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Proceedings Paper

Digitized data segmentation using neural networks for reverse engineering
Author(s): Abdalla Alrashdan; Saeid Motavalli
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Paper Abstract

In many instances a new product design starts with a physical prototype. The CAD model is then extracted from the physical model. Moreover, there are many products that do not have an associated CAD model. To redesign or modify such a product, a CAD model should be available. The creation of a CAD model and the extraction of manufacturing information from a prototype or product is called Reverse Engineering. In general, reverse engineering is accomplished through three stages. These stages include part digitization data segmentation and surface modeling. Techniques for part digitizing are well established and commercial systems are available. The area that is less developed is to model the part from the cloud of points created by the digitizing systems. In this paper the emphasis is on data processing and CAD modeling for reverse engineering. Here a new method for developing a CAD model for an existing part is discussed. In this approach the cloud of points acquired from the part surface are segmented such that each segment represents a set of coordinate data belonging to a surface segment. The data is segmented using parameters derived from differential geometry. Accurate segmentation of the surfaces is achieved using a neural networks system that uses the value of defined surface parameters as input and identifies the surface segments.

Paper Details

Date Published: 6 October 1994
PDF: 7 pages
Proc. SPIE 2350, Videometrics III, (6 October 1994); doi: 10.1117/12.189152
Show Author Affiliations
Abdalla Alrashdan, Wichita State Univ. (United States)
Saeid Motavalli, Wichita State Univ. (United States)

Published in SPIE Proceedings Vol. 2350:
Videometrics III
Sabry F. El-Hakim, Editor(s)

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