A flexible visual inspection system based on neural networks
International Journal of Systems Science - Innovative Production Machines and Systems, Guest Editors: Duc-Truong Pham, Anthony Soroka and Eldaw Eldukhri
Development of a soldering quality classifier system using a hybrid data mining approach
Expert Systems with Applications: An International Journal
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In this paper, a novel framework is proposed to inspect the placement quality of surface mount technology devices (SMDs), immediately after they have been placed in wet solder paste on a printed circuit board (PCB). The developed approach involves the indirect measurement of each lead displacement with respect to its ideal position, centralized on its pad region. This displacement is inferred from area measurements on the raw image data of the lead region through a classification process. To increase the accuracy in the computation of the lead displacement, we introduce a combined classification/estimation process, in which the individual lead displacement classifications are viewed as measurements (or observations) of the same physical quantity i.e., the displacement of the entire component as a rigid body. Certain geometric relations connecting lead shifts to component displacement are also derived. Employing these relations we can infer a new refined measurement of the shift of each individual lead, a quantity crucial to the calculation of the quality measures. Experimental results highlight the potential of the developed algorithm.