Добірка наукової літератури з теми "Grain-Oriented (GO) and Non-Grain-Oriented (NO) Electrical Steels"

Electrical steels can be classified into two groups: grain-oriented (GO) and non-oriented (NGO) electrical steel. NGO electrical steels are mainly considered as core materials for different devices, such as electric motors, generators, and rotating machines. The magnetic properties and texture evolution of NGO electrical steels depend on multiple factors (such as chemical content, heat-treatment, and rolling process) making the development of new products a complex task. In this review, studies on the magnetic properties of NGO electrical steels and the corresponding texture evolution are summarized. The results indicate that further research is required for NGO electrical steels to ensure high permeability and low core loss properties.

Improving the performance of electrical steels within the magnetic circuits is essential to save energy. The domain refinement through local surface treatment by laser is an effective technique to reduce the iron losses in grain-oriented iron silicon steels. To interpret the mechanism of this technique, we have quantitatively studied the impact of nanosecond pulse laser treatment on the magnetic properties of grain-oriented Fe(3%wt)Si sheets. We measured the total power loss and apparent permeability of the samples using a Single-Sheet Tester (SST). The laser treatment resulted in a loss reduction of up to 24% compared to the average power loss of standard samples at 50 Hz. At mid-induction levels, the reduction was also accompanied by an improvement in apparent permeability. A dynamic magnetic behavior law was used to identify a dynamic property Λ including information on density, surface area and wall mobility and another internal permeability property µ representative of static field and magnetization characteristics. Lastly, we presented the behavior of these properties under different laser treatment.

Grain-oriented (GO) electrical steel (ES) laminates are still very important in industrial applications due to their remarkable crystallographic properties. Cores of large electrical machines and transformers are built from ES. The performances of these devices are significantly influenced by the properties of ES. The improvement of ES properties has been the subject of considerable research for many years. The phenomenon of magnetic anisotropy is highly non-linear, and it should be taken into account by the designers of magnetic circuits. The article proposes a modified model for calculating the angular properties of specific total loss of ES. The modeling takes into account the isotropic component (from classic eddy currents) and the anisotropic component, which is the sum of hysteresis and excess losses. For the directional loss modeling, the Boltzmann function was used. An analysis of the dependency of model coefficients on the frequency is presented.

This paper presents a new approach to the extraction and analysis of information contained in magnetic Barkhausen noise (MBN) for evaluation of grain oriented (GO) electrical steels. The proposed methodology for MBN analysis is based on the combination of the Short-Time Fourier Transform for the observation of the instantaneous dynamics of the phenomenon and deep convolutional neural networks (DCNN) for the extraction of hidden information and building the knowledge. The use of DCNN makes it possible to find even complex and convoluted rules of the Barkhausen phenomenon course, difficult to determine based solely on the selected features of MBN signals. During the tests, several samples made of conventional and high permeability GO steels were tested at different angles between the rolling and transverse directions. The influences of the angular resolution and the proposed additional prediction update algorithm on the DCNN accuracy were investigated, obtaining the highest gain for the angle of 3.6°, for which the overall accuracy exceeded 80%. The obtained results indicate that the proposed new solution combining time–frequency analysis and DCNN for the quantification of information from MBN having stochastic nature may be a very effective tool in the characterization of the magnetic materials.

A tailor-made microstructure, especially regarding grain size and texture, improves the magnetic properties of non-oriented electrical steels. One way to adjust the microstructure is to control the production and processing in great detail. Simulation and modeling approaches can help to evaluate the impact of different process parameters and finally select them appropriately. We present individual model approaches for hot rolling, cold rolling, annealing and shear cutting and aim to connect the models to account for the complex interrelationships between the process steps. A layer model combined with a microstructure model describes the grain size evolution during hot rolling. The crystal plasticity finite-element method (CPFEM) predicts the cold-rolling texture. Grain size and texture evolution during annealing is captured by the level-set method and the heat treatment model GraGLeS2D+. The impact of different grain sizes across the sheet thickness on residual stress state is evaluated by the surface model. All models take heterogeneous microstructures across the sheet thickness into account. Furthermore, a relationship is established between process and material parameters and magnetic properties. The basic mathematical principles of the models are explained and demonstrated using laboratory experiments on a non-oriented electrical steel with 3.16 wt.% Si as an example.

Les matériaux ferromagnétiques doux, souvent employés sous la forme de tôles laminées fines, sont utilisés dans les stators et rotors des machines électriques tournantes. Le rendement de ces machines est réduit par des pertes dites "pertes fer", dues aux mécanismes d'aimantation et aux courants induits. La nature du matériau reflétée par sa structure magnétique couplée à la géométrie, à l'anisotropie et à la texture de la surface sont autant de facteurs qui influent sur les performances électromagnétiques finales. Ces travaux de thèse ont donc pour objectif de proposer des matériaux magnétiques sur mesure par texturisation laser sélective de surface pour des dispositifs électromagnétiques tels que les machines électriques tournantes. L'applicabilité d'un tel procédé au niveau industriel pour des matériaux à grains orientés ou à grains non orientés dans les machines électriques nécessite de contrôler davantage la technologie et les spécifications du procédé dans le but d'optimiser les propriétés électromagnétiques. En effet, l'impact déterministe de cette technique sur la structure magnétique d'un matériau et ses performances observables (perméabilité magnétique et pertes fer) reste incomplètement appréhendé, modélisé et connu. Les conditions d'industrialisation doivent être analysée et optimisée vis-à-vis des contraintes techniques et économiques. On cherche donc ici à étudier l'impact d'une texturisation de surface par laser pulsé sur la structure magnétique en surface et en volume d'un matériau pour pouvoir les contrôler. L'adaptation des procédés laser avec augmentation de la vitesse de traitement est étudiée théoriquement, puis engagée et vérifiée expérimentalement pour correspondre aux ambitions d'industrialisation. Ainsi, ce travail effectué en très grande proximité avec le projet européen H2020 ESSIAL permettra de proposer différents traitements de surface adaptés aux machines tournantes pour ajuster certaines propriétés magnétiques, de façon théorique et expérimentale
Soft ferromagnetic materials, which are often used in the form of laminated sheets, compose rotating electrical machines' stators and rotors. The efficiency of those machines is reduced by losses called "iron losses", induced by magnetization mechanisms and eddy currents. Those magnetization reversal mechanisms can only be explained with the magnetic structure coupled to the material geometry, anisotropy and surface texture, which are also deterministic factors for the final electromagnetic performances. Then, present work aims at proposing tailor-made soft ferromagnetic materials by means of selective laser texturizing for electromagnetic devices such as rotating electrical machines. To apply such a process at an industrial level for grain-oriented and non-grain-oriented materials in electrical machines, it is necessary to better control the associated technology and specify the process in order to optimize electromagnetic properties. Indeed, the deterministic impact of this technic on a material's magnetic structure and its performances (magnetic permeability and iron losses) remains partially modelled and understood. The integration of such solution at the industrial scale must be analyzed and optimized regarding technical and economical constraints. In this work, the study of the impact of laser surface texturizing on magnetic structure (regarding surface and volume) of a material with the aim to control it is performed. Future industrialization requires to adapt the pulsed laser processes at a higher speed which has been theoretically studied, initiated and experimentally verified. To finish, present work performed in parallel with the H2020 European project ESSIAL will allow to propose different surface treatments adapted to rotating machines to adjust some quantifiable electromagnetic properties with the help of both experimental and theoretical tools