The first experimentally obtained dataset on processing parameters and properties of soft magnetic composites

Area of Science:

• Materials Science
• Condensed Matter Physics
• Data Science

Background:

• Soft magnetic composites (SMC) are crucial for various electronic applications.
• Understanding the complex interplay between processing and properties is vital for optimizing SMC performance.
• Existing research often lacks comprehensive, experimentally derived datasets.

Purpose of the Study:

• To create and analyze the first experimental dataset of soft magnetic composites (SMC).
• To establish correlations between processing parameters, material structure, and functional properties.
• To facilitate data-driven research methodologies in the field of SMCs.

Main Methods:

• Experimentally fabricated 282 unique Fe-3% MgO soft magnetic composite samples.
• Varied key processing parameters: MgO nanoparticle size, pressing pressure, sintering temperature, time, and atmosphere.
• Measured critical properties: density, complex magnetic permeability, coercivity, and resonant frequency.

Main Results:

• Developed the first comprehensive, experimentally validated dataset for SMCs.
• The dataset includes detailed processing parameters and measured functional properties for each sample.
• The data provides a foundation for exploring the processing-structure-property relationships in SMCs.

Conclusions:

• The created dataset is a significant resource for the SMC research community.
• Enables advanced data-driven analysis and predictive modeling of SMC behavior.
• The dataset is publicly available and will be expanded with more parameters and properties.