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Abstract Disassembly is an essential process for the recovery of end-of-life (EOL) electronics in remanufacturing sites. Nevertheless, the process remains labor-intensive due to EOL electronics’ high degree of uncertainty and complexity. The robotic technology can assist in improving disassembly efficiency; however, the characteristics of EOL electronics pose difficulties for robot operation, such as removing small components. For such tasks, detecting small objects is critical for robotic disassembly systems. Screws are widely used as fasteners in ordinary electronic products while having small sizes and varying shapes in a scene. To enable robotic systems to disassemble screws, the location information and the required tools need to be predicted. This paper proposes a computer vision framework for detecting screws and recommending related tools for disassembly. First, a YOLOv4 algorithm is used to detect screw targets in EOL electronic devices and a screw image extraction mechanism is executed based on the position coordinates predicted by YOLOv4. Second, after obtaining the screw images, the EfficientNetv2 algorithm is applied for screw shape classification. In addition to proposing a framework for automatic small-object detection, we explore how to modify the object detection algorithm to improve its performance and discuss the sensitivity of tool recommendations to the detection predictions. A case study of three different types of screws in EOL electronics is used to evaluate the performance of the proposed framework.
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This content will become publicly available on September 1, 2026
AI-driven value assessment for intelligent remanufacturing; Advances in remanufacturing 2024: Proceedings of VIII International Workshop on Autonomous Remanufacturing (Lecture Notes in Mechanical Engineering)
This paper aims to introduce an Artificial Intelligence (AI) guided computational framework for the automatic identification, inspection, assessment, and remanufacturing of end-of-use products. The proposed framework consists of three main steps: (1) developing computer vision and image processing algorithms for analyzing product teardown images, (2) quantifying the economic and environmental value of remanufacturing from product images, and (3) developing recommender algorithms to identify the best recovery decision for each device. The paper discusses the importance of advancing object detection, image segmentation, and machine learning algorithms to automatically compute the value embedded in discarded items and developing recommendation systems to determine remanufacturing operations from product configurations. The main focus of the paper is on the value assessment and remanufacturing of electronic waste (e-waste). The paper emphasizes the need for developing object detection for identifying small objects (e.g., screws, bolts, snaps) and overlapped components (e.g., cables, printed circuit boards) standard in the design of consumer electronics by incorporating product shapes and features. The proposed value assessment framework has applications beyond remanufacturing and can be used in take-back programs and other business models that benefit from product serialization and assessment of individual devices.
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- PAR ID:
- 10617555
- Editor(s):
- Rickli, Jeremy
- Publisher / Repository:
- Springer
- Date Published:
- ISSN:
- 2195-4364
- ISBN:
- 978-3-031-92425-5
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Disassembly is an essential process for the recovery of end-of-life (EOL) electronics in remanufacturing sites. Nevertheless, the process remains labor-intensive due to EOL electronics' high degree of uncertainty and complexity. The robotic technology can assist in improving disassembly efficiency, however, the characteristics of EOL electronics pose difficulties for robot operation, such as removing small components. For such tasks, detecting small objects is critical for robotic disassembly systems. Screws are widely used as fasteners in ordinary electronic products while having small sizes and varying shapes in a scene. To achieve robotic disassembly of screws, the location information and the required tools need to be predicted. This paper proposes a framework to automatically detect screws and recommend related tools for disassembly. First, the YOLOv4 algorithm is used to detect screw targets in EOL electronic devices, and then a screw image extraction mechanism is executed based on the position coordinates predicted by YOLOv4. Second, after obtaining the screw images, the EfficientNetv2 algorithm is applied for screw shape classification. In addition to proposing a framework for automatic small-object detection, we explore how to modify the object detection algorithm to improve its performance and discuss the sensitivity of tool recommendations to the detection predictions. A case study of three different types of screws is used to evaluate the performance of the proposed framework.more » « less
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