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Title: Small Sample Properties of an Adaptive Filter with Application to Low Volume Statistical Process Control

Abstract

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.more » We also demonstrate the techniques with a case study from the nuclear weapons manufacturing complex.« less

Authors:
Publication Date:
Research Org.:
Sandia National Labs., Albuquerque, NM (US); Sandia National Labs., Livermore, CA (US)
Sponsoring Org.:
US Department of Energy (US)
OSTI Identifier:
12655
Report Number(s):
SAND98-1109
TRN: AH200120%%368
DOE Contract Number:  
AC04-94AL85000
Resource Type:
Technical Report
Resource Relation:
Other Information: PBD: 1 Sep 1999
Country of Publication:
United States
Language:
English
Subject:
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEFENSE; MANUFACTURING; NUCLEAR WEAPONS; PERFORMANCE; PROCESS CONTROL; RELIABILITY; COMPUTERIZED SIMULATION; PERFORMANCE TESTING; QUALITY CONTROL; TIME-SERIES ANALYSIS

Citation Formats

CROWDER, STEPHEN V. Small Sample Properties of an Adaptive Filter with Application to Low Volume Statistical Process Control. United States: N. p., 1999. Web. doi:10.2172/12655.
CROWDER, STEPHEN V. Small Sample Properties of an Adaptive Filter with Application to Low Volume Statistical Process Control. United States. doi:10.2172/12655.
CROWDER, STEPHEN V. Wed . "Small Sample Properties of an Adaptive Filter with Application to Low Volume Statistical Process Control". United States. doi:10.2172/12655. https://www.osti.gov/servlets/purl/12655.
@article{osti_12655,
title = {Small Sample Properties of an Adaptive Filter with Application to Low Volume Statistical Process Control},
author = {CROWDER, STEPHEN V.},
abstractNote = {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.},
doi = {10.2172/12655},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1999},
month = {9}
}