Automatic component recognition and defect detection in electronic board recycling process

The growing problem of Waste Electrical and Electronic Equipment (WEEE), or e-waste, posessignificant environmental and resource challenges that require innovative management strategies.The main objective of this research is to develop an automated system that can detect defects inelectronic components, enabling the reuse of electronic boards and reducing their environmentalfootprint. A comprehensive methodology using Convolutional Neural Networks (CNNs) and MachineLearning (ML) is proposed, targeting to inspect different customised boards with different compo-nent layouts. The approach exploits the capabilities of advanced pattern recognition and predictiveanalysis to identify faults in electronic components. To preliminarily validate and demonstrate theeffectiveness of this methodology, a simple case study was considered. Extensive testing on this casestudy confirmed the potential of the method, achieving a 95% confidence level of defect detection.The proposed methodology aims to extend the life of electronic devices, improve maintenancestrategies and promote sustainable use. This strategic application addresses the current challenges ofmanaging e-waste and paves the way for future advances in managing it.