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A STUDY ON THE OPTIMIZATION OF METALLOID CONTENTS OF Fe-Si-B-C BASED AMORPHOUS SOFT MAGNETIC MATERIALS USING ARTIFICIAL INTELLIGENCE METHOD
- Choi, Young-sin;
- Kwon, Do-hun;
- Lee, Min-woo;
- Cha, Eun-ji;
- Jeon, Junhyup;
- ... Kim, Jongryoul;
- 외 2명
WEB OF SCIENCE
2SCOPUS
3초록
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.
키워드
- 제목
- A STUDY ON THE OPTIMIZATION OF METALLOID CONTENTS OF Fe-Si-B-C BASED AMORPHOUS SOFT MAGNETIC MATERIALS USING ARTIFICIAL INTELLIGENCE METHOD
- 저자
- Choi, Young-sin; Kwon, Do-hun; Lee, Min-woo; Cha, Eun-ji; Jeon, Junhyup; Lee, Seok-jae; Kim, Jongryoul; Kim, Hwi-jun
- 발행일
- 2022-11
- 유형
- Article
- 권
- 67
- 호
- 4
- 페이지
- 1459 ~ 1463
- 언어
- ENG
- 출판사
- POLSKA AKAD NAUK, POLISH ACAD SCIENCES, INST METALL & MATER SCI PAS
- 발행국가
- 폴란드
- 분량
- 5 페이지
- ISSN
- E 2300-1909
P 1733-3490