Diagnosing spatial variation patterns in manufacturing processes

dc.contributorApley, Daniel
dc.creatorLee, Ho Young
dc.date.accessioned2004-09-30T01:42:58Z
dc.date.accessioned2017-04-07T19:48:04Z
dc.date.available2004-09-30T01:42:58Z
dc.date.available2017-04-07T19:48:04Z
dc.date.created2003-05
dc.date.issued2004-09-30
dc.description.abstractThis 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.
dc.identifier.urihttp://hdl.handle.net/1969.1/122
dc.language.isoen_US
dc.publisherTexas A&M University
dc.subjectfactor analysis
dc.subjectmanufacturing variation diagnosis
dc.subjectdata analysis
dc.subjectblind source separation
dc.subjectdata mining
dc.titleDiagnosing spatial variation patterns in manufacturing processes
dc.typeBook
dc.typeThesis

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