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논문 기본 정보

자료유형
학술저널
저자정보
Xiaoyue Fan (University of Science and Technology Beijing) Shanchao Gao (University of Science and Technology Beijing) Jianliang Zhang (University of Science and Technology Beijing) Kexin Jiao (University of Science and Technology Beijing)
저널정보
대한금속·재료학회 Metals and Materials International Metals and Materials International Vol.30 No.8
발행연도
2024.8
수록면
2,067 - 2,076 (10page)
DOI
10.1007/s12540-024-01644-6

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This paper comprehensively considers 12 indicators, including temperature, component content, solid–liquid ratio, freevolume ratio, atomic cluster as characteristic parameters, to establish a back-propagation (BP) neural network predictionmodel for the viscosity of multi-element titanium-containing iron-based melts. The comprehensive model is dissected intodistinct sub-models based on specific characteristic parameters, including the temperature and composition (T&C)-BP,Liquid structure parameters (LS)-BP, and Solid-phase particle parameters (S)-BP sub-models. The performance and applicabilityof each sub-model are rigorously analyzed, providing valuable insights into their respective scopes and limitations. By comparing the actual molten iron viscosity with the model predicted value, it was found that, the relative errors for allpredicted values were found to be within 10%. The relative error for individual samples at 1350 °C was an impressive 1.3%. Furthermore, a substantial 56% of the predictions exhibited a relative error of less than 5%.

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