There is a considerably history of using Eddy Current measurements for inspecting metal surfaces. The interpretation of the results, however, has been somewhat subjective. The sensitivity of the Eddy Current measurement to changes in material thickness is easily demonstrated. However, the reconstruction of thickness from the measurements (with any degree of confidence) has remained elusive. Part of the issue is the additional sensitivity of Eddy Current measurements to Lift distance and local geometry. This project is a culmination of an feasibility study to see if neural networks could provide estimates of material thicknesses for thin materials (< 0.15 inches or 4 mm) for metals with low conductivity (approximately 0.7 x mega/Ohm cm). The study a neural network model, whose accuracy varies depending upon the conditions. It turns out that the conditions required for good accuracy are usually not the conditions one wants to take measurements. The model not only suggests why this problem has been unwieldy, but also suggests that most of the difficulties could be alleviated by using an independent source to measure the Lift distance.
Lloyd G. Allred,
"Eddy-current modeling using neural networks", Proc. SPIE 3962, Applications of Artificial Neural Networks in Image Processing V, (14 April 2000); doi: 10.1117/12.382915; https://doi.org/10.1117/12.382915