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Neural network diagnosis for visual inspection in printed circuit boards

In this paper we present an Automatic Optical Inspection system to diagnose Printed Circuit Boards mounted in Surface Mounting Technology. The diagnosis task is handled as a classification problem with a neural network approach. The Printed Circuit Board tested images are preprocessed by means of several methods to reduce the amount of data to feed to the neural networks. We compare the results obtained in the diagnosis for all methods. The Automatic Optical Inspection system seems to be a good solution in an industrial application because of the low cost, very fast diagnosis and easiness to set-up and handle.