Neuro-fuzzy classification of surface form deviations

Today the method for surface quality analysis of exterior car body panels is still characterized by manual detection of local form deviations and evaluation by experts. The new approach presented in this paper is based on 3-D image processing. A major step in this process is the classification of the different kinds of surface form deviations. For this purpose, we used neuro-fuzzy classification and other soft computing techniques and compared the performance of the different approaches. Although the dataset was rather small, high-dimensional and unbalanced, we achieved promising results with regard to classification accuracies and interpretability of rule bases.