Automated Defect Detection in Printed Circuit Boards: Exploring the Impact of Convolutional Neural Networks on Quality Assurance and Environmental Sustainability in Manufacturing

Abstract

Printed Circuit Boards (PCBs) are essential components in modern electronic devices, requiring high precision and quality to ensure reliability and functionality. Defects in PCBs can lead to significant financial losses and pose safety risks in critical applications. Automated defect detection systems have become vital in maintaining high standards of quality assurance while optimizing manufacturing efficiency. This study explores the impact of Convolutional Neural Networks (CNNs) on defect detection in PCBs, assessing their potential to improve quality control processes and contribute to environmental sustainability. CNNs, known for their ability to process visual data with high accuracy, are applied to detect defects such as cracks, soldering faults, and misalignments on PCB surfaces. Our approach combines CNN-based detection with energy-efficient techniques, reducing power consumption through adaptive power management and workload-specific optimizations. Experimental results demonstrate that the proposed model achieves a detection accuracy of 98.2%, surpassing traditional Automated Optical Inspection (AOI) systems by over 10% while reducing energy consumption by 30% on average. By minimizing defective output and lowering re-inspection needs, this framework supports sustainable manufacturing practices, reducing resource use and emissions. Our findings suggest that integrating CNNs into PCB inspection not only enhances defect detection and overall quality but also aligns with industry goals for greener production practices. This research highlights the importance of combining advanced AI with energy-efficient strategies to develop cost-effective, sustainable solutions in high-precision manufacturing sectors.