Effective Electromagnetic Properties of Composite Material Computed From Neural Network Approach

Thanks to their lightweight, composite materials have become widely used in the automotive and aerospace industries. The design of components made from these materials is carried out by numerical modeling which can sometimes be tedious because of the need to take into account the internal structure of these materials. Obtaining the effective properties of an equivalent homogeneous material to replace the composite in our numerical models makes modeling easier. Classical homogenization approaches are not always suitable to obtain these effective properties. This article deals with an inverse problem that consists in computing the electromagnetic properties from the knowledge of the magnetic shielding effectiveness values. For different composite samples, an artificial neural network method is used to predict the effective conductivities from the magnetic shielding effectiveness measurements. The magnetic shielding effectiveness values computed from the predicted conductivities are close to those obtained from the measurements.