大功率BaTiO3陶瓷电容器物理性能的神经网络估计

R. Kapoor, P. Upadhyay, Thirmal Chinthakuntta, Ganapavarapu Neeraj Kumar
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摘要

电子元器件是未来电力系统的重要组成部分。开发高功率、可持续的电子材料用于电容器、传感器、传感器等。钛酸钡优良的介电性能使其成为电子工业的重要材料。但这种介电性能取决于材料的特性,如孔隙率、密度、浓度等。每次通过实验来确定这些性质是非常昂贵和耗时的。因此,开发一种可以预测钛酸钡性能的系统对电子工业有一定的帮助。目前的工作是开发基于人工智能的模型,可以估计钛酸钡介电材料的物理性质。利用均方误差小于1的实验数据,训练了一个三层人工神经网络来估计陶瓷的物理性质。在不同的实验数据集上验证了训练模型的性能。
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Neuro-Estimation of Physical Properties of BaTiO3 Ceramic Capacitors for High Power Applications
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.
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