@ARTICLE{Choi_Young-Sin_A_2022,
 author={Choi, Young-Sin and Kwon, Do-Hun and Lee, Min-Woo and Cha, Eun-Ji and Jeon, Junhyup and Lee, Seok-Jae and Kim, Jongryoul and Kim, Hwi-Jun},
 volume={vol. 67},
 number={No 4},
 journal={Archives of Metallurgy and Materials},
 pages={1459-1463},
 howpublished={online},
 year={2022},
 publisher={Institute of Metallurgy and Materials Science of Polish Academy of Sciences},
 publisher={Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences},
 abstract={The soft magnetic properties of Fe-based amorphous alloys can be controlled by their compositions through alloy design. Experimental data on these alloys show some discrepancy, however, with predicted values. For further improvement of the soft magnetic properties, machine learning processes such as random forest regression, k-nearest neighbors regression and support vector regression can be helpful to optimize the composition. In this study, the random forest regression method was used to find the optimum compositions of Fe-Si-B-C alloys. As a result, the lowest coercivity was observed in Fe80.5Si3.63B13.54C2.33 at.% and the highest saturation magnetization was obtained Fe81.83Si3.63B12.63C1.91 at.% with R2 values of 0.74 and 0.878, respectively.},
 type={Article},
 title={A Study on the Optimization of Metalloid Contents of Fe-Si-B-C Based Amorphous Soft Magnetic Materials Using Artificial Intelligence Method},
 URL={http://ochroma.man.poznan.pl/Content/125110/PDF/AMM-2022-4-32-Hwi-Jun%20Kim.pdf},
 doi={10.24425/amm.2022.141074},
 keywords={Fe-based amorphous, Soft magnetic properties, Artificial intelligence, machine learning, Random forest regression},
}