

The automatic grinding of casted components handled by industrial robots gains increasing importance especially in connection with free formed surfaces. This paper describes an automatically working quality assurance cell that is able to test and evaluate the grinding results. Within this task image processing algorithms are used combined with simulated classifying neural networks to get at first a quality feature for the shape of free formed surfaces. The second step of the quality testing procedure is the search for surface flaws. They can be detected by adapted image filtering algorithms using polarized lighting sources. Another trained neural net supports this testing process, too. Herewith it is possible to detect and classify point and linear defects lying on the surface. By the examination of the scattering cone of infrared light sent to the surface microscopic defects such as roughness deviations off the target value are detected. These techniques have now been applied to armatures that are produced in the sanitary industry. The testing results are adapted to the needs of a control loop and transmitted to the grinding cell for a detailed touching up of the surface, if necessary.

Paper Details

Date Published: 23 November 1994
PDF: 11 pages
Proc. SPIE 2249, Automated 3D and 2D Vision, (23 November 1994); doi: 10.1117/12.196078
Show Author Affiliations
F. D. Schroeder, Univ. Dortmund (Germany)
Horst-Artur Crostack, Univ. Dortmund (Germany)

Published in SPIE Proceedings Vol. 2249:
Automated 3D and 2D Vision
Rolf-Juergen Ahlers; Donald W. Braggins; Gary W. Kamerman, Editor(s)

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