A data mining approach to process optimization without an explicit quality function

Il Gyo Chong, Susan L. Albin, Chi Hyuck Jun

Research output: Contribution to journalArticlepeer-review

27 Scopus citations

Abstract

In process optimization, the setting of the process variables is usually determined by estimating a function that relates the quality to the process variables and then optimizing this estimated function. However, it is difficult to build an accurate function from process data in industrial settings because the process variables are correlated, outliers are included in the data, and the form of the functional relation between the quality and process variables may be unknown. A solution derived from an inaccurate function is normally far from being optimal. To overcome this problem, we use a data mining approach. First, a partial least squares model is used to reduce the dimensionality of the process and quality variables. Then the process settings that yield the best output are identified by sequentially partitioning the reduced process variable space using a rule induction method. The proposed method finds an optimal setting from historical data without constructing an explicit quality function. The proposed method is illustrated with two examples obtained from steel making processes. We also show, through simulation, that the proposed method gives more stable results than estimating an explicit function even when the form of the function is known in advance.

Original languageEnglish (US)
Pages (from-to)795-804
Number of pages10
JournalIIE Transactions (Institute of Industrial Engineers)
Volume39
Issue number8
DOIs
StatePublished - Aug 1 2007

All Science Journal Classification (ASJC) codes

  • Industrial and Manufacturing Engineering

Keywords

  • Data mining
  • Multicollinearity
  • Partial least squares (PLS)
  • Patient rule induction method (PRIM)
  • Process optimization

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