Accuracy estimation of detection of casting defects in X-ray images using some statistical techniques
Casting is one of the most important processes in the manufacture of parts for various kinds of industries, among which the automotive industry stands out. Like every manufacturing process, there is the possibility of the occurrence of defects in the materials from which the parts are
made, as well as of the appearance of faults during their operation. One of the most important tools for verifying the integrity of cast parts is radioscopy. This paper presents pattern recognition methodologies in radioscopic images of cast automotive parts for the detection of defects. Image
processing techniques were applied to extract features to be used as input of the pattern classifiers developed by artificial neural networks. To estimate the accuracy of the classifiers, use was made of random selection techniques with sample reposition (Bootstrap technique) and without sample
reposition. This work can be considered innovative in that field of research, and the results obtained motivate this paper.
Keywords: Bootstrap; Casting defects; accuracy estimation; image processing; radioscopy
Document Type: Research Article
Publication date: 01 October 2007
- Official Journal of The British Institute of Non-Destructive Testing - includes original research and development papers, technical and scientific reviews and case studies in the fields of NDT and CM.
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