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dc.contributor.authorLiu, Jiaen_US
dc.date.accessioned2017-08-22T08:00:38Z
dc.date.available2017-08-22T08:00:38Z
dc.date.issued2017-08-21en_US
dc.identifier.othervt_gsexam:12552en_US
dc.identifier.urihttp://hdl.handle.net/10919/78722
dc.description.abstractOnline quality assurance is crucial for elevating product quality and boosting process productivity in advanced manufacturing. However, the inherent complexity of advanced manufacturing, including nonlinear process dynamics, multiple process attributes, and low signal/noise ratio, poses severe challenges for both maintaining stable process operations and establishing efficacious online quality assurance schemes. To address these challenges, four different advanced manufacturing processes, namely, fused filament fabrication (FFF), binder jetting, chemical mechanical planarization (CMP), and the slicing process in wafer production, are investigated in this dissertation for applications of online quality assurance, with utilization of various sensors, such as thermocouples, infrared temperature sensors, accelerometers, etc. The overarching goal of this dissertation is to develop innovative integrated methodologies tailored for these individual manufacturing processes but addressing their common challenges to achieve satisfying performance in online quality assurance based on heterogeneous sensor data. Specifically, three new methodologies are created and validated using actual sensor data, namely, (1) Real-time process monitoring methods using Dirichlet process (DP) mixture model for timely detection of process changes and identification of different process states for FFF and CMP. The proposed methodology is capable of tackling non-Gaussian data from heterogeneous sensors in these advanced manufacturing processes for successful online quality assurance. (2) Spatial Dirichlet process (SDP) for modeling complex multimodal wafer thickness profiles and exploring their clustering effects. The SDP-based statistical control scheme can effectively detect out-of-control wafers and achieve wafer thickness quality assurance for the slicing process with high accuracy. (3) Augmented spatiotemporal log Gaussian Cox process (AST-LGCP) quantifying the spatiotemporal evolution of porosity in binder jetting parts, capable of predicting high-risk areas on consecutive layers. This work fills the long-standing research gap of lacking rigorous layer-wise porosity quantification for parts made by additive manufacturing (AM), and provides the basis for facilitating corrective actions for product quality improvements in a prognostic way. These developed methodologies surmount some common challenges of advanced manufacturing which paralyze traditional methods in online quality assurance, and embody key components for implementing effective online quality assurance with various sensor data. There is a promising potential to extend them to other manufacturing processes in the future.en_US
dc.format.mediumETDen_US
dc.publisherVirginia Techen_US
dc.rightsThis item is protected by copyright and/or related rights. Some uses of this item may be deemed fair and permitted by law even without permission from the rights holder(s), or the rights holder(s) may have licensed the work for use under certain conditions. For other uses you need to obtain permission from the rights holder(s).en_US
dc.subjectRecurrent hierarchical Dirichlet processen_US
dc.subjectonline process monitoringen_US
dc.subjectspatial Dirichlet processen_US
dc.subjectstatistical control schemeen_US
dc.subjectwafer profiles modelingen_US
dc.subjectaugmented point patternen_US
dc.subjectaugmented spatiotemporal log Gaussian Cox processen_US
dc.subjectporosity predictionen_US
dc.titleHeterogeneous Sensor Data based Online Quality Assurance for Advanced Manufacturing using Spatiotemporal Modelingen_US
dc.typeDissertationen_US
dc.contributor.departmentIndustrial and Systems Engineeringen_US
dc.description.degreePh. D.en_US
thesis.degree.namePh. D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen_US
thesis.degree.disciplineIndustrial and Systems Engineeringen_US
dc.contributor.committeechairKong, Zhenyuen_US
dc.contributor.committeememberCamelio, Jaime A.en_US
dc.contributor.committeememberWilliams, Christopher Bryanten_US
dc.contributor.committeememberJin, Ranen_US


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