hysteresis in magnetism (electromagnetism)

The growing global demand for energy makes it necessary to adopt measures ranging from the exploration of new energy sources to the development of technology for machinery and equipment with greater energy efficiency. Non-grain oriented electrical steels are widely used in the construction of rotors and stators that form the core of electric motors, and their microstructures are directly related to its electromagnetic performance. This paper presents a new, fast and efficient method for the classification of non-grain oriented electrical steel microstructural states and their electromagnetic performance using photomicrographic analysis. The study was performed on non-grain oriented electrical steel samples with 1.28% silicon, cold-rolled with reductions of 50% and 70%, annealed in box at 730 °C for 12 h, and subjected to a subsequent annealing heat treatment for grain growth at 620 °C, 730 °C, 840 °C and 900 °C for 1, 10, 100 and 1000 min at each temperature. A total of 32 samples were used to form a database with 192 images. Our approach used a combination of extractor features (GLCM, LBP and moments) with the classifiers (Bayes, K -NN, K -means, MLP and SVM), also combined with two data partitioning, and the hold out and leave one out. KNN with 1 neighbor using the GLCM extractor showed the highest accuracy rate of 97.44%, and values greater than 96.0% for the other validation methods. The time required for the test was only 15.4 ms. The results obtained with this proposed approach, generate a new approach to evaluate a non-grain oriented electrical steel electromagnetic performance.

New approach to evaluate a non-grain oriented electrical steel electromagnetic performance using photomicrographic analysis via digital image processing

Energy losses during the magnetization process of soft magnetic laminations depend on the excitation waveform. Manufacturers of magnetic materials may not provide losses for different excitation waveforms. This may affect the accuracy of the predicted losses which are used to estimate the performance of electrical equipment during the predesign stage. Thus, a characterization tool which is capable of measuring hysteresis loops under controlled magnetization is required. Digital characterization systems available in literature use expensive commercial power amplifiers. In this work, a cost-effective power amplifier circuit is developed and interfaced with a LabVIEW program to characterize soft magnetic materials. The measurements for a sinusoidal excitation from the proposed setup are validated with those from a standard commercial measuring setup. The system is also used to measure hysteresis loops for arbitrary magnetization conditions (magnetic flux density ( $ {B}$ ) with harmonics). The proposed setup can operate over a frequency range 35–500 Hz. A small Epstein frame is also designed for measurements at higher frequencies (>200 and <500 Hz). A provision is also made to predict three components of dynamic hysteresis losses at any frequency in the above range using the loss separation approach. This capability of the setup enables the user to predict the losses at frequencies beyond the capability of the tool. The predicted losses using this algorithm are validated with measured losses. The proposed setup is cost-effective and can be developed easily using basic electronic components.

A Novel Cost-Effective Magnetic Characterization Tool for Soft Magnetic Materials Used in Electrical Machines

Among the many factors that contribute to achieving a sustainable and efficient economy is the raw material of machinery and equipment of strategic sectors. Non-grain oriented (NGO) electrical steel is used in the manufacturing of electric motors. Therefore, it is directly related to the electromagnetic efficiency of engines both in industry and in homes. This work aims to develop an intelligent 1.26% Si NGO electrical steel photomicrograph classification system to assist in the identification of better energy-efficient steel. The concept of Transfer Learning was used to apply Convolutional Neural Network architectures as feature extractors. Traditional machine learning classifiers are applied for coherent categorization of material efficiency. From the results, it is noted that the combination of the InceptionV3 architecture with the k-nearest neighbors classifier reached 100% accuracy and F1-Score. The average extraction time and test time were approximately 15 and 0.920 μs, respectively. Given these results, the literature on this application is surpassed. The best extractor-classifier combination is available in an Internet of Things (IoT) system. Therefore, a professional can freely make use of the proposed approach to assist them in identifying low magnetic loss electrical steel.

Non-grain oriented electrical steel photomicrograph classification using transfer learning

Designing smart devices based on magneto-rheological elastomers (MREs) is a quite challenging task due to the inherent nonlinear behavior of MREs in the presence of an applied magnetic field. In the early stage of the design process, the magnetic circuit parameters need to be optimized via either analytical approaches or finite element (FE) simulations. These require a precise nonlinear magnetic permeability model (<inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> curve formulation) of MREs. The model should consider the effect of essential design factors [particle volume fraction (PVF) and anisotropy] and loading conditions (magnetic field and pre-strain). The main contribution of the present study is to formulate a field-dependent novel and reliable nonlinear <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> relationship for MREs as functions of the basic design and loading factors. Three batches of isotropic and anisotropic MRE samples were fabricated with different PVFs (15%, 30%, and 45%). Subsequently, a simple methodology was presented to determine the magnetic permeability and then the nonlinear <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> curves of MREs considering wide variations in pre-strain (0%, 12.5%, 25%, 37.5%, and 50%) and coil current, ranging from 0.2 to 8 A. Experimental results showed that the relative magnetic permeability of both types of MREs increased with increasing pre-strain. Phenomenological-based models were consequently developed to estimate the nonlinear <inline-formula> <tex-math notation="LaTeX">$B$ </tex-math></inline-formula>–<inline-formula> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> curves of the isotropic and anisotropic MREs accurately and efficiently over the entire design and loading conditions considered. The proposed model can be effectively used for modeling and design optimization of MRE-based smart devices.

Development of New Phenomenological Models for Predicting Magnetic Permeability of Isotropic and Anisotropic Magneto-Rheological Elastomers

This paper is aimed at providing a review of the possible approaches to model the dependence of power losses vs. frequency in grain-oriented steels. Two models are compared using representative datasets for grades of different thickness.

Loss separation in chosen grades of grain-oriented steel

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-epa.2020.0363

Boggavarapu Sai Ram

Papers

A Novel Cost-Effective Magnetic Characterization Tool for Soft Magnetic Materials Used in Electrical Machines

Loss separation in chosen grades of grain-oriented steel

A Wavelet descriptor model of hysteresis loop phenomena