| Title: |
The Prediction of Optimized Metalloid Content in Fe-Si-B-P Amorphous Alloys Using Artificial Intelligence Algorithm |
| Authors: |
Min-Woo Lee, Young-Sin Choi, Do-Hun Kwon, Eun-Ji Cha, Hee-Bok Kang, Jae-In Jeong, Seok-Jae Lee, Hwi-Jun Kim |
| Source: |
Archives of Metallurgy and Materials, Vol vol. 67, Iss No 4, Pp 1539-1542 (2022) |
| Publisher Information: |
Polish Academy of Sciences, 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Mining engineering. Metallurgy
LCC:Materials of engineering and construction. Mechanics of materials |
| Subject Terms: |
fe-based amorphous alloy, metalloid elements, artificial intelligence, coercivity, saturation magnetization, Mining engineering. Metallurgy, TN1-997, Materials of engineering and construction. Mechanics of materials, TA401-492 |
| More Details: |
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. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2300-1909 |
| Relation: |
https://journals.pan.pl/Content/125130/PDF/AMM-2022-4-48-Hwi-Jun%20Kim.pdf; https://doaj.org/toc/2300-1909 |
| DOI: |
10.24425/amm.2022.141090 |
| Access URL: |
https://doaj.org/article/1453c86641564505822ea9edda07f987 |
| Accession Number: |
edsdoj.1453c86641564505822ea9edda07f987 |
| Database: |
Directory of Open Access Journals |
