- Tytuł:
- A Study on the Optimization of Metalloid Contents of Fe-Si-B-C Based Amorphous Soft Magnetic Materials Using Artificial Intelligence Method
- Autorzy:
-
Choi, Young-Sin
Kwon, Do-Hun
Lee, Min_Woo
Cha, Eun-Ji
Jeon, Junhyub
Lee, Seok-Jae
Kim, Jongryoul
Kim, Hwi-Jun - Powiązania:
- https://bibliotekanauki.pl/articles/2174571.pdf
- Data publikacji:
- 2022
- Wydawca:
- Polska Akademia Nauk. Czytelnia Czasopism PAN
- Tematy:
-
Fe-based amorphous
soft magnetic properties
artificial intelligence
machine learning
random forest regression - Opis:
- 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 Fe81.83Si3.63B12.63C1.91at.% with R2 values of 0.74 and 0.878, respectively.
- Źródło:
-
Archives of Metallurgy and Materials; 2022, 67, 4; 1459--1463
1733-3490 - Pojawia się w:
- Archives of Metallurgy and Materials
- Dostawca treści:
- Biblioteka Nauki
Artykuł