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 while 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 we address the issue of low volume statistical process control. We investigate an adaptive filtering approach ... continued below

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38 pages

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CROWDER, STEPHEN V. September 1, 1999.

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  • Sandia National Laboratories
    Publisher Info: Sandia National Labs., Albuquerque, NM, and Livermore, CA (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 while 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 we address the issue of low volume statistical process control. We 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. We develop an adaptive filtering technique that assumes initial process parameters are unknown, and updates the parameters as more data become available. Using simulation techniques, we study the data requirements (the length of a time series of autocorrelated data) necessary to adequately estimate process parameters. We show that far fewer data values are needed than is typically recommended for process control applications. We also demonstrate the techniques with a case study from the nuclear weapons manufacturing complex.

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38 pages

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OSTI as DE00012655

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  • Other Information: PBD: 1 Sep 1999

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  • Report No.: SAND98-1109
  • Grant Number: AC04-94AL85000
  • DOI: 10.2172/12655 | External Link
  • Office of Scientific & Technical Information Report Number: 12655
  • Archival Resource Key: ark:/67531/metadc618380

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

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Creation Date

  • September 1, 1999

Added to The UNT Digital Library

  • June 16, 2015, 7:43 a.m.

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  • April 12, 2016, 2:54 p.m.

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CROWDER, STEPHEN V. Small Sample Properties of an Adaptive Filter with Application to Low Volume Statistical Process Control, report, September 1, 1999; Albuquerque, New Mexico. (digital.library.unt.edu/ark:/67531/metadc618380/: accessed December 13, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.