Considering the Hamaker constant,inclusion size,and distance between inclusions on the surface of the molten steel,a new collision model of the inclusions on the surface of the molten steel was established based on in...Considering the Hamaker constant,inclusion size,and distance between inclusions on the surface of the molten steel,a new collision model of the inclusions on the surface of the molten steel was established based on in-situ observed results of the collision process of different types of inclusions on the surface of the molten steel.The developed model can be used to calculate the attraction of inclusions on the surface of the molten steel including Al_(2)O_(3)MgO,SiO_(2),etc.展开更多
The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial in...The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.展开更多
基金support from the National Natural Science Foundation of China(Grant No.U22A20171)the National Key Research and Development Program Project(2023YFB3709901)+3 种基金the China Baowu Low Carbon Metallurgical Innovation Fund(BWLCF202315)the Pangang-USTB Vanadium and Titanium Research Institute Research Projectthe High Steel Center(HSC)at North China University of TechnologyYanshan University and University of Science and Technology Beijing,China.
文摘Considering the Hamaker constant,inclusion size,and distance between inclusions on the surface of the molten steel,a new collision model of the inclusions on the surface of the molten steel was established based on in-situ observed results of the collision process of different types of inclusions on the surface of the molten steel.The developed model can be used to calculate the attraction of inclusions on the surface of the molten steel including Al_(2)O_(3)MgO,SiO_(2),etc.
基金support from the National Key Research and Development Program of China(No.2024YFB3713705)is acknowledgedWangzhong Mu would like to acknowledge the Strategic Mobility,Sweden(SSF,No.SM22-0039)+1 种基金the Swedish Foundation for International Cooperation in Research and Higher Education(STINT,No.IB2022-9228)the Jernkontoret(Sweden)for supporting this clean steel research.Gonghao Lian would like to acknowledge China Scholarship Council(CSC,No.202306080032).
文摘The detection and characterization of non-metallic inclusions are essential for clean steel production.Recently,imaging analysis combined with high-dimensional data processing of metallic materials using artificial intelligence(AI)-based machine learning(ML)has developed rapidly.This technique has achieved impressive results in the field of inclusion classification in process metallurgy.The present study surveys the ML modeling of inclusion prediction in advanced steels,including the detection,classification,and feature prediction of inclusions in different steel grades.Studies on clean steel with different features based on data and image analysis via ML are summarized.Regarding the data analysis,the inclusion prediction methodology based on ML establishes a connection between the experimental parameters and inclusion characteristics and analyzes the importance of the experimental parameters.Regarding the image analysis,the focus is placed on the classification of different types of inclusions via deep learning,in comparison with data analysis.Finally,further development of inclusion analyses using ML-based methods is recommended.This work paves the way for the application of AIbased methodologies for ultraclean-steel studies from a sustainable metallurgy perspective.
