Component Identification and Defect Detection of Printed Circuit Board using Artificial Intelligence

Abstract

This Printed circuit board(PCBs) flaws are identified and detected using artificial intelligence. The necessity for effective and precise inspection procedures in the production of electronic devices is a result of the rising demand for high-quality electronic products. The suggested technique makes use of a deep learning model that was trained on digital microscope pictures of PCBs. The AI model can reliably recognize different components on the PCB and find any flaws, including broken trace lines, missing components, and improper component placement. With an average precision of 99.6% for component identification and an average precision of 98.7% for defect detection, the results demonstrate that the AI model performs with a high degree of accuracy. The effectiveness and dependability of PCB inspection and quality control processes can be greatly increased by putting this strategy into practice