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ABSTRACT
Automated flaw detection in PCB manufacturing has undergone a transformative shift with theintegration of deep learning models, ensuring meticulous and error-free outputs. This researchemphasizes the challenges posed by industrial images in PCB flaw detection, such as contaminationand degradation, and evaluates various defect detection techniques. Each method is scrutinized for itsunique attributes, and experiments are conducted to understand the influence of various factors,including detection strategies and image quality. The paramount importance of PCB quality in numerous devices is underscored by the potentialrepercussions of malfunctions due to defects like missing components. Traditional manualinspections, constrained by the intricate dimensions of PCBs, often result in oversights. To addressthis, the study introduces an AI chatbot, equipped with image recognition. This chatbot, powered bya Convolutional Neural Network (CNN), can adeptly discern between fault-free and defective PCBimages, marking a significant advancement over conventional algorithms. Beyond mere defect detection, the research also delves into environmental threats to PCBs, particularly failures triggered by electrochemical migration and leakage currents. Through advancedmachine learning techniques, predictive models are devised, offering insights into potential PCBsurface degradation from various environmental factors. By amalgamating image processing withdeep learning, this project envisions a future where PCB fault detection is not only automated butalso remarkably precise, paving the way for unparalleled manufacturing quality and efficiency
