Blind identification of manufacturing variation patterns by combining source separation criteria

Xuemei Shan*, Daniel Apley

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

Blind source separation recently has been investigated for blindly identifying variation patterns in multivariate manufacturing data, to aid in tracking down and eliminating root causes of manufacturing variation. Many different criteria can be used in blind separation algorithms, the performance and applicability of which depend on conditions that generally are not known a priori. We present a method for automatically combining the different criteria so as to directly minimize the mean squared estimation error. The resulting algorithm is more effective and more robust than counterparts that use other means of combining the criteria.

Original languageEnglish (US)
Pages (from-to)332-343
Number of pages12
JournalTechnometrics
Volume50
Issue number3
DOIs
StatePublished - Aug 1 2008

Keywords

  • Blind source separation
  • Factor rotation
  • Manufacturing variation reduction
  • Multivariate statistical process control
  • Principal components analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Applied Mathematics

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