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Proceedings Paper

Automatic casting surface defect recognition and classification
Author(s): Boon Kwei Wong; M. Paul Elliot; C. W. Rapley
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Paper Abstract

High integrity castings require surfaces free from defects to reduce, if not eliminate, vulnerability to component failure from such as physical or thermal fatigue or corrosion attack. Previous studies have shown that defects on casting surfaces can be optically enhanced from the surrounding randomly textured surface by liquid penetrants, magnetic particle and other methods. However, very little has been reported on recognition and classification of the defects. The basic problem is one of shape recognition and classification, where the shape can vary in size and orientation as well as in actual shape generally within an envelope that classifies it as a particular defect. The initial work done towards this has focused on recognizing and classifying standard shapes such as the circle, square, rectangle and triangle. Various approaches were tried and this led eventually to a series of fuzzy logic based algorithms from which very good results were obtained. From this work fuzzy logic memberships were generated for the detection of defects found on casting surfaces. Simulated model shapes of such as the quench crack, mechanical crack and hole have been used to test the generated algorithm and the results for recognition and classification are very encouraging.

Paper Details

Date Published: 27 March 1995
PDF: 9 pages
Proc. SPIE 2423, Machine Vision Applications in Industrial Inspection III, (27 March 1995); doi: 10.1117/12.205515
Show Author Affiliations
Boon Kwei Wong, Univ. of Sunderland (United Kingdom)
M. Paul Elliot, Univ. of Sunderland (United Kingdom)
C. W. Rapley, Univ. of Sunderland (United Kingdom)

Published in SPIE Proceedings Vol. 2423:
Machine Vision Applications in Industrial Inspection III
Frederick Y. Wu; Stephen S. Wilson, Editor(s)

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