Developing a neural network model for magnetic yoke structure

Magnetic flux leakage technique is used extensively to detect and characterize defects in natural gas and oil transmission pipelines. The amount of magnetic flux introduced into the test sample is an important factor in the resolution of flaw detection. It depends on the power of permanent magnets and the geometrical design of the magnetic yoke. Finite element method (FEM) is the most widely used method of analyzing magnetic yoke due to its power, accuracy and straightforwardness. On the other hand its calculations are so complicated and time consuming, and every single modification in the parameters of the problem requires a new run. In this paper, we present an innovative method to overcome the problem of heavy calculations. In this method an artificial neural network (ANN) is trained to simulate the behavior of the magnetic yoke for different design parameters with an acceptable error. Afterwards the trained ANN calculates the desired output (usually generated flux) for a new design of the yoke by generalization of the already seen samples. This new method has got two advantages over the traditional FEM. First it is very fast and second it is flexible due to modifications in parameters.