Artificial neural network approach in printed circuit board assembly

In this study, automation of the circuit board assembly process is considered using artificial neural networks with knowledge-based systems. Basic issues in achieving intelligent control that can adapt to changing conditions in the assembly process are discussed. The feasibility of using neural networks for pattern recognition and optimum component insertion sequence generation is examined. The study provides a basic foundation for designing a conceptual architecture for adaptive intelligent control of circuit board assembly. Real-time testing of component recognition is conducted using adaptive resonance theory (ART 1) as a neural network paradigm.