Orthogonal Analysis of Multisensor Data Fusion for Improved Quality Control

Peng Wang, Zhaoyan Fan, David O. Kazmer, Robert X. Gao

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

Multisensor data fusion can enable comprehensive representation of manufacturing processes, thereby contributing to improved part quality control. The effectiveness of data fusion depends on the nature of the input data. This paper investigates orthogonality as a measure for the effectiveness of data fusion, with the goal to maximize data correlation with part quality toward manufacturing process control. By decomposing sensor data into a lifted-dimensional space, contribution from each of the sensors for quantifying part quality is revealed by the corresponding projection vector. Performance evaluation using data measured from polymer injection molding confirmed the effectiveness of the developed technique.

Original languageEnglish
Article number101008
JournalJournal of Manufacturing Science and Engineering, Transactions of the ASME
Volume139
Issue number10
DOIs
StatePublished - Oct 1 2017

Bibliographical note

Funding Information:
The support for this research by the National Science Foundation under Grant No. CMMI-1000816/1000551 is greatly appreciated.

Publisher Copyright:
© 2017 by ASME.

Keywords

  • accuracy
  • data fusion
  • measurement

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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