- Tytuł:
- The Prediction of Optimized Metalloid Content in Fe-Si-B-P Amorphous Alloys Using Artificial Intelligence Algorithm
- Autorzy:
-
Lee, Min_Woo
Choi, Young-Sin
Kwon, Do-Hun
Cha, Eun-Ji
Kang, Hee-Bok
Jeong, Jae-In
Lee, Seok-Jae
Kim, Hwi-Jun - Powiązania:
- https://bibliotekanauki.pl/articles/2176648.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
Fe-based amorphous alloy
metalloid elements
artificial intelligence
coercivity
saturation magnetization - Opis:
- 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.
- Źródło:
-
Archives of Metallurgy and Materials; 2022, 67, 4; 1539--1542
1733-3490 - Pojawia się w:
- Archives of Metallurgy and Materials
- Dostawca treści:
- Biblioteka Nauki
Artykuł