Muhammad Mohsin, Stefano Rovetta
The rapid advancement of technology has led to a substantial increase in Waste Electrical and Electronic Equipment (WEEE), which poses significant environmental threats and increases pressure on the planet’s limited natural resources. In response, Artificial Intelligence (AI) has emerged as a key enabler of the Circular Economy (CE), particularly in improving the speed and precision of waste sorting through machine learning and computer vision techniques. Despite this progress, to our knowledge, no comprehensive, systematic review has focused specifically on the role of AI in disassembling and recycling Waste-Printed Circuit Boards (WPCBs). This paper addresses this gap by systematically reviewing recent advancements in AI-driven disassembly and sorting approaches with a focus on machine learning and vision-based methodologies. The review is structured around three areas: (1) the availability and use of datasets for AI-based WPCB recycling; (2) state-of-the-art techniques for selective disassembly and component recognition to enable fast WPCB recycling; and (3) key challenges and possible solutions aimed at enhancing the recovery of critical raw materials (CRMs) from WPCBs.
@article{e1198986-8acb-43ae-8547-17bb7aa146fc,
title={Artificial Intelligence Approach for Waste-Printed Circuit Board Recycling: A Systematic Review},
author={Muhammad Mohsin and Stefano Rovetta},
year={2025},
language={English}
}TY - JOUR TI - Artificial Intelligence Approach for Waste-Printed Circuit Board Recycling: A Systematic Review AU - Muhammad Mohsin AU - Stefano Rovetta PY - 2025 LA - English ER -
Md Kaviul Islam, Anirudha Karati
The printed circuit board (PCB), a central component of most electronic devices, represents a significant fraction of the electronic product waste str
Xabier Sarrionandia, Javier Nieves
Metallographic analyses of nodular iron casting methods are based on visual comparisons according to measuring standards. Specifically, the microstruc
Jie Li, Jianxin He
Rotary kiln shell temperature monitoring is essential for metallic shell protection and refractory lining maintenance in high-temperature industrial p
Jirang Cui, Hans Jørgen Roven
Rapid growth in electronic equipment consumption has generated large quantities of electronic waste containing hazardous substances and high-value met