Flaws Identification Using an Approximation Function and Artificial Neural Networks

This paper presents flaws identification algorithm based on artificial neural networks and dedicated approximation functions. An eddy-current differential transducer was used to detect the flaws in thin conducting plates. The measured signals were approximated and utilized for flaws identification. Various experiments with the flaws having rectangular and nonrectangular profiles were carried out in order to verify usability of the proposed technique