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

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)

An Overview of Non-Destructive Testing of Goss Texture in Grain-Oriented Magnetic Steels

Grain oriented steels are widely used for electrical machines and components, such as transformers and reactors, due to their high magnetic permeability and low power losses. These outstanding properties are due to the crystalline structure known as Goss texture, obtained by a suitable process that is well-known and in widespread use among industrial producers of ferromagnetic steel sheets. One of the most interesting research areas in this field has been the development of non-destructive methods for the quality assessment of Goss texture. In particular, the study of techniques that can be implemented in industrial processes is very interesting. Here, we provide an overview of techniques developed in the past, novel approaches recently introduced, and new perspectives. The reliability and accuracy of several methods and equipment are presented and discussed

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

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)

Vector Hysteresis Processes for Innovative Fe-Si Magnetic Powder Cores: Experiments and Neural Network Modeling

A thorough investigation of the 2-D hysteresis processes under arbitrary excitations was carried out for a specimen of innovative Fe-Si magnetic powder material. The vector experimental measurements were first performed via a single disk tester (SDT) apparatus under a controlled magnetic induction field, taking into account circular, elliptic, and scalar processes. The experimental data relative to the circular loops were utilized to identify a vector model of hysteresis based on feedforward neural networks (NNs), having as an input the magnetic induction vector B and as an output the magnetic field vector H. Then the model was validated by the simulation of the other experimental hysteresis processes. The comparison between calculated and measured loops evidenced the capability of the model in both the reconstruction of the magnetic field trajectory and the prediction of the power loss under various excitation waveforms. Finally, the computational efficiency of the model makes it suitable for future application in finite element analysis (FEA)

Magnetic modelling for the texture analysis of Fe-Si alloys

In this paper the texture reconstruction of magnetic materials by means of a magnetic model for vector hysteresis is presented. The evaluation of the orientation distribution function (ODF) from magnetic measurements is being carried out for both non-grain-oriented and grain-oriented electrical steels. The estimated ODFS are finally compared to the measured ones

Computer Modeling of Nickel-Iron Alloy in Power Electronics Applications

Rotational magnetizations of an Ni-Fe alloy are simulated using two different computer modeling approaches, physical and phenomenological. The first one is a model defined using a single hysteron operator based on the Stoner and Wohlfarth theory and the second one is a model based on a suitable system of neural networks. The models are identified and validated using experimental data, and, finally, an example of their application for a finite-element analysis is given

A challenging hysteresis operator for the simulation of Goss-textured magnetic materials

A new hysteresis operator for the simulation of Goss-textured ferromagnets is here defined. The operator is derived from the classic Stoner–Wohlfarth model, where the anisotropy energy is assumed to be cubic instead of uniaxial, in order to reproduce the magnetic behavior of Goss textured ferromagnetic materials, such as grain-oriented Fe–Si alloys, Ni–Fe alloys, and Ni–Co alloys. A vector hysteresis model based on a single hysteresis operator is then implemented and used for the prediction of the rotational magnetizations that have been measured in a sample of grain-oriented electrical steel. This is especially promising for FEM based calculations, where the magnetization state in each point must be recalculated at each time step. Finally, the computed loops, as well as the magnetic losses, are compared to the measured data

Computing Frequency-Dependent Hysteresis Loops and Dynamic Energy Losses in Soft Magnetic Alloys via Artificial Neural Networks

A neural network model to predict the dynamic hysteresis loops and the energy-loss curves (i.e., the energy versus the amplitude of the magnetic induction) of soft ferromagnetic materials at different operating frequencies is proposed herein. Firstly, an innovative Fe-Si magnetic alloy, grade 35H270, is experimentally characterized via an Epstein frame in a wide range of frequencies, from 1 Hz up to 600 Hz. Parts of the dynamic hysteresis loops obtained through the experiments are involved in the training of a feedforward neural network, while the remaining ones are considered to validate the model. The training procedure is accurately designed to, firstly, identify the optimum network architecture (i.e., the number of hidden layers and the number of neurons per layer), and then, to effectively train the network. The model turns out to be capable of reproducing the magnetization processes and predicting the dynamic energy losses of the examined material in the whole range of inductions and frequencies considered. In addition, its computational and memory efficiency make the model a useful tool in the design stage of electrical machines and magnetic components