Bures Radovan, Milyutin Vasily A, Faberova Maria, Bircakova Zuzana, Kollar Peter, Fuzer Jan
Institute of Materials Research, Slovak Academy of Sciences, Watsonova 47, 040 01, Kosice, Slovak Republic.
Institute of Metal Physics, Ural Branch of Russian Academy of Sciences, Sofia Kovalevskaya 18, 620108, Ekaterinburg, Russia.
Sci Data. 2025 Jan 3;12(1):8. doi: 10.1038/s41597-024-04286-w.
The present work describes the process of the creation and analysis of the first dataset containing processing parameters and functional properties of soft magnetic composites (SMC). All data were obtained experimentally using Fe-3% MgO system. When creating samples, parameters such as a size of MgO nanoparticles, pressing pressure, sintering temperature, time and atmosphere were varied. In total, 282 samples with a unique combination of processing parameters were obtained. In each sample, density, real part of complex magnetic permeability at different frequencies, coercivity, and resonant frequency were measured. This allowed us to create the first experimentally obtained dataset devoted to SMC. Such dataset is necessary for implementing data-driven research in the field of SMCs, as well as for studying correlations in the chain: processing parameters - structure - properties. The dataset is currently being expanded both in terms of expanding the set of variable independent parameters and in terms of expanding the set of controlled properties. The dataset is hosted in Figshare open repository.
本工作描述了首个包含软磁复合材料（SMC）加工参数和功能特性的数据集的创建及分析过程。所有数据均使用Fe-3% MgO体系通过实验获得。在制备样品时，MgO纳米颗粒尺寸、压制压力、烧结温度、时间和气氛等参数会发生变化。总共获得了282个具有独特加工参数组合的样品。对每个样品测量了密度、不同频率下复磁导率的实部、矫顽力和谐振频率。这使我们能够创建首个通过实验获得的、专门针对SMC的数据集。这样的数据集对于在SMC领域开展数据驱动研究以及研究加工参数 - 结构 - 性能链中的相关性而言是必要的。目前，该数据集正在从扩大可变独立参数集以及扩大受控特性集这两方面进行扩展。该数据集托管于Figshare开放存储库中。
