한국재료학회지 (Korean Journal of Materials Research)

  • 제33권7호
  • /
  • Pages.273-278
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  • 2023
  • /
  • 1225-0562(pISSN)
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  • 2287-7258(eISSN)
한국재료학회 (Materials Research Society of Korea)
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인공신경망을 이용한 벌크 비정질 합금 소재의 포화자속밀도 예측 성능평가

Artificial Neural Network Supported Prediction of Magnetic Properties of Bulk Metallic Glasses

  • 남충희 (한남대학교 전기전자공학과)
  • Chunghee Nam (Department of Electrical and Electronic Engineering, Hannam University)
  • 투고 : 2023.06.05
  • 심사 : 2023.06.27
  • 발행 : 2023.07.27
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초록

In this study, based on the saturation magnetic flux density experimental values (Bs) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict Bs using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R2 score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict Bs above the threshold value of Bs (1.4 T). After obtaining two ANN models optimized for the R2 and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

키워드

과제정보

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Korea government (MSIT) (NRF-2021R1F1A1052971).

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