Solder joints inspection using neural network and fuzzy rule-based classification

In this paper, we described an approach to automation of visual inspection of solder joint defects of SMC (surface mounted components) on PCB (printed circuit board) by using neural network and fuzzy rule-based classification method. Inherently, the surface of the solder joints is curved, tiny and specular reflective; it induces a difficulty of taking good image of the solder joints. The shape of the solder joints tends to vary greatly with soldering condition and the shapes are not identical to each other, even for solder joints belonging to a set of the same soldering quality. This problem makes it difficult to classify the solder joints according to their qualities. Neural network and fuzzy rule-based classification method is proposed to efficiently make human-like classification criteria of the solder joint shapes. The performance of the proposed approach is tested on numerous samples of commercial computer PCB boards and compared with the human inspector´s performance