Detailed Information
Cited 0 time in 
Cited 0 time in 
Cited 0 time in
Metadata Downloads
A STUDY ON THE OPTIMIZATION OF METALLOID CONTENTS OF Fe-Si-B-C BASED AMORPHOUS SOFT MAGNETIC MATERIALS USING ARTIFICIAL INTELLIGENCE METHODopen access
- Authors
- Choi, Young-sin; Kwon, Do-hun; Lee, Min-woo; Cha, Eun-ji; Jeon, Junhyup; Lee, Seok-jae; Kim, Jongryoul; Kim, Hwi-jun
- Issue Date
- Nov-2022
- Publisher
- Polish Academy of Sciences
- Keywords
- Fe-based amorphous; Soft magnetic properties; Artificial intelligence; Machine learning; Random forest reg-ression
- Citation
- Archives of Metallurgy and Materials, v.67, no.4, pp 1459 - 1463
- Pages
- 5
- Indexed
- SCIE
SCOPUS
- Journal Title
- Archives of Metallurgy and Materials
- Volume
- 67
- Number
- 4
- Start Page
- 1459
- End Page
- 1463
- ISSN
- 1733-3490
- 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.
- Files in This Item
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
Related Researcher
- Kim, Jong ryoul
- ERICA 첨단융합대학 (ERICA 신소재·반도체공학전공)