Fuzzy reasoning based automatic inspection of radiographic welds: weld recognition

A fuzzy reasoning based expert system is developed for the recognition of welds in radiographic images. First, each object in a radiographic image is identified and described with a three-feature vector. Of interest are to distinguish welds from non-weld objects in order to extract welds for further processing, such as, flaws detection. To this end, a fuzzy reasoning method is proposed. The fuzzy rules are extracted from feature data one feature at a time based on a modified fuzzy c-means algorithm. The number of fuzzy terms with overlapping between adjacent terms and the shape of terms are optimized based on the mean squared error criterion. The total number of fuzzy rules is the product of the number of fuzzy terms for each feature. The performance of this optimal set of fuzzy rules is tested with unseen data in terms of accurate rate, false positive rate, and false negative rate. For comparison, selected sets of rules are extracted by varying the number of fuzzy terms for each feature and subsequently tested. The performance of the fuzzy expert system is also found to be better than that of multi-layer perceptron neural networks, if appropriately designed.