Small sample properties of an adaptive filter with application to low volume statistical process control

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In many manufacturing environments such as the nuclear weapons complex, emphasis has shifted from the regular production and delivery of large orders to infrequent small orders. However, the challenge to maintain the same high quality and reliability standards white building much smaller lot sizes remains. To meet this challenge, specific areas need more attention, including fast and on-target process start-up, low volume statistical process control, process characterization with small experiments, and estimating reliability given few actual performance tests of the product. In this paper the authors address the issue of low volume statistical process control. They investigate an adaptive filtering ... continued below

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35 p.

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Crowder, S.V. & Eshleman, L. August 1, 1998.

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  • Crowder, S.V. Sandia National Labs., Albuquerque, NM (United States)
  • Eshleman, L. Cornell Univ., Ithaca, NY (United States)

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM (United States)
    Place of Publication: Albuquerque, New Mexico

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Description

In many manufacturing environments such as the nuclear weapons complex, emphasis has shifted from the regular production and delivery of large orders to infrequent small orders. However, the challenge to maintain the same high quality and reliability standards white building much smaller lot sizes remains. To meet this challenge, specific areas need more attention, including fast and on-target process start-up, low volume statistical process control, process characterization with small experiments, and estimating reliability given few actual performance tests of the product. In this paper the authors address the issue of low volume statistical process control. They investigate an adaptive filtering approach to process monitoring with a relatively short time series of autocorrelated data. The emphasis is on estimation and minimization of mean squared error rather than the traditional hypothesis testing and run length analyses associated with process control charting. The authors develop an adaptive filtering technique that assumes initial process parameters are unknown, and updates the parameters as more data become available. Using simulation techniques, they study the data requirements (the length of a time series of autocorrelated data) necessary to adequately estimate process parameters. They show that far fewer data values are needed than is typically recommended for process control applications. And they demonstrate the techniques with a case study from the nuclear weapons manufacturing complex.

Physical Description

35 p.

Notes

OSTI as DE98007178

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  • American Statistical Association/American Society for Quality Control fall technical conference, Corning, NY (United States), 22-23 Oct 1998

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  • Other: DE98007178
  • Report No.: SAND--98-1109C
  • Report No.: CONF-981017--
  • Grant Number: AC04-94AL85000
  • Office of Scientific & Technical Information Report Number: 665979
  • Archival Resource Key: ark:/67531/metadc702798

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Creation Date

  • August 1, 1998

Added to The UNT Digital Library

  • Sept. 12, 2015, 6:31 a.m.

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  • May 5, 2016, 8:16 p.m.

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Crowder, S.V. & Eshleman, L. Small sample properties of an adaptive filter with application to low volume statistical process control, article, August 1, 1998; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc702798/: accessed December 10, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.