## Abstract

Large sets of multivariate measurement data are now routinely available through automated in-process measurement in many manufacturing industries. These data typically contain valuable information regarding the nature of each major source of process variability. In this article we assume that each variation source causes a distinct spatial variation pattern in the measurement data. The model that we use to represent the variation patterns is of identical structure to one widely used in the so-called "blind source separation" problem that arises in many sensor-array signal processing applications. We argue that methods developed for blind source separation can be used to identify spatial variation patterns in manufacturing data. We also discuss basic blind source separation concepts and their applicability to diagnosing manufacturing variation.

Original language | English |
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Pages | 220-234 |

Number of pages | 15 |

Volume | 45 |

No | 3 |

Specialist publication | Technometrics |

State | Published - Aug 2003 |

## Keywords

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

## ASJC Scopus subject areas

- Mathematics(all)
- Statistics and Probability