Eddy current flaw detection with neural network applications

Eddy current inspection is a fast and effective method for detecting and sizing most of the flaws in conducting materials. The inverse problem solution, i.e., inferencing about possible defect, based on the measurement signal from the eddy current probe, is a difficult task. In this paper an implementation of artificial neural networks for multi-frequency eddy current testing on conducting layers (aluminum, copper) and ferrous tubes has been presented.