The Prediction of Optimized Metalloid Content in Fe-Si-B-P Amorphous Alloys Using Artificial Intelligence Algorithm

• Autorzy: Lee Min_Woo , Choi Young-Sin , Kwon Do-Hun , Cha Eun-Ji , Kang Hee-Bok , Jeong Jae-In , Lee Seok-Jae , Kim Hwi-Jun

Identyfikatory

DOI

10.24425/amm.2022.141090

• Języki publikacji: EN

Abstrakty

EN

Artificial intelligence operated with machine learning was performed to optimize the amount of metalloid elements (Si, B, and P) subjected to be added to a Fe-based amorphous alloy for enhancement of soft magnetic properties. The effect of metalloid elements on magnetic properties was investigated through correlation analysis. Si and P were investigated as elements that affect saturation magnetization while B was investigated as an element that affect coercivity. The coefficient of determination R² (coefficient of determination) obtained from regression analysis by learning with the Random Forest Algorithm (RFR) was 0.95 In particular, the R² value measured after including phase information of the Fe-Si-B-P ribbon increased to 0.98. The optimal range of metalloid addition was predicted through correlation analysis method and machine learning.

Słowa kluczowe

EN

Fe-based amorphous alloy; metalloid elements; artificial intelligence; coercivity; saturation magnetization

• 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: 1539--1542
• Opis fizyczny: Bibliogr. 14 poz., rys., tab., wzory

Twórcy

autor

Lee Min_Woo

• Smart Liquid Processing R&D Department of Korea Institute of Industrial Technology, Incheon 21999, Korea

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

autor

Choi Young-Sin

• Smart Liquid Processing R&D Department of Korea Institute of Industrial Technology, Incheon 21999, Korea

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

autor

Kwon Do-Hun

• Smart Liquid Processing R&D Department of Korea Institute of Industrial Technology, Incheon 21999, Korea

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

autor

Cha Eun-Ji

• Smart Liquid Processing R&D Department of Korea Institute of Industrial Technology, Incheon 21999, Korea

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

autor

Kang Hee-Bok

• R&D Center of Youngin Electronic, Youngin 1033, Korea

• https://orcid.org/0000-0002-3016-2348

autor

Jeong Jae-In

• R&D Center of Youngin Electronic, Youngin 1033, Korea

• https://orcid.org/0000-0002-7043-6817

autor

Lee Seok-Jae

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

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

autor

Kim Hwi-Jun

• Smart Liquid Processing R&D Department of Korea Institute of Industrial Technology, Incheon 21999, Korea

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

Bibliografia

• [1] J.H. Jeon, N. Seo, H.J. Kim, M.H. Lee, H.K. Lim, S.B. Son, S.J. Lee, Metals 11, 729 (2021).
• [2] J.H.N. Seo, S.B. Son, S.J. Lee, M. Jung, Metals 11, 1159 (2021).
• [3] A. Inoue, T. Zhang, A. Takeuchi, Appl. Phys Letters 71, 105 (1997).
• [4] F.E. Luborsky, Handbook of Ferromagnetic Materials 1, 451-529 (1980).
• [5] C. Suryanarayana, A. Inoue, Bulk Metallic Glass 12, 55, 469 (2011).
• [6] B.L. Shen, M. Akiba, A. Inoue, Appl. Phys Letters 88, 131907 (2006).
• [7] Z. Jie, A. Julian Morris, Neural Network 11.1 65-80 (1998).
• [8] I.M. Monirul, K. Murase, Neural Network 14.9, 1265-1278 (2001).
• [9] B.L. Shen, M. Akiba, A. Inoue, Appl. Phys Letters 88, 131907 (2006).
• [10] M. Mitera, M. Naka, T. Masumoto, N. Kazama, K. Watanabe, Physica Status Solids (a) 49, K163-K165 (1978).
• [11] B. Shen, A. Inoue, Materials Transactions 43, 1235-1239 (2002).
• [12] M. Zou, S. Meng, Q. Li, H. Li, C. Chang, Y. Sun, Intermetallics 83, 83-86 (2017).
• [13] K. Fazil, International Journal of Electrical Powder & Energy System 67, 431-438 (2015).
• [14] W. Wen, Z. Hao, X. Yang, Neurocomputing 71, 3096-3103 (2008).

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).