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通过电磁振动处理控制片状钕铁硼相的晶体取向以产生磁各向异性。

Crystalline orientation control of the platelet NdFeB phase to produce magnetic anisotropy via electromagnetic vibration processing.

作者信息

Li Mingjun, Tamura Takuya

机构信息

National Institute of Advanced Industrial Science and Technology (AIST), Magnetic Powder Metallurgy Research Center, 2266-98, Moriyama, Nagoya, 463-8560, Japan.

出版信息

Sci Rep. 2019 Apr 5;9(1):5733. doi: 10.1038/s41598-019-42053-9.

Abstract

Controlled crystalline orientation of the discontinuous phase in a composite enables the production of improved anisotropic properties, e.g., well-aligned NdFeB platelets by hot pressing and then soaking in a low-melting Nd-Cu eutectic melt to infiltrate to grain boundary. Alternatively, an anisotropic magnet can be fabricated by sintering NdFeB powder pre-aligned with a static magnetic field. In this study, we used a two-step electromagnetic vibration (EMV) technique to solidify the NdCu-30wt% NdFeB alloy, by which the magnetic NdFeB compound could be segmented into short laths and the easy magnetisation axes of these discontinuous platelets could be highly aligned, as revealed by electron backscatter diffraction (EBSD) patterns. Magnetic properties showed that the alloy exhibited strong anisotropy in its magnetism. Our present results opened a new avenue for the simple production of anisotropic NdFeB magnets via solidification without the powder metallurgy routine. Moreover, the technique is highly expected to be applied to other systems, e.g., graphene-reinforced metallic and/or polymer composites in which the alignment of graphene can maximise the anisotropy in the thermal or electrical properties of the composites.

摘要

复合材料中不连续相的可控晶体取向能够产生改善的各向异性性能,例如,通过热压然后浸泡在低熔点的钕 - 铜共晶熔体中以使晶界渗透,从而制备出排列良好的钕铁硼薄片。或者,可以通过烧结预先在静磁场中排列的钕铁硼粉末来制造各向异性磁体。在本研究中,我们使用两步电磁振动(EMV)技术来凝固NdCu - 30wt% NdFeB合金,通过该技术,磁性钕铁硼化合物可被分割成短条,并且这些不连续薄片的易磁化轴可高度排列,电子背散射衍射(EBSD)图案显示了这一点。磁性性能表明该合金在磁性方面表现出很强的各向异性。我们目前的结果为通过凝固简单生产各向异性钕铁硼磁体开辟了一条新途径,而无需粉末冶金常规工艺。此外,该技术极有望应用于其他体系,例如石墨烯增强金属和/或聚合物复合材料,其中石墨烯的排列可使复合材料的热性能或电性能的各向异性最大化。

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