PLACEMENT SEQUENCE IDENTIFICATION USING ARTIFICIAL NEURAL NETWORKS INSURFACE-MOUNT PCB ASSEMBLY

The widespread use of automation in the printed circuit board (PCB) as sembly domain has been dictated by the increasing density of component s on PCBs coupled with the continual decrease in component lead pitch, greater product mix, smaller volumes, quality considerations, and the increased cost of labour. However, these advances in technology have also resulted in automated systems that are complex, and solving probl ems related to these systems requires the efficient use of extensive s pecialised knowledge. Expert (or knowledge-based) systems have become a widely accepted problem solving methodology for the surface mount PC B assembly domain. Nevertheless, problems in the PCB assembly domains are frequently unstructured, ill-defined, and difficult to communicate . Artificial neural networks provide a novel approach and an advanced technology to deal with the weaknesses and problems associated with ex pert systems. The surface mount component (SMC) placement process play s a vital and influential part in determining the throughput time of a PCB assembly line. It is important to identify an efficient component placement sequence while considering constraints such as feeder locat ion and tooling and nozzle optimisation. This research studied the use of artificial neural networks as a complement to expert systems in PC B assembly. A prototype decision support system that combined the use of artificial neural networks and expert system techniques to identify a near optimal solution for the surface mount placement sequence prob lem was designed, implemented, and validated. Artificial intelligence based technologies such as expert systems and artificial neural networ ks were used in a mutually supportive manner to solve a complex proble m within the surface mount PCB assembly domain.