THESIS
2016
xii, 110 pages : illustrations ; 30 cm
Abstract
Due to the rapid advancement in measurement and sensing technology, large amount
of data are commonly generated in various engineering applications. These data contain
detailed information of the engineering process, and provide great opportunity for quality
improvement. However, the massive data collected from engineering processes usually
have complex dependency structure, are influenced by multiple sources of errors and require high computational effort for real time analysis. This thesis contains four essays which propose methodologies to address these issues. In the first essay, a method is proposed to model and monitor surface data with complex local variations. The second essay
is concerned with modeling and improving the shape deviation of additive manufactured
products, w...[
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Due to the rapid advancement in measurement and sensing technology, large amount
of data are commonly generated in various engineering applications. These data contain
detailed information of the engineering process, and provide great opportunity for quality
improvement. However, the massive data collected from engineering processes usually
have complex dependency structure, are influenced by multiple sources of errors and require high computational effort for real time analysis. This thesis contains four essays which propose methodologies to address these issues. In the first essay, a method is proposed to model and monitor surface data with complex local variations. The second essay
is concerned with modeling and improving the shape deviation of additive manufactured
products, which is influenced by multiple error sources. The third essay proposes a novel
method of spatial adaptive sampling and monitoring of high dimensional data streams
for detecting clustered out-of-control patterns under the constraint that only a portion of
observations can be made at each time, due to the hardware limitations. The fourth essay propose a modeling and predicting method for offline-experimental data with multiple
functional responses. These essays provide effective solutions for both offline analysis of
experimental data and online monitoring of real-time data with certain complexity that
appear in modern engineering applications.
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