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Monitoring and Diagnosis of Assembly Fixture Faults Using Modified Multivariate Control Charts and Surface Scanning Content

[+] Author Affiliations
Jeremy Rickli, Jaime Camelio

Michigan Technological University, Houghton, MI

Paper No. MSEC2007-31075, pp. 555-563; 9 pages
  • ASME 2007 International Manufacturing Science and Engineering Conference
  • ASME 2007 International Manufacturing Science and Engineering Conference
  • Atlanta, Georgia, USA, October 15–18, 2007
  • Conference Sponsors: Manufacturing Division
  • ISBN: 0-7918-4290-8 | eISBN: 0-7918-3809-9
  • Copyright © 2007 by ASME


Recent advances in process monitoring technology have introduced an influx of exceptionally large data sets containing information on manufacturing process health. Recorded data sets are comprised of numerous parameters for which multivariate statistical process control (MSPC) methodologies are required. Current multivariate control charts are ideal for monitoring data sets with a minimal amount of parameters, however, new monitoring devices such as surface scanning cameras increase the number of parameters by two orders of magnitude in some cases. This paper proposes a modified form of the original multivariate Hotelling T2 chart possessing the capability to monitor manufacturing processes containing a large number of parameters and a fault diagnosis procedure incorporating least squares analysis in conjunction with univariate control charts. A case study considering surface scanning of compliant sheet metal components and comparisons to processes utilizing Optical CMM’s is presented as verification of the proposed assembly fixture fault diagnosis methodology and modified Hotelling T2 multivariate control chart.

Copyright © 2007 by ASME



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