Crack Sizing Using a Neural Network Classifier Trained with Data Obtained from Finite Element Models
Ultrasonic inspection of riveted joints carried out by human operator is cumbersome and time consuming. An automated signal classification system would provide better reliability and accuracy in the determination of crack size and orientation. In this paper, we discuss a neural network designed for use in ultrasonic signal classification. The network can give classification results in a short time which makes possible real time ultrasonic inspection. An automated crack sizing system was presented earlier for similar applications  and the present paper is an extension of that work. The latest improvement is the use of numerically obtained ultrasonic data to train the neural network classifier (NNC).
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