@ARTICLE{Lee_Min-Woo_The_2022,
 author={Lee, Min-Woo and Choi, Young-Sin and Kwon, Do-Hun and Cha, Eun-Ji and Kang, Hee-Bok and Jeong, Jae-In and Lee, Seok-Jae and Kim, Hwi-Jun},
 volume={vol. 67},
 number={No 4},
 journal={Archives of Metallurgy and Materials},
 pages={1539-1542},
 howpublished={online},
 year={2022},
 publisher={Institute of Metallurgy and Materials Science of Polish Academy of Sciences},
 publisher={Committee of Materials Engineering and Metallurgy of Polish Academy of Sciences},
 abstract={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.},
 type={Article},
 title={The Prediction of Optimized Metalloid Content in Fe-Si-B-P Amorphous Alloys Using Artificial Intelligence Algorithm},
 URL={http://www.journals.pan.pl/Content/125130/PDF/AMM-2022-4-48-Hwi-Jun%20Kim.pdf},
 doi={10.24425/amm.2022.141090},
 keywords={Fe-based amorphous alloy, Metalloid elements, Artificial intelligence, Coercivity, Saturation magnetization},
}