DocumentCode :
582741
Title :
Modeling for prediction steel mechanical property based on KFA-KPLS
Author :
Wang Ling ; Fu Dong Mei ; Li Qing
Author_Institution :
Sch. of Autom. & Electr. Eng., Univ. of Sci. & Technol. Beijing, Beijing, China
fYear :
2012
fDate :
25-27 July 2012
Firstpage :
7005
Lastpage :
7008
Abstract :
Based on Kernel Factor Analysis (KFA) and Kernel partial least square (KPLS), a modeling method for the prediction of steel mechanical property is proposed in this paper. In order to eliminate the heterogeneity among variables in the hot rolling process, the KFA is used for latent factor load vectors, then the variables with bigger factor load are clustered into sub-clusters, which are reorganized by KPLS with the objective variable. Finally, the results of all KPLS were used as the input of the KPLS model to predict the steel mechanical property. An application study is carried out on the real production data acquired from a steel-making plant. The experimental result shows that the precision of the presented method is greatly improved.
Keywords :
hot rolling; least squares approximations; mechanical properties; steel; steel manufacture; KFA-KPLS; Kernel factor analysis; Kernel partial least square; hot rolling process; latent factor load vectors; prediction steel mechanical property modeling; production data; steel mechanical property; steel-making plant; Educational institutions; Electronic mail; Kernel; Load modeling; Mechanical factors; Predictive models; Steel; Kernel Factor Analysis; Kernel Partial Least Square; Steel Mechanical Property;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (CCC), 2012 31st Chinese
Conference_Location :
Hefei
ISSN :
1934-1768
Print_ISBN :
978-1-4673-2581-3
Type :
conf
Filename :
6391175
Link To Document :
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