Diagnosing manufacturing variation using second-order and fourth-order statistics

Ho Young Lee, Daniel W. Apley*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

This article discusses a method that can aid in diagnosing root causes of product and process variability in complex manufacturing processes, when large amounts of multivariate in-process measurement data are available. A linear structured model, similar to the standard factor analysis model, is used to generically represent the variation patterns that result from the root causes. Blind source separation techniques form the basis for identifying the precise characteristics of each individual variation pattern in order to facilitate the identification of their root causes. The second-order and fourth-order, statistics that are used in various blind separation algorithms are combined in an optimal manner to form a more effective and black-box method with wider applicability.

Original languageEnglish (US)
Pages (from-to)45-64
Number of pages20
JournalInternational Journal of Flexible Manufacturing Systems
Volume16
Issue number1
DOIs
StatePublished - Jan 2004

Keywords

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

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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