Leveraging Defect Trend Analysis for Sustainable Printed Circuit Board and Assembly (PCBA) Quality Assurance: A Low-Cost Portable Smart Inspection Solution for Small-Scale Electronics Manufacturers
DOI:
https://doi.org/10.65141/ject.v2i2.n1Keywords:
Printed Circuit Board and Assembly, defect analysis, quality assurance inspection, Artificial Intelligence, computer visionAbstract
This research addresses the challenges in quality assurance (QA) for low-volume Printed Circuit Board and Assembly (PCBA) production, where manual inspection often leads to inconsistencies, limited traceability, and delays. Analyzing defect trends from 2019 to 2024 across six suppliers, the study identified common issues such as missing components, misalignment, and solder defects. This defect analysis introduces the concept of developing a low-cost, portable, AI-driven PCBA QA inspection system that would utilize a high-resolution microscope, Python-based computer vision, and object detection tools like YOLO to provide an affordable, scalable, and customizable solution ideal for small-scale manufacturers, SMEs, and research environments. This conceptual system is intended to enhance inspection efficiency, accuracy, and traceability while promoting sustainable engineering practices. Future research would focus on developing and implementing this system, including AI-based defect classification and conducting pilot studies to validate its performance in real-world settings. This system has significant implications for SMEs in electronics manufacturing, providing an accessible, cost-effective solution to improve product quality and support the digital transformation of manufacturing operations.
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