Diagnosing spatial variation patterns in manufacturing processes

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dc.contributor.advisor Apley, Daniel en_US
dc.creator Lee, Ho Young en_US
dc.date.accessioned 2004-09-30T01:42:58Z
dc.date.available 2004-09-30T01:42:58Z
dc.date.created 2003-05 en_US
dc.date.issued 2004-09-30T01:42:58Z
dc.identifier.uri http://handle.tamu.edu/1969.1/122
dc.description.abstract This dissertation discusses a method that will aid in diagnosing the root causes of product and process variability in complex manufacturing processes when large quantities of multivariate in-process measurement data are available. As in any data mining application, this dissertation has as its objective the extraction of useful information from the data. A linear structured model, similar to the standard factor analysis model, is used to generically represent the variation patterns that result from the root causes. Blind source separation methods are investigated to identify spatial variation patterns in manufacturing data. Further, the existing blind source separation methods are extended, enhanced and improved to be a more effective, accurate and widely applicable method for manufacturing variation diagnosis. An overall strategy is offered to guide the use of the presented methods in conjunction with alternative methods. en_US
dc.description.provenance Made available in DSpace on 2004-09-30T01:42:58Z (GMT). No. of bitstreams: 2 etd-tamu-2003A-2003032114-1.pdf: 1340160 bytes, checksum: 7f8bfa68a1226bda038a910da6e63517 (MD5) etd-tamu-2003A-2003032114-1.pdf.txt: 183787 bytes, checksum: 66ed8446aa0d667385acda175ddf5590 (MD5) en
dc.format.extent 1340160 bytes
dc.format.extent 183787 bytes
dc.format.medium electronic en_US
dc.format.mimetype application/pdf
dc.format.mimetype text/plain
dc.language.iso en_US en_US
dc.publisher Texas A&M University en_US
dc.subject factor analysis en_US
dc.subject manufacturing variation diagnosis en_US
dc.subject data analysis en_US
dc.subject blind source separation en_US
dc.subject data mining en_US
dc.title Diagnosing spatial variation patterns in manufacturing processes en_US
thesis.degree.department Industrial Engineering en_US
thesis.degree.discipline Industrial Engineering en_US
thesis.degree.grantor Texas A&M University en_US
thesis.degree.name PHD en_US
thesis.degree.level Doctoral en_US
dc.contributor.committeeMember Kuo, Way en_US
dc.contributor.committeeMember Longnecker, Michael T. en_US
dc.contributor.committeeMember Ding, Yu en_US
dc.type.genre Electronic Dissertation en_US
dc.type.material text en_US
dc.format.digitalOrigin born digital en_US

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