Quality prediction and control in rolling processes using logistic regression

Ran Jin, Jing Li, Jianjun Shi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

11 Scopus citations

Abstract

With the advancement of distributed sensing technologies, abundant data are generated in rolling processes. While these data contain rich information about the process and product, it is a challenging task to develop a systematic method to model the relationship between process and product quality variables for quality improvements. This paper addresses this challenge by using logistic regression in which the quality measure is binary. Efforts are made to select minimum number of process variables In the model, based on which product qualities can be adequately predicted. If the predicted quality is worse than a target value, active control is initiated by adjusting key process variables. Considering the constraints of quality target, control costs and control feasibility, selecting appropriate control actions is formulated as mathematical optimization problems. Solutions and sensitivity studies are provided. Case studies using the data from real rolling lines are reported to demonstrate the effectiveness of this method.

Original languageEnglish (US)
Title of host publicationTransactions of the North American Manufacturing Research Institution of SME 2007 - Papers Presented at NAMRC 35
Pages113-120
Number of pages8
StatePublished - Aug 22 2007
Externally publishedYes
Event35th North American Manufacturing Research Conference, NAMRC 35 - Ann Arbor, MI, United States
Duration: May 22 2007May 25 2007

Publication series

NameTransactions of the North American Manufacturing Research Institution of SME
Volume35
ISSN (Print)1047-3025

Other

Other35th North American Manufacturing Research Conference, NAMRC 35
CountryUnited States
CityAnn Arbor, MI
Period5/22/075/25/07

Keywords

  • Control
  • Logistic regression
  • PCA
  • Quality prediction

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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