4.7 Article

Preparation and characterisation of Fe/Fe3O4 fibres based soft magnetic composites

期刊

CERAMICS INTERNATIONAL
卷 47, 期 1, 页码 581-589

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.ceramint.2020.08.165

关键词

Fibres based soft magnetic composites; Fe3O4 insulating layer; DC and AC magnetic Characterisation; Core loss separation

资金

  1. Romanian Ministry of Research and Innovation, CNCS -UEFISCDI within PNCDI III [PN-III-P1-1.1-TE-2016-0649]

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The method of preparing soft magnetic composites using Fe3O4 coated Fe fibers and the performance characteristics were studied. The experiment showed that increasing the coating thickness may slightly reduce magnetic properties, but can inhibit the development of eddy currents on a larger scale.
Soft magnetic composites (FSMCs) have been prepared by using Fe fibres coated with a layer of Fe3O4, this layer playing the role of insulating material. The coating was made via blackening method by simply immersing the fibres in the blackening bath for 5, 10 and 15 min. The formation of the Fe3O4 coating on the surface of the fibres was confirmed by X-ray diffraction. The SEM investigation, used to evaluate the thickness of the coatings, has proved that increasing the coating duration leads to the increase of the coating thickness and complete coverage of the surface of the fibres. Differential scanning calorimetry and thermomagnetic measurements were used to investigate the thermal stability of the composite fibres. The fibres coated with Fe3O4 were compacted at a compaction pressure of 700 MPa to obtain toroidal magnetic cores. The obtained cores were characterised in DC and AC magnetisation regime. The analysis of the quasi-static hysteresis loops evidenced that increasing the thickness of the Fe3O4 leads to a slight deterioration of the compact's magnetic properties. However, as the thickness of the Fe3O4 layer increases, the development of eddy currents at a larger scale is hindered as proved by the AC magnetic investigations. A model for analytic separation of the core losses is proposed. By applying this model to the prepared samples, we are now able to discriminate between the occurring losses and adjust the preparation process of new samples to the targeted characteristics.

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