System for PCB Defect Detection Using Visual Computing and Deep Learning for Production Optimization

Abstract

With the growing competition between the various manufacturers of electronic products, the quality of the products developed and the consequent confidence in the brand are fundamental factors for the survival of companies. To guarantee the quality of the products in the manufacturing process, it is crucial to identify defects during the production stage of an electronic device. This study presents a system based on traditional visual computing and new deep learning methods to detect defects in electronic devices during the manufacturing process. A prototype of the proposed system was developed and manufactured for direct use in the production line of electronic devices. Tests were performed using a particular smartphone model that had 22 critical components to inspect and the results showed that the proposed system achieved an average accuracy of more than 90% in defect detection when it was directly used in the operational production line. Other studies in this field perform measurements in controlled laboratory environments and identify fewer critical components. Therefore, the proposed method is a real-time high-performance system. Furthermore, the proposed system conforms with the Industry 4.0 goal that process system digitization is essential to improve indicators and optimize production.