A Study on the Optimization of Metalloid Contents of Fe-Si-B-C Based Amorphous Soft Magnetic Materials Using Artificial Intelligence Method

• Autorzy: Choi Young-Sin , Kwon Do-Hun , Lee Min_Woo , Cha Eun-Ji , Jeon Junhyub , Lee Seok-Jae , Kim Jongryoul , Kim Hwi-Jun

Identyfikatory

DOI

10.24425/amm.2022.141074

• Języki publikacji: EN

Abstrakty

EN

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 Fe_80.5Si_3.63B_13.54C_2.33 at.% and the highest saturation magnetization was obtained Fe_81.83Si_3.63B_12.63C_1.91 at.% with R² values of 0.74 and 0.878, respectively.

Słowa kluczowe

EN

Fe-based amorphous; soft magnetic properties; artificial intelligence; machine learning; random forest regression

• Wydawca: Institute of Metallurgy and Materials Science of Polish Academy of Sciences ; Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences
• Czasopismo: Archives of Metallurgy and Materials
• Rocznik: 2022
• Tom: Vol. 67, iss. 4
• Strony: 1459--1463
• Opis fizyczny: Bibliogr. 16 poz., rys., tab., wzory

Twórcy

autor

Choi Young-Sin

• Smart Liquid Processing R&D Department, Korea Institute of Industrial Technology, 156, Gaetbeol-ro, Yeonsu-Gu, Incheon 21999, Korea
• Hanyang Univ., Department of Materials Science and Chemical Engineering, Ansan 15588, Korea

• https://orcid.org/0000-0003-2388-1843

autor

Kwon Do-Hun

• Smart Liquid Processing R&D Department, Korea Institute of Industrial Technology, 156, Gaetbeol-ro, Yeonsu-Gu, Incheon 21999, Korea

• https://orcid.org/0000-0003-0738-2307

autor

Lee Min_Woo

• Smart Liquid Processing R&D Department, Korea Institute of Industrial Technology, 156, Gaetbeol-ro, Yeonsu-Gu, Incheon 21999, Korea

• https://orcid.org/0000-0002-5635-4495

autor

Cha Eun-Ji

• Smart Liquid Processing R&D Department, Korea Institute of Industrial Technology, 156, Gaetbeol-ro, Yeonsu-Gu, Incheon 21999, Korea

• https://orcid.org/0000-0003-3263-8116

autor

Jeon Junhyub

• Jeonbuk National Univ., Division of Advanced Materials Engineering, Jeonju 54896, Korea

• https://orcid.org/0000-0001-8214-1576

autor

Lee Seok-Jae

• Jeonbuk National Univ., Division of Advanced Materials Engineering, Jeonju 54896, Korea

• https://orcid.org/0000-0003-2463-8706

autor

Kim Jongryoul

• Hanyang Univ., Department of Materials Science and Chemical Engineering, Ansan 15588, Korea

• https://orcid.org/0000-0002-6961-1382

autor

Kim Hwi-Jun

• Smart Liquid Processing R&D Department, Korea Institute of Industrial Technology, 156, Gaetbeol-ro, Yeonsu-Gu, Incheon 21999, Korea

• https://orcid.org/0000-0002-6157-955X

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).