Neuro-Estimation of Physical Properties of BaTiO3 Ceramic Capacitors for High Power Applications

Abstract

Electronic components are the vital parts of future power system. It is required to develop high power sustainable electronic materials for capacitors, transducers, sensors etc. Good dielectric property of Barium Titanate makes it an important material for electronic industry. But this dielectric property, depends on properties of material like porosity, density, concentration etc. It is very costly and time consuming to determine these properties every time by experimentation. So, developing a system that can predict the properties of barium titanate can be helpful for electronic industries. The Current work is to develop AI based model that can estimate the physical properties of barium titanate dielectric material. A three-layer artificial neural network is trained to estimate the physical properties of the ceramic using the experimental data with the mean square error of less than 1. The performance of trained model is verified on different experimental data set.