Publisher : The Korean Society of Mechanical Engineers
DOI : 10.22634/KSME-A.1918.104.22.1689
Title & Authors
Classification of Welding Defects in Austenitic Stainless Steel by Neural Pattern Recognition of Ultrasonic Signal Lee, Gang-Yong; Kim, Jun-Seop;
The research for the classification of the natural defects in welding zone is performd using the neuro-pattern recognition technology. The signal pattern recognition package including the user's defined function is developed to perform the digital signal processing, feature extraction, feature selection and classifier selection, The neural network classifier and the statistical classifiers such as the linear discriminant function classifier and the empirical Bayesian calssifier are compared and discussed. The neuro-pattern recognition technique is applied to the classificaiton of such natural defects as root crack, incomplete penetration, lack of fusion, slag inclusion, porosity, etc. If appropriately learned, the neural network classifier is concluded to be better than the statistical classifiers in the classification of the natural welding defects.
Welding Defect Evaluation;Ultrasonic Signal;Digital Signal Processing;Pattern Recognition;Neural Network;