Automatic casting surface defect recognition and classification

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. There have been many algorithm proposed for object recognition and classification based on such as neural networks, template matching, fuzzy logic, moment invariant methods etc. and all of these were tested by the authors for robustness and flexibility. From this initial study, the algorithms based on fuzzy logic emerged as having the most potential.<
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