Dataset Citra Papan Sirkuit Tercetak dengan Komponen yang Terbakar

Abstract

The application of artificial intelligence, especially in the automatic optical inspection of printed circuit boards or PCBs, is increasingly being carried out by researchers. Unfortunately, the data used to train and test artificial intelligence models is synthetic data. Printed circuit boards in good condition are imaged and then changed by software to give the impression of defects. In addition, the type of damage is limited to pre-operation, namely when the PCB is not yet operational. After the PCB is operational, damage can occur, for example, burned components. Until now, there is no data set of PCB images with burned components. This study, therefore, explores data retrieval techniques that can produce the required data set. This data collection technique includes hardware setup and PCB data sources. Based on the exploration results, it is concluded that a trinocular digital microscope with high resolution can produce sharp PCB images. The obstacle that arises is the difficulty of getting PCBs with burned components. The solution was obtained by referring to the PCB repair video from the Youtube channel. Several data were collected and tested with EfficientDet with 90% mAP.