Placement sequence identification using artificial neural networks in surface mount PCB assembly

The widespread use of automation in the printed circuit board (PCB) assembly domain has been dictated by the increasing density of components 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 problems related to these systems requires the efficient use of extensive specialised knowledge. Expert (or knowledge-based) systems have become a widely accepted problem solving methodology for the surface mount PCB 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 expert systems. The surface mount component (SMC) placement process plays 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 location and tooling and nozzle optimisation. This research studied the use of artificial neural networks as a complement to expert systems in PCB 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 problem was designed, implemented, and validated. Artificial intelligence based technologies such as expert systems and artificial neural networks were used in a mutually supportive manner to solve a complex problem within the surface mount PCB assembly domain.