<![CDATA[This research pioneers the application of artificial intelligence (AI) methodologies—machine learning, deep learning, hybrid models, transfer learning, and edge AI deployment—in enhancing bubble detection within conformal coatings, a critical as- pect of electronics manufacturing quality control. By addressing the limitations of traditional detection methods, our work offers a novel approach that significantly improves automation, accuracy, and speed, thereby ensuring the reliability of electronic assemblies and contributing to economic and safety benefits. We navigate through the challenges of creating diverse datasets, system robustness, and the imperative for industry-wide standardization, proposing strategies for overcoming these obstacles. Our findings highlight the transformative impact of AI on quality control processes, demonstrating substantial advancements in detection capabilities. Furthermore, we advocate for future research, development, and collaboration to extend these AI-driven improvements across the manufacturing spectrum. This study underscores the potential of AI to revolutionize electronics manufacturing, emphasizing the need for continued innovation and standardization to realize safer, more efficient, and cost-effective production methodologies.]]>
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