Neuro-Fuzzy Method for Automated Defect Detection in Aluminium Castings

The automated flaw detection in aluminium castings consists of two steps: a) identification of potential defects using image processing techniques, and b) classification of potential defects into defects and regular structures (false alarms) using pattern recognition techniques. In the second step, since several features can be extracted from the potential defects, a feature selection must be performed. In addition, since the two classes have a skewed distribution, the classifier must be carefully trained. In this paper, we deal with the classifier design, i.e., which features can be selected, and how the two classes can be efficiently separated in a skewed class distribution. We propose the consideration of a self-organizing feature map (SOM) approach for stratified dimensionality reduction for simplified model building. After a feature selection and data compression stage, a neuro-fuzzy method named ANFIS is used for pattern classification. The proposed method was tested on r...

• Type: Conference
• Year: 2004
• Where: ICIAR
• Authors: Sergio Hernández, Doris Saez, Domingo Mery
• Keywords: Feature Selection, ICIAR 2004, Pattern Recognition Techniques, Potential Defect