Using EM Algorithm to identify defective parts per million on shifting production process

The objective of this project is to determine whether utilizing an EM Algorithm to fit a Gaussian mixed model distribution model provides needed accuracy in identifying the number of defective parts per million when the overall population is made up of multiple independent runs or lots. The other o...

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Bibliographic Details
Main Author:	Freeman, James Wesley
Format:	Others
Language:	en_US
Published:	2013
Subjects:	Production population shift EM Algorithm
Online Access:	http://hdl.handle.net/2152/19996

id	ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-19996
record_format	oai_dc
spelling	ndltd-UTEXAS-oai-repositories.lib.utexas.edu-2152-199962015-09-20T17:14:32ZUsing EM Algorithm to identify defective parts per million on shifting production processFreeman, James WesleyProduction population shiftEM AlgorithmThe objective of this project is to determine whether utilizing an EM Algorithm to fit a Gaussian mixed model distribution model provides needed accuracy in identifying the number of defective parts per million when the overall population is made up of multiple independent runs or lots. The other option is approximating using standard software tools and common known techniques available to a process, industrial or quality engineer. These tools and techniques provide methods utilizing familiar distributions and statistical process control methods widely understood. This paper compares these common methods with an EM Algorithm programmed in R using a dataset of actual measurements for length of manufactured product.text2013-04-23T15:54:52Z2012-122012-12-18December 20122013-04-23T15:54:52Zapplication/pdfhttp://hdl.handle.net/2152/19996en_US
collection	NDLTD
language	en_US
format	Others
sources	NDLTD
topic	Production population shift EM Algorithm
spellingShingle	Production population shift EM Algorithm Freeman, James Wesley Using EM Algorithm to identify defective parts per million on shifting production process
description	The objective of this project is to determine whether utilizing an EM Algorithm to fit a Gaussian mixed model distribution model provides needed accuracy in identifying the number of defective parts per million when the overall population is made up of multiple independent runs or lots. The other option is approximating using standard software tools and common known techniques available to a process, industrial or quality engineer. These tools and techniques provide methods utilizing familiar distributions and statistical process control methods widely understood. This paper compares these common methods with an EM Algorithm programmed in R using a dataset of actual measurements for length of manufactured product. === text
author	Freeman, James Wesley
author_facet	Freeman, James Wesley
author_sort	Freeman, James Wesley
title	Using EM Algorithm to identify defective parts per million on shifting production process
title_short	Using EM Algorithm to identify defective parts per million on shifting production process
title_full	Using EM Algorithm to identify defective parts per million on shifting production process
title_fullStr	Using EM Algorithm to identify defective parts per million on shifting production process
title_full_unstemmed	Using EM Algorithm to identify defective parts per million on shifting production process
title_sort	using em algorithm to identify defective parts per million on shifting production process
publishDate	2013
url	http://hdl.handle.net/2152/19996
work_keys_str_mv	AT freemanjameswesley usingemalgorithmtoidentifydefectivepartspermilliononshiftingproductionprocess
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Using EM Algorithm to identify defective parts per million on shifting production process

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