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Proceedings Paper

Surface classification using artificial neural networks
Author(s): E. Mainsah; Divine T. Ndumu; Abongwa N. Ndumu
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Paper Abstract

Quality control tools in manufacturing industry would be significantly enhanced by the development of methods for the consistent and reliable classification of 3D imaged machined surfaces. Such tools would boost the capability of manufacturers to carry out inter- and intra-surface differentiation during the manufacturing phase of component life-cycles. This paper presents an approach to such a classification based on artificial neural networks (ANN). ANN techniques are increasingly sued to resolve demanding problems across the spectrum of engineering disciplines. They are particularly suited for handling classification problems, especially those dealing with noisy data and highly non-linear relationships. Furthermore, once trained, their operation gives them a distinct speed advantage over other technologies. In this paper, the authors use adaptive resonance theory and back propagation neural networks to classify a number of machined surfaces. The authors compare the results with those obtained from conventional methods to determine the effectiveness of the proposed technique.

Paper Details

Date Published: 20 January 1997
PDF: 12 pages
Proc. SPIE 2909, Three-Dimensional Imaging and Laser-Based Systems for Metrology and Inspection II, (20 January 1997); doi: 10.1117/12.263318
Show Author Affiliations
E. Mainsah, Coventry Univ. (United Kingdom)
Divine T. Ndumu, Univ. of Central Lancashire (United Kingdom)
Abongwa N. Ndumu, Univ. of Central Lancashire (United Kingdom)


Published in SPIE Proceedings Vol. 2909:
Three-Dimensional Imaging and Laser-Based Systems for Metrology and Inspection II
Kevin G. Harding; Donald J. Svetkoff, Editor(s)

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