Magnetic losses in a ferromagnetic lamination can be separated into three contributions. Bertotti theoretically assessed this distribution at the end of the 20th century in the Statistical Theory of Losses (STL), triggering significant progress in understanding the dissipation mechanisms. Recent studies have shown the possibility of reconstructing a hysteresis cycle from the high-frequency Barkhausen noise signal. Applying STL to the Barkhausen noise cycles has never been done before. Still, it could help establish a parallel with the measurement of the magnetization cycle versus frequency and the energy loss. However, STL analysis in its ultimate description requires sinusoidal flux density, while Barkhausen noise measurements are usually done with a constant excitation slope. Multiple magnetic flux density control methods were described in the literature and are reviewed in this manuscript. However, the Barkhausen noise context, requiring high-frequency sampling during the magnetization cycle, is more constraining. Therefore, specific performance criteria were considered, followed by numerical tests to determine the most adapted method to a Barkhausen STL description. Eventually, the Proportional Iterative Learning Control (P-ILC) gave the highest satisfaction rate and was chosen for experimental tests. Some of these experimental results are provided in the manuscript discussion together with suggestions for convergence speed improvement. It is, for instance, recommended to increase the gain near saturation, where the system response is poor.

Non-destructive magnetic controls are increasing in the industrial field. In this domain, the expectation for simulation tools able to anticipate the magnetic signature, improve the understanding and avoid fastidious and uncertain experimental pre-characterizations is high. Among different methods, the magnetic Barkhausen noise (MBN) control is one of the most popular. MBN raw signal is stochastic, not reproducible, and complex to exploit. MBN_energy, which is obtained by integrating the square of the MBN voltage signal with respect to the time axis is a much more stable indicator. Although the so-called MBN_energy is not, strictly speaking, an energy, it is connected to the domain wall motions and to their kinetic energy. By plotting MBN_energy as a function of H (the tangent surface excitation field), hysteresis cycles are observed. After a rescaling step, B(H) and MBN_energy(H) hysteresis cycles can be compared. They look similar for materials with high magnetocrystalline anisotropy (i.e. when the domain wall contribution is dominant over the magnetization rotation one during the magnetization process). In this study, a multiscale model is used to simulate a virtual anhysteretic behavior of a ferromagnetic steel with a magnetization process limited to the domain wall contribution. By using this anhysteretic contribution in the Jiles-Atherton model and running an inverse procedure, a MBN envelop can be obtained. By modulating the amplitude of an alternating, high frequency signal using this envelop, an accurate simulation of the raw Barkhausen noise is obtained.

Grain-oriented silicon steel (GO FeSi) laminations are vital components for efficient energy conversion in electromagnetic devices. While traditionally optimized for power frequencies of 50/60 Hz, the pursuit of higher frequency operation (f ≥ 200 Hz) promises enhanced power density. This paper introduces a model for estimating GO FeSi laminations’ magnetic behavior under these elevated operational frequencies. The proposed model combines the Maxwell diffusion equation and a material law derived from a fractional differential equation, capturing the viscoelastic characteristics of the magnetization process. Remarkably, the model’s dynamical contribution, characterized by only two parameters, achieves a notable 4.8% Euclidean relative distance error across the frequency spectrum from 50 Hz to 1 kHz. The paper’s initial section offers an exhaustive description of the model, featuring comprehensive comparisons between simulated and measured data. Subsequently, a methodology is presented for the localized segregation of magnetic losses into three conventional categories: hysteresis, classical, and excess, delineated across various tested frequencies. Further leveraging the model’s predictive capabilities, the study extends to investigating the very high-frequency regime, elucidating the spatial distribution of loss contributions. The application of proportional–iterative learning control facilitates the model’s adaptation to standard characterization conditions, employing sinusoidal imposed flux density. The paper deliberates on the implications of GO FeSi behavior under extreme operational conditions, offering insights and reflections essential for understanding and optimizing magnetic core performance in high-frequency applications.

This study investigates the magneto-elastic behavior of Iron-Silicon single crystals with predetermined crystallographic orientations. For that purpose, multi-crystalline samples with a large single-crystal in their central area were cut from a large-grain high-permeability electrical steel. The samples were subjected to tensile stress. Three complementary characterization techniques were employed to explore the effect of stress on the magnetization mechanisms: classical hysteresis loops to analyze the global magnetic response, magnetic Barkhausen noise measurements to study local magnetic domain dynamics and pinning effects, and magnetostriction measurements to quantify strain behavior and shed light on the magneto-elastic coupling. The comparative analysis reveals the intricate relationships between magnetic and mechanical properties, emphasizing the role of microstructural features such as crystallographic orientation in the magnetization process. These findings can help in the definition of optimal non-destructive testing (NDT) conditions for stress observation. For instance, the accumulation of magnetic Barkhausen noise energy up to saturation is revealed as a promising indicator for indirectly assessing stress in ferromagnetic materials.

Different magnetic needle probe setups are presented, and their strengths and weaknesses are investigated. A new configuration, combining the advantages of the other setups, is presented. Finite element (FE) simulations are implemented to explore the effect of the excitation yokes and residual stresses on the magnetic flux symmetry. The error sources affecting the accuracy of the experimental measurements are analyzed and quantified. Solutions to minimize these errors are also proposed. By minimizing error sources, the obtained experimental measurements closely follow the reference hysteresis loop obtained from a wrapping coil in the unidirectional case. The proposed measurement device notably provides enhanced accuracy for surface measurements when wrapping coils are not implementable.