Abstract
The ever increasing use of surface mount technology in printed circuit board assembly has considerably increased the importance of automated solder joint inspection. This paper documents our experience with the development of an experimental automated solder joint inspection system. Dominant solder joint defect classes are identified. Three-dimensional geometric models of these defects are created and used to test a non-parametric classification algorithm and optimal feature selection scheme. The classification algorithm and optimal feature selection scheme are then implemented on an experimental system using a laser line and vision system for solder joint data acquisition. The capability and limitations of this system are assessed through experimentation with actual solder joint defects.
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