A STUDY ON THE OPTIMIZATION OF METALLOID CONTENTS OF Fe-Si-B-C BASED AMORPHOUS SOFT MAGNETIC MATERIALS USING ARTIFICIAL INTELLIGENCE METHOD

Young Sin Choi; Do Hun Kwon; Min Woo Lee; Eun Ji Cha; Junhyup Jeon; Seok Jae Lee; Jongryoul Kim; Hwi Jun Kim

doi:10.24425/amm.2022.141074

A STUDY ON THE OPTIMIZATION OF METALLOID CONTENTS OF Fe-Si-B-C BASED AMORPHOUS SOFT MAGNETIC MATERIALS USING ARTIFICIAL INTELLIGENCE METHOD

Young Sin Choi
, Do Hun Kwon
, Min Woo Lee
, Eun Ji Cha
, Junhyup Jeon
, Seok Jae Lee
, Jongryoul Kim
, Hwi Jun Kim^*

^*Corresponding author for this work

Metallurgical System Engineering

Research output: Contribution to journal › Journal article › peer-review

2 Scopus citations

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.

Original language	English
Pages (from-to)	1459-1463
Number of pages	5
Journal	Archives of Metallurgy and Materials
Volume	67
Issue number	4
DOIs	https://doi.org/10.24425/amm.2022.141074
State	Published - 2022

Keywords

Artificial intelligence
Fe-based amorphous
Machine learning
Random forest regression
Soft magnetic properties

Quacquarelli Symonds(QS) Subject Topics

Materials Science

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10.24425/amm.2022.141074

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@article{f85b21cf0e16491998e787f1536a0986,

title = "A STUDY ON THE OPTIMIZATION OF METALLOID CONTENTS OF Fe-Si-B-C BASED AMORPHOUS SOFT MAGNETIC MATERIALS USING ARTIFICIAL INTELLIGENCE METHOD",

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.",

keywords = "Artificial intelligence, Fe-based amorphous, Machine learning, Random forest regression, Soft magnetic properties",

author = "Choi, \{Young Sin\} and Kwon, \{Do Hun\} and Lee, \{Min Woo\} and Cha, \{Eun Ji\} and Junhyup Jeon and Lee, \{Seok Jae\} and Jongryoul Kim and Kim, \{Hwi Jun\}",

note = "Publisher Copyright: {\textcopyright} 2022. The Author(s).",

year = "2022",

doi = "10.24425/amm.2022.141074",

language = "English",

volume = "67",

pages = "1459--1463",

journal = "Archives of Metallurgy and Materials",

issn = "1733-3490",

number = "4",

}

TY - JOUR

T1 - A STUDY ON THE OPTIMIZATION OF METALLOID CONTENTS OF Fe-Si-B-C BASED AMORPHOUS SOFT MAGNETIC MATERIALS USING ARTIFICIAL INTELLIGENCE METHOD

AU - Choi, Young Sin

AU - Kwon, Do Hun

AU - Lee, Min Woo

AU - Cha, Eun Ji

AU - Jeon, Junhyup

AU - Lee, Seok Jae

AU - Kim, Jongryoul

AU - Kim, Hwi Jun

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Fe-based amorphous

KW - Machine learning

KW - Random forest regression

KW - Soft magnetic properties

UR - https://www.scopus.com/pages/publications/85139510378

U2 - 10.24425/amm.2022.141074

DO - 10.24425/amm.2022.141074

M3 - Journal article

AN - SCOPUS:85139510378

SN - 1733-3490

VL - 67

SP - 1459

EP - 1463

JO - Archives of Metallurgy and Materials

JF - Archives of Metallurgy and Materials

IS - 4

ER -

A STUDY ON THE OPTIMIZATION OF METALLOID CONTENTS OF Fe-Si-B-C BASED AMORPHOUS SOFT MAGNETIC MATERIALS USING ARTIFICIAL INTELLIGENCE METHOD

Abstract

Keywords

Quacquarelli Symonds(QS) Subject Topics

Access to Document

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