CAVIS: CAD-based automated visual inspection system

Automated visual inspection promises to play an important role in the factory of the future. A prototype automatic computer vision inspection system was developed for the Quick Turnaround Cell (QTC) at Purdue University. The objective of the QTC project is to integrate design, process planning, cell control, and inspection functions into a manufacturing system that quickly produces parts using little operator knowledge and intervention. This thesis focuses on the vision inspection module of the QTC project. To achieve a truly flexible automated visual inspection system, an interface between computer vision processes and CAD databases is an essential step. A design feature-based representation which includes dimensions and tolerances of the part is introduced as the part specification. A 3-D boundary representation CAD model is generated from the high level description. A CAD interface system that understands the geometric shape of the part based on the CAD model generates a vision data base and serves as a front end to the inspection planning system. This planning system automatically generates inspection and recognition procedures from the design data. The recognition planning subsystem uses rules to select the important vision features from the given CAD data base, generates a list of simultaneously visible features, and suggests appropriate matching constraints. The inspection planning subsystem interprets each engineering specification of the part and provides proper inspection procedures. The on-line inspection subsystem executes programs based on the planning results and returns information about the part based on all the dimensions which are measured to subpixel accuracy. Thus, after the design cycle, parts can be thoroughly inspected with no technical decisions or programming required. Finally, results of experiments produced by the current implementation of the system are illustrated