Inspection is a process used to determine whether a component deviates from a given set of specifications. In industry, we usually use a coordinate measuring machine (CMM) to inspect CAD-based models, but inspection using vision sensors has recently drawn more attention because of advances that have been made in computer and imaging technologies. In this dissertation, we introduce active vision inspection for CAD-based three-dimensional models. We divide the dissertation into three major components: (i) planning, (ii) error analysis, and (iii) tolerance design. In inspection planning, the inputs are boundary representation (object centered representation) and an aspect graph (viewer centered representation) of the inspected component; the output is a sensor arrangement for dimensioning a set of topologic entities. In planning, we first use geometric reasoning and object oriented representation to determine a set of topologic entities (measurable entities) to be dimensioned based on the manufactured features on the component (such as slot, pocket, hole etc.) and their spatial relationships. Using the aspect graph, we obtain a set of possible sensor settings and determine an optimized set of sensor settings (sensor arrangement) for dimensioning the measurable entities. Since quantization errors and displacement errors are inherent in an active vision system, we analyze and model the density functions of these errors based on their characteristics and use them to determine the accuracy of inspection for a given sensor setting. In addition, we utilize hierarchical interval constraint networks for tolerance design. We redefine network satisfaction and constraint consistency for the application in tolerance design and develop new forward and backward propagation techniques for tolerance analysis and tolerance synthesis, respectively.
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