Machine Learning to Predict Effective Conductivity of Composite Materials for Shielding Applications

The design of shielding systems such as enclosures for embedded electronics is often based on numerical modelling. When composite materials are used in these shields, it's necessary to determine their effective properties of equivalent homogeneous materials to simplify the modelling work. This paper presents an approach based on machine learning techniques to evaluate the effective conductivity of composite materials from measurements of their shielding effectiveness. Several composite samples are tested. The results obtained are validated by comparing the computed shielding effectiveness of the equivalent homogeneous materials to that obtained from measurements.