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This paper discusses the integration of an automated neural network-based vision inspection system with robots to detect and report IC lead defects on-line. The vision system consists of custom software that contains a neural network database for each IC to be inspected on a PCB. The vision system uses gray scale images and a single layer neural network with three outputs based on defect criteria. Each IC has different inspection area, thus, the input vector varies for each ICs. The IC networks were trained with Matlab’s Bayesian regularization module. This module was used because it prevents over and under training the image data. Performance of each of the networks investigated was found to be 100% based on the defect criteria. An on-line robotic inspection monitoring system has been developed, using ProE, C++ and OpenGL software 1,2 . Technical issues and collaborative efforts in the execution of this capstone project are discussed in the paper. This research project was embarked as a collaborative effort between the senior design project students of the University of Texas at Brownsville and a graduate student of manufacturing engineering at the University of Pan American.

A Vision And Robot Based Online Monitoring Of Defects In Electronics Manufacturing A Collaborative Effort In Capstone Project