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Advancing Manufacturing Quality Control Capabilities Through The Use Of In-Line High-Density Dimensional Data

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dc.contributor.authorWells, Lee Jayen
dc.contributor.committeechairCamelio, Jaime A.en
dc.contributor.committeememberWilliams, Christopher B.en
dc.contributor.committeememberSturges, Robert H.en
dc.contributor.committeememberJin, Ranen
dc.contributor.departmentIndustrial and Systems Engineeringen
dc.date.accessioned2014-01-16T09:00:09Zen
dc.date.available2014-01-16T09:00:09Zen
dc.date.issued2014-01-15en
dc.description.abstractThrough recent advancements in high-density dimensional (HDD) measurement technologies, such as 3D laser scanners, data-sets consisting of an almost complete representation of a manufactured part's geometry can now be obtained. While HDD data measurement devices have traditionally been used in reverse engineering application, they are beginning to be applied as in-line measurement devices. Unfortunately, appropriate quality control (QC) techniques have yet to be developed to take full advantage of this new data-rich environment and for the most part rely on extracting discrete key product characteristics (KPCs) for analysis. In order to maximize the potential of HDD measurement technologies requires a new quality paradigm. Specifically, when presented with HDD data, quality should not only be assessed by discrete KPCs but should consider the entire part being produced, anything less results in valuable data being wasted. This dissertation addresses the need for adapting current techniques and developing new approaches for the use of HDD data in manufacturing systems to increase overall quality control (QC) capabilities. Specifically, this research effort focuses on the use of HDD data for 1) Developing a framework for self-correcting compliant assembly systems, 2) Using statistical process control to detect process shifts through part surfaces, and 3) Performing automated part inspection for non-feature based faults. The overarching goal of this research is to identify how HDD data can be used within these three research focus areas to increase QC capabilities while following the principles of the aforementioned new quality paradigm.en
dc.description.degreePh. D.en
dc.format.mediumETDen
dc.identifier.othervt_gsexam:2064en
dc.identifier.urihttp://hdl.handle.net/10919/24865en
dc.publisherVirginia Techen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectCompliant Assemblyen
dc.subjectHigh Density Dimensional Dataen
dc.subjectInspection Systemsen
dc.subjectStatistical Process Controlen
dc.subjectSurface Monitoringen
dc.subjectQuality Controlen
dc.titleAdvancing Manufacturing Quality Control Capabilities Through The Use Of In-Line High-Density Dimensional Dataen
dc.typeDissertationen
thesis.degree.disciplineIndustrial and Systems Engineeringen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.leveldoctoralen
thesis.degree.namePh. D.en

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