scholarly journals Rapid Characterization Method for SMC Materials for a Preliminary Selection

2021 ◽  
Vol 11 (24) ◽  
pp. 12133
Author(s):  
Emir Pošković ◽  
Fausto Franchini ◽  
Luca Ferraris ◽  
Federico Carosio ◽  
Marco Actis Grande

In electrical machines, laminated steels are commonly adopted as soft magnetic materials, while for permanent magnets, sintered ferrites and NdFeB are the most common solutions. On the other hand, the growing demand for volume reduction with the increment of efficiency leads to the necessity of exploring other magnetic materials able to face the challenge better than the traditional ones. Bonded magnets have been used to replace sintered magnets, obtaining a better use of space and particular magnetic properties. Instead, for the magnetic circuit, Soft Magnetic Composites (SMC) allow realizing very complex magnetic design (3D path for flux) with iron loss reduction at medium-high frequencies, especially for the eddy currents loss contribution. On the other hand, SMC materials have such drawbacks as low mechanical properties and high hysteresis losses. For this reason, in this work, different studies considering several variables have been carried out. SMCs were produced through a moulding process; inorganic and organic layers to cover ferromagnetic particles were used, adopting different coating processes. Particular tests have been performed for a quicker and more indicative overview of the materials obtained. The single sheet tester (SST) is easier than traditional toroidal methods; on the other hand, the multiplicity of variables affects the SMC materials and their process. For this reason, coercivity and conductibility tests permit rapid measurement and provide a direct classification of the produced SMCs, providing the main information needed to select suitable materials. Results highlighted that choosing the more appropriate SMC material is possible after using these simple preliminary tests. After these tests, it was possible to argue that with 0.2 wt% of phenolic resin as the organic layer (and compaction pressure of 800 MPa), it is possible to produce a good SMC. On the other hand, the SMC with 0.2 wt% of epoxy resin (and compaction pressure of 800 MPa) gives a minor coercivity value. Additionally, despite the SMC with the inorganic layer, 0.2 wt% of nano-ferrites showing the best coercivity values (specifically for vacuum treatment at 600 °C), their resistivity was unsatisfactory.

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4400
Author(s):  
Luca Ferraris ◽  
Fausto Franchini ◽  
Emir Pošković ◽  
Marco Actis Grande ◽  
Róbert Bidulský

In recent years, innovative magnetic materials have been introduced in the field of electrical machines. In the ambit of soft magnetic materials, laminated steels guarantee good robustness and high magnetic performance but, in some high-frequency applications, can be replaced by Soft Magnetic Composite (SMC) materials. SMC materials allow us to reduce the eddy currents and to design innovative 3D magnetic circuits. In general, SMCs are characterized at room temperature, but as electrical machines operate at high temperature (around 100 °C), an investigation analysis of the temperature effect has been carried out on these materials; in particular, three SMC samples with different binder percentages and process parameters have been considered for magnetic and energetic characterization.


2016 ◽  
Vol 869 ◽  
pp. 596-601 ◽  
Author(s):  
Marcos Flavio de Campos

Loss separation has fundamental importance for optimizing the magnetic material for a given frequency of operation. The loss separation model assumes the existence of two main terms: one due to the hysteresis at the quasi-static situation with frequency less than 0.01 Hz and another dynamic, due to high frequency eddy currents. In this study, it is discussed the physical reasoning behind the loss separation model. Magnetic Barkhausen Noise can be a valuable tool for better understanding the physics of loss separation.


2021 ◽  
Vol 7 (6) ◽  
pp. 84
Author(s):  
Elio A. Périgo ◽  
Rubens N. de Faria

The implementation of artificial intelligence into the research and development of (currently) the most economically relevant classes of engineering hard and soft magnetic materials is addressed. Machine learning is nowadays the key approach utilized in the discovery of new compounds, physical–chemical properties prediction, microstructural/magnetic characterization, and applicability of permanent magnets and crystalline/amorphous soft magnetic alloys. Future opportunities are envisioned on at least two fronts: (a) ultra-low losses materials, as well as processes that enable their manufacturing, unlocking the next step for higher efficiency electrification, power conversion, and distribution; (b) additively manufactured magnetic materials by predicting and developing novel powdered materials properties, generative design concepts, and optimal processing conditions.


2016 ◽  
Vol 83 (6) ◽  
Author(s):  
Gerd Bramerdorfer ◽  
Dietmar Andessner ◽  
Wolfgang Amrhein ◽  
Bernhard Bauer

AbstractThis article is about the design of a measurement system for measuring the iron losses in soft magnetic materials exerted by periodic flux density characteristics. The losses are due to hysteresis and eddy currents effects. The aim is to predict the iron losses which occur in electric machines. Common loss modeling techniques are derived by considering sinusoidal flux density characteristics. As nowadays highly-utilized machine designs with special winding topologies are employed, the periodic flux density characteristics in a big part of the ferromagnetic components are far off from being sinusoidal. Hence, the here presented measurement system and the associated control are especially developed for analyzing any periodic flux density characteristics. A further part of this article is dedicated to the comparison of state-of-the-art iron loss modeling techniques and measurement results. Several scenarios with different flux density harmonic magnitudes and frequencies are considered. It turns out that currently available loss modeling techniques show significant modeling errors for non-sinusoidal periodic flux density excitations. Thus, future work has to be on deriving more accurate models by considering their applicability for computer-aided engineering software.


Sign in / Sign up

Export Citation Format

Share Document