PRINTED CIRCUIT BOARD DEFECT DETECTION USING MACHINE LEARNING

Abstract

As technology advances, printed circuit boards (PCBs) add more components and change their layout. One of the most important quality control procedures is PCB surface inspection, as small defects in signal traces can cause major damage to the system. Due to the disadvantages of manual scanning, great efforts have been made to automate scanning using high-resolution CCD or CMOS sensors. In traditional machine vision approaches, it is always difficult to determine pass/fail criteria based on small failure examples. the development of improved sensor technology. To solve this problem, we propose an advanced PCB inspection solution based on a jump-related convolutional autoencoder. A deep autoencoder model is trained to separate imperfect original images from defective ones. The location of defects is determined by comparing the decoded image with the input image. In the first production, we scaled the correct representation to improve the performance of training samples through a small and unbalanced database. The printed circuit board (PCB), which is the basic structure for electronic devices, is very important to the electronics industry. PCB quality and reliability must be ensured, but manual inspection methods are often labor-intensive and error-prone. This study presents a new machine learning (ML) method for PCB fault detection. To automatically detect defects, we use advanced ML models such as Convolutional Neural Networks (CNNs) on a large database of PCB images marked for defects. To provide reliable and accurate detection results, our research focuses on data preparation, feature extraction, model selection, and robust validation. The results show that ML-based PCB defect detection is effective, which enables better quality control. electronics manufacturing industry. The performance of our system has been carefully evaluated, showing good accuracy and efficiency in detecting defects using precise validation methods. This research is an important step towards automating PCB inspection, improving the reliability of electronic products, and reducing production costs. It also laid the groundwork for advances in quality control and defect prevention in electronics manufacturing. Added ML-based PCB defects These findings are expected to revolutionize quality assurance procedures as the electronics industry evolves and open the door to more efficient, error-free, and costeffective electronics manufacturing.