Performance Entitlement by Using Novel High Strength Electrical Steels and Copper Alloys for High-Speed Laminated Rotor Induction Machines

The laminated rotor Induction Machine (IM), with its simple construction and manufacturing, robustness, ease of control and comparatively lower cost remains by far the most utilized electromechanical energy converter. At very high speeds, traditionally its use is considered to be limited to the previously established operational limits of 2.5 × 105 rpm√kW, beyond which the surface Permanent Magnet (PM) Machine and the solid rotor Induction Machine become the machines available for consideration. The aforesaid limits are derived from the use of classic materials. This paper reviews the recent developments in electrical steels and copper alloys and translates these into the resulting performance entitlement and operational limits through a case study involving a marine application, for which an existing rare-earth PM machine is in use. It is concluded that with novel materials, laminated rotor induction machines can be operated up to 6 × 105 rpm√kW, thus opening the use of the rare-earth free Induction Machine for a wider application range previously limited to PM machines.

Identification of Grain Oriented SiFe Steels Based on Imaging the Instantaneous Dynamics of Magnetic Barkhausen Noise Using Short-Time Fourier Transform and Deep Convolutional Neural Network

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.

Correlation between Cutting Clearance, Deformation Texture, and Magnetic Loss Prediction in Non-Oriented Electrical Steels

Manufacturing the magnetic cores in electrical machines impacts the magnetic performance of the electrical steel by inducing stresses near the cutting edge. In this paper, energy loss behaviour in non-oriented electrical steels punched with different cutting clearances before and after annealing is investigated. An experimental shear cutting tool was employed to punch the ring-shaped parts from electrical steels in a finished state with four different values of cutting clearance corresponding to 1%, 3%, 5%, and 7% of the sheet thickness. The effect of cutting clearance on the magnetic losses is derived and analysed by the statistical theory of losses and associated loss separation concept including the analysis of movable magnetic objects. In this framework, this paper assesses the combined effect of cutting clearance, frequency, and heat treatment on the hysteresis loops and iron losses in non-oriented FeSi electrical steels. Measurements have been performed from quasi-static to 400 Hz at peak induction Bp = 1.0 T. Both states before and after heat treatment have been considered. The excess loss is observed as the most sensitive loss component to cutting clearance and its magneto–structural correlation is quantified.

Study of the effect of the demagnetizing field in Epstein strips of grain-oriented electrical steels through 3D finite element analysis

Purpose The purpose of this study is to explore the effect of the demagnetizing field in the Epstein characterization of grain-oriented electrical steels through a finite element method (FEM) simulations. Design/methodology/approach A 3D finite element simulation has been realized to represent the parallel and X-stacking configurations in the Epstein frame. The numerical results have been compared with experimental measures. Findings In a parallel configuration, the measured induction is actually the one in the material, whereas the resulting magnetic field differs from the applied one (in magnitude and angle) due to the shape anisotropy (demagnetizing field). In X-stacking configuration, the resulting magnetic field is close to the applied magnetic field (and then the supposed excitation field in the Epstein frame), whereas the magnetic induction has deviated from the axis of the strips. Originality/value Both stacking configurations (parallel and cross) of the Epstein frame are analyzed by three-dimensional finite element simulation.

Integrated Process Simulation of Non-Oriented Electrical Steel

[d=A]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. Improving the magnetic properties of non-oriented electrical steels are of high interest. In this context, improvement by a tailor-made microstructure, especially regarding grain size and texture, is one focus. One way to adjust the microstructure is to control the production and processing in great detail. Simulation and modeling approaches, emphasizing grain size and texture development, can help to evaluate and finally set process parameters. Here, 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. Furthermore, a connection between the process parameters and the magnetic properties is drawn. Grain size, grain size distribution, texture and dislocation density are the main transfer parameters in between the models. All models take heterogeneous microstructures across the sheet thickness into account. The basic mathematical principles of the models are explained, and a case study is presented in each case using FeSi3.2wt%Si as an example material.

Material Design for Low-Loss Non-Oriented Electrical Steel for Energy Efficient Drives

Due to the nonlinear material behavior and contradicting application requirements, the selection of a specific electrical steel grade for a highly efficient electrical machine during its design stage is challenging. With sufficient knowledge of the correlations between material and magnetic properties and capable material models, a material design for specific requirements can be enabled. In this work, the correlations between magnetization behavior, iron loss and the most relevant material parameters for non-oriented electrical steels, i.e., alloying, sheet thickness and grain size, are studied on laboratory-produced iron-based electrical steels of 2.4 and 3.2 wt % silicon. Different final thicknesses and grain sizes for both alloys are obtained by different production parameters to produce a total of 21 final material states, which are characterized by state-of-the-art material characterization methods. The magnetic properties are measured on a single sheet tester, quantified up to 5 kHz and used to parametrize the semi-physical IEM loss model. From the loss parameters, a tailor-made material, marked by its thickness and grain size is deduced. The influence of different steel grades and the chance of tailor-made material design is discussed in the context of an exemplary e-mobility application by performing finite-element electrical machine simulations and post-processing on four of the twenty-one materials and the tailor-made material. It is shown that thicker materials can lead to fewer iron losses if the alloying and grain size are adapted and that the three studied parameters are in fact levers for material design where resources can be saved by a targeted optimization.

Using the Design of Experiment Method to Develop an Energy Loss Model for Non-Oriented Electrical Steel Alloys

The main scope of the paper is to apply the Design of Experiment (DoE) method and to develop a predictive model of energy losses for non-oriented electrical steels. This approach permits us to determine a mathematical model, which is the predicted response (energy losses) as a function of input data (strip width and peak magnetic polarization) and experimental results. The presented DoE model is based on a classical central composite design of type 2n + 2n + 1 with two-levels (n = 2) and as a consequence only nine experimental points are necessary. The equation system that is associated with the model, generates a surface response equation, which permits the energy loss computation for different values of width strip and peak magnetic polarization. The DoE model was implemented, using different software packages as MathCad, Excel and OriginPro 2018, in the case of two types of electrical steels namely NO20 and M300-35A alloys that are used in small size electrical machines. In this case, the strain hardening phenomena at the cut edge becomes important, due to its negative impact on energy losses. The computed results were compared with the experimental data and errors lower than 5 % were determined.