A Novel Spatiotemporal Statistical Quality Control Scheme Using 3D Point Cloud Data

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dc.contributor.advisorCastillo, Krystel K.
dc.contributor.authorStankus, Sue Ellen
dc.contributor.committeeMemberMontoya, Arturo
dc.contributor.committeeMemberSaygin, Can
dc.contributor.committeeMemberTurek, Steven
dc.contributor.committeeMemberWan, Hung-Da
dc.creator.orcidhttps://orcid.org/0000-0003-2292-918X
dc.date.accessioned2024-03-08T15:42:50Z
dc.date.available2018-12-18
dc.date.available2024-03-08T15:42:50Z
dc.date.issued2017
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractThis dissertation proposes a novel spatiotemporal monitoring system for three-dimensional data such as point clouds created from 3D laser scanners. The research is divided into three sections. First, a methodology is proposed for measuring the uncertainty of 3D laser scanners to facilitate the reporting of the uncertainty budget for these systems as they gain usage in industry. Factors impacting laser scanner uncertainty were varied in a designed experiment. Analysis of the results yielded a model that was used in a Monte Carlo simulation to estimate the laser scanner uncertainty interval for several geometric features. Second, a three-dimensional spatiotemporal monitoring method was proposed to overcome the challenge of projecting a complex shaped surface onto two dimensions. The proposed method divides the point cloud into regions of interest (ROIs) and uses the ROI summary data to calculate a multivariate generalized likelihood ratio (MGLR) test statistic for inclusion on a 3D MGLR control chart. This 3D MGLR control chart identified small defects more quickly than prior methodologies. Finally, two metaheuristic algorithms, simulated annealing (SA) and genetic algorithm (GA), were implemented to optimize the 3D MGLR control chart to minimize the time needed to identify process shifts. The 3D MGLR control chart has four parameters that can be set: three defining the number of ROIs and one defining the number of past observations used to calculate the test statistic. While the GA found significantly better results than SA, it took almost twice as many iterations and took longer to run.
dc.description.departmentMechanical Engineering
dc.format.extent145 pages
dc.format.mimetypeapplication/pdf
dc.identifier.isbn9780355533064
dc.identifier.urihttps://hdl.handle.net/20.500.12588/5611
dc.languageen
dc.subject3D laser scanners
dc.subjectcontrol charts
dc.subjectnoncontact metrology systems
dc.subjectspatiotemporal monitoring
dc.subjectstatistical design of experiments
dc.subject.classificationEngineering
dc.subject.classificationMechanical engineering
dc.titleA Novel Spatiotemporal Statistical Quality Control Scheme Using 3D Point Cloud Data
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentMechanical Engineering
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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