Skip to Main Content
Recently, surface mount technology is extensively used in the production of printed circuit boards due to the high level of miniaturization and to the increase of density in the electronic device integration. In such production process several defects could occur on the final electronic components, compromising its correct working. In this paper a neurofuzzy solution to process information deriving from an automatic optical system is proposed. The designed system provides a global quality index of a solder joint, starting from the assessment of a human inspector. This target is achieved by reproducing the modus operandi of the expert, evaluating the area, the shape and the barycentre position of a solder joint. The proposed architecture is constituted by three supervised neural networks and two fuzzy rule-based modules which automate expert's work and provide a refined evaluation of the quality. The considered solution presents some attractive advantages: a complex acquisition system is not needed, equipment costs could be reduced by shifting the assessment of a solder joint on the fuzzy parts. Moreover, intermediate variables used in the method could be employed as control parameters in the production process under analysis.