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Proceedings Paper

Automated Computer Vision Inspection System For Quick Turnaround Manufacturing
Author(s): H. D. Park; O.Robert Mitchell
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Paper Abstract

Automated visual inspection promises to play an important role in the factory of the future. A prototype automatic computer vision inspection system was developed for the Quick Turnaround Cell (QTC) at Purdue University. The objective of the QTC project is to integrate design, process planning, cell control, and inspection functions into a manufacturing system that quickly produces parts using little operator knowledge and intervention. This paper focuses on the vision inspection module of the QTC project. To achieve a truly flexible automated visual inspection system, an interface between computer vision processes and CAD databases is an essential step. A design-feature based representation which includes dimensions and tolerances of the part is introduced as the part specification. A 3-D boundary representation CAD model is generated from the high level description. An interface system that understands the geometric shape of the part based on the CAD model generates a vision data base which serves as a front end to the inspection planning system. This planning system automatically generates inspection and recognition procedures from the design data. The recognition planning subsystem uses rules to select the important vision features from the given CAD data base, generates a list of simultaneously visible features, and suggests appropriate matching constraints. The inspection planning subsystem interprets each engineering specification of the part and provides proper inspection procedures. The on-line inspection subsystem executes programs based on the planning results and returns information about the part based on all the dimensions which are measured to subpixel accuracy. Thus, after the design cycle, parts can be throughly inspected with no technical decisions or programming required. Finally, results of experiments produced by the current implementation of the system are illustrated.

Paper Details

Date Published: 21 March 1989
PDF: 12 pages
Proc. SPIE 1004, Automated Inspection and High-Speed Vision Architectures II, (21 March 1989); doi: 10.1117/12.948980
Show Author Affiliations
H. D. Park, Purdue University (United States)
O.Robert Mitchell, University of Texas at Arlington (United States)


Published in SPIE Proceedings Vol. 1004:
Automated Inspection and High-Speed Vision Architectures II
Michael J. W. Chen, Editor(s)

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