DocumentCode :
2643497
Title :
Quality improvement of steel products by using multivariate data analysis
Author :
Nakagawa, Yoshiaki ; Nakagawa, Shigemasa ; Kano, Manabu ; Tanizaki, Takashi
Author_Institution :
Sumitomo Metals(Kokura) Ltd., Kitakyusyu
fYear :
2007
fDate :
17-20 Sept. 2007
Firstpage :
2428
Lastpage :
2432
Abstract :
This paper describes quality improvement methods based on multivariate data analysis and their application to an industrial steel process. The PCA-LDA, which combines principal component analysis and linear discriminant analysis, is used for influential factor analysis of the qualitative quality variables. In addition, Data-Driven Quality Improvement (DDQI) is used to determine the optimal operating condition that can achieve the desired product quality under a given objective function and various constraints. The PCA-LDA and the DDQI can provide useful information to improve product quality. The experimental results show the effectiveness of the proposed methods.
Keywords :
principal component analysis; quality management; steel industry; PCA-LDA; data-driven quality improvement; industrial steel process; influential factor analysis; linear discriminant analysis; multivariate data analysis; principal component analysis; product quality; qualitative quality variables; quality improvement; steel products; Data analysis; Iron; Linear discriminant analysis; Mathematical model; Metal product industries; Metals industry; Principal component analysis; Process control; Steel; Testing; DDQI; linear discriminant analysis; principal component analysis; qualitative quality; quality improvement;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
SICE, 2007 Annual Conference
Conference_Location :
Takamatsu
Print_ISBN :
978-4-907764-27-2
Electronic_ISBN :
978-4-907764-27-2
Type :
conf
DOI :
10.1109/SICE.2007.4421396
Filename :
4421396
Link To Document :
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