Prediction of Physical Properties of Steels Using Artificial Neural Networks for Numerical Simulation of Electrical Installations

Abstract

For modern electric installations to be competitive, they should be supported by numerical simulations of high quality and accuracy. In turn, to improve the accuracy of numerical simulation of electric installations, it is required to take into account the thermal-physical, electromagnetic, and mechanical properties of materials as much as possible. This article discusses the possibility of predicting the properties of carbon steels on the basis of their chemical composition for a wide temperature range using a set of artificial neural networks as exemplified by electrical resistivity. Training and verification of neural networks have been based on experimental data from reference books and databases. The methods have been applied so as to avoid retraining of neural networks, as well as to use initial data more completely. The training results of neural networks are given, the error level is evaluated, and the potential is determined of applying neural networks to production of material properties and, as a consequence, improving simulation quality.