Detection of welding flaws from radiographic images with fuzzy clustering methods
✍ Scribed by T.W. Liao; D.-M. Li; Y.-M. Li
- Publisher
- Elsevier Science
- Year
- 1999
- Tongue
- English
- Weight
- 587 KB
- Volume
- 108
- Category
- Article
- ISSN
- 0165-0114
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✦ Synopsis
Manual interpretation of radiographic weld images is subjective, inconsistent, labor intensive, and sometimes biased. This paper presents a welding #aw detection methodology based on fuzzy clustering methods. The methodology processes each weld image line by line. For each line, 25 features are selected. The performance of two fuzzy clustering methods, i.e. fuzzy k nearest neighbors (K-NN) and fuzzy c-means, are studied and compared. It is found that the fuzzy K-NN classi"er outperforms the fuzzy c-means classi"er with the best results of 6.01% missing rate and 18.68% false alarm rate. Issues related to the selection of features and training examples are also discussed.