Deep Learning-Enabled Robotic Disassembly for Sustainable E-Waste Recycling
Abstract
Electronic waste is increasing at a pace that exceeds current recycling capabilities. Most recycling facilities still rely on slow, hazardous manual labor or rigid robotic systems limited to specific device types. This study presents a deep learning–enabled robotic disassembly framework that integrates real-time object detection using YOLOv5 and semantic segmentation using U-Net with a modular robotic arm capable of dynamic tool switching.
A balanced dataset of twelve thousand four hundred fifty annotated images covering six device types and nine component categories was developed and evaluated using fivefold cross-validation to ensure robustness. The integrated system achieved a mean average precision of ninety-four point six percent for detection and a disassembly success rate of ninety-two point three percent, processing devices forty percent faster than manual operations.
When tested on one hundred fifty mixed e-waste devices, the system consistently maintained ninety-six point one percent material purity, outperforming both rule-based robotic systems and detection-only baselines. By merging computer vision intelligence with adaptive robotic mechanics, this work demonstrates a scalable path toward faster, safer, and more sustainable electronic waste recycling.
References
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