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Design of regression model‐based automatic process control with reduced adjustment frequency
Author(s) -
Ye Liang,
Pan Ershun,
Shi Jianjun
Publication year - 2009
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.1017
Subject(s) - process (computing) , control (management) , compensation (psychology) , quality (philosophy) , process control , regression , computer science , engineering , regression analysis , margin (machine learning) , control chart , control theory (sociology) , control engineering , artificial intelligence , statistics , mathematics , machine learning , psychology , philosophy , epistemology , psychoanalysis , operating system
Abstract Automatic process control based on a regression model has been adopted as one of the important techniques to improve product quality in manufacturing processes. Though more frequent adjustments generally produce a superior control performance, it may also increase control cost and impair control applicability. In this paper, the concepts of quality margin and self‐compensation of noise change are introduced. Based on these concepts, a control strategy is proposed which is capable of ensuring an acceptable process performance with a reduced adjustment frequency. A case study of leaf spring forming process is conducted to compare the control performance and control adjustment frequency between the proposed approach and the existing methods. Some properties of the proposed control law are also studied. The proposed method is implemented in a hot steel rolling process to demonstrate the applicability of the proposed method. Copyright © 2009 John Wiley & Sons, Ltd.