A STUDY ON THE OPTIMIZATION OF METALLOID CONTENTS OF Fe-Si-B-C BASED AMORPHOUS SOFT MAGNETIC MATERIALS USING ARTIFICIAL INTELLIGENCE METHOD
- Title
- A STUDY ON THE OPTIMIZATION OF METALLOID CONTENTS OF Fe-Si-B-C BASED AMORPHOUS SOFT MAGNETIC MATERIALS USING ARTIFICIAL INTELLIGENCE METHOD
- Author
- 김종렬
- Keywords
- Fe-based amorphous; Soft magnetic properties; Artificial intelligence; Machine learning; Random forest reg-ression
- Issue Date
- 2022-11
- Publisher
- POLSKA AKAD NAUK, POLISH ACAD SCIENCES, INST METALL & MATER SCI PAS
- Citation
- ARCHIVES OF METALLURGY AND MATERIALS, v. 67, NO. 4, Page. 1459-1463
- 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.
- URI
- https://journals.pan.pl/dlibra/publication/141074/edition/125110/contenthttps://repository.hanyang.ac.kr/handle/20.500.11754/182866
- ISSN
- 1733-3490;2300-1909
- DOI
- 10.24425/amm.2022.141074
- Appears in Collections:
- COLLEGE OF ENGINEERING SCIENCES[E](공학대학) > MATERIALS SCIENCE AND CHEMICAL ENGINEERING(재료화학공학과) > Articles
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- 98175_김종렬.pdfDownload
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