Fast plug-in capacitors polarity detection with morphology and SVM fusion method in automatic optical inspection system

Abstract

Defect detection is a critical element in the PCB manufacturing process. Different from surface mount PCB, the components on the plug-in PCB are usually installed manually, resulting in significant errors. Furthermore, because plug-in components have many different types and irregularities, it is difficult to detect their flaws. We make contributions in the following two aspects: (1) a framework and measurement method of a light source and make a cheap and efficient lighting system; (2) a fusion algorithm based on machine learning and morphology for polarity detection of plug-in capacitors. The capacitor is detected using SVM and fused with the polar coordinate expansion method. The AOI system and the proposed fusion algorithm have been applied to the production line, with an accuracy of 99.73% and a missed detection rate of only 0.12%, according to the field test of the production line.