Abstract

In this paper we present the results obtained recently by inspecting castings and welds using Neuro-Fuzzy classifiers. The proposed approach to detecting defects follows a general pattern recognition scheme based on three steps: segmentation, feature extraction and classification. In the first step (segmentation), potential defects are segmented using an edge detector. In the second step (feature extraction), several features of the potential defects are extracted in order to characterise them. We investigate two groups of features: geometric and intensity features. In order to make a compact pattern representation and a simple decision strategy, the number of features are reduced using a sensibility analysis, sequential forward selection and branch and bound selection. Finally, in third step (classification), neuro-fuzzy classifiers are implemented in order to establish decision boundaries in the space of the selected features which separate patterns (our segmented regions) belonging to two different classes (defects or no-defects). The results are compared with a statistical classifier and the performance analysis is evaluated using the area under the Receiver Operation Curve (ROC).