Neural Network Modeling of Arbitrary Hysteresis Processes: Application to GO Ferromagnetic Steel

Abstract

A computationally efficient hysteresis model, based on a standalone deep neural network, with the capability of reproducing the evolution of the magnetization under arbitrary excitations, is here presented and applied in the simulation of a commercial grain-oriented electrical steel sheet. The main novelty of the proposed approach is to embed the past history dependence, typical of hysteretic materials, in the neural net, and to illustrate an optimized training procedure. Firstly, an experimental investigation was carried out on a sample of commercial GO steel by means of an Epstein equipment, in agreement with the international standard. Then, the traditional Preisach model, identified only using three measured symmetric hysteresis loops, was exploited to generate the training set. Once the network was trained, it was validated with the reproduction of the other measured hysteresis loops and further hysteresis processes obtained by the Preisach simulations. The model implementation at a low level of abstraction shows a very high computational speed and minimal memory allocation, allowing a possible coupling with finite-element analysis (FEA).