Automated Optical Inspection (AOI) systems are rapidly replacing slow and tedious manual inspections of Printed Circuit Boards (PCBs). In an AOI system, a minicamera traverses the PCB in a pre-defined travel path, snapping shots of all the PCB components or nodes, at pre-defined locations. The images are then processed and information about the different nodes is extracted and compared against ideal standards stored in the AOI system. This way, a flawed board is detected. Minimizing both the number of images required to scan all the PCB nodes, and the path through which the camera must travel to achieve this, will minimize the image acquisition time and the traveling time, and thus the overall time of inspection. This consequently both reduces costs and increases production rate. This work breaks down this problem into two sub-problems: The first is a clustering problem; the second a travelling salesman sequencing problem. In the clustering problem, it is required to divide all the nodes of a PCB into the minimum number of clusters. The cluster size is constrained by the given dimensions of the camera’s scope or Field of Vision (FOV). These dimensions determine the dimension of the inspection windows. It is thus required to find the minimum number of inspection windows that will scan all the nodes of a PCB, and their locations. Genetic algorithms are applied in a two-step approach with special operators suited for the problem. A continuous Genetic Algorithm (GA) is applied to find the optimum inspection window locations that cover one node and as many other nodes as possible. A discrete GA is then applied to eliminate redundant inspection windows leaving the minimum number of windows that cover all nodes throughout the PCB. In the second sub-problem, an Ant Colony Optimization (ACO) method is used to find the optimum path between the selected inspection windows. The method proposed in this paper is compared against relevant published work, and it is shown to yield better results.

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