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Automated visual inspection system based on HAVNET architecture

In this study, the HAusdorff-Voronoi NETwork (HAVNET) developed at the UMR Smart Engineering Systems Lab is tested in the recognition of mounted circuit components commonly used in printed circuit board assembly systems. The automated visual inspection system used consists of a CCD camera, a neural network based image processing software and a data acquisition card connected to a PC. The experiments are run in the Smart Engineering Systems Lab in the Engineering Management Dept. of the University of Missouri-Rolla. The performance analysis shows that the vision system is capable of recognizing different components under uncontrolled lighting conditions without being effected by rotation or scale differences. The results obtained are promising and the system can be used in real manufacturing environments. Currently the system is being customized for a specific manufacturing application.