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Proceedings Paper

Fault Detection In Manufacturing Cells Based On Three-Dimensional Visual Information
Author(s): David A. Bourne; Robert Milligan; Paul K. Wright
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Paper Abstract

A three dimensional representation of a part is reconstructed from multiple camera views. Measurements are then collected from this three dimensional data and can be used to detect faults in the manufacturing process. The manufacturing faults are detected as visual abnormalities in the final parts. These abnormalities correspond to error conditions in earlier phases of manufacturing and could represent equipment failure, equipment wear or the use of a faulty control algorithm. A gage station which collects visual information is discussed. The algorithm which converts the visual information into a three dimensional representation of the part is presented and compared to other similar reconstruction strategies. Once the data have been collected and reconstructed, measurements are taken and correlated with possible error conditions. New correlations between the part measurements and manufacturing errors can be added to the control system as problems occur. For example, hammer wear in an open-die forge can be discovered by measuring the length of a work piece after it was struck. Along with each casual relationship there is a suggested course of action which is intended to be an immediate remedy for the error condition. In the forge example, a simple corrective action would be to move the hammers closer together to account for their wear. This makes it possible for the overall system to approach immunity to catastrophic errors while minimizing the number of defective parts.

Paper Details

Date Published: 22 November 1982
PDF: 8 pages
Proc. SPIE 0336, Robot Vision, (22 November 1982); doi: 10.1117/12.933608
Show Author Affiliations
David A. Bourne, Carnegie-Mellon University (United States)
Robert Milligan, Carnegie-Mellon University (United States)
Paul K. Wright, Carnegie-Mellon University (United States)


Published in SPIE Proceedings Vol. 0336:
Robot Vision
Azriel Rosenfeld, Editor(s)

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