3 shows the calculated induction B in the detector, for a particular crack position. This magnitude is utilized in the neural data base creation. Induction deviations That method such as moving a probe over a magnetic testing circuit allows us to note the presence of a variation in the deviation curves. ANN are expected to learn the highly non-linear relationships between the magnetic deviation within the detector, and the defect. This must include the three defect families, the ten possible defect positions, and the complete scan of the probe.
Defects present the air permeability and conductivity.
Then the considered problem is to obtain the position and the shape of any crack located in the sample. This is done, as mentioned previously, with the association of two different neural networks as shown in Fig. 5. The feed-forward neural network contains three cell layers. This architecture is adequate to learn most of the non-linear functions. The last layer is composed of linear transfer function neurons, while the two hidden layers are made up of sigmoid ones.