Robotic system for automated disassembly of electronic waste: Unscrewing

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing Pub Date : 2025-04-29 DOI:10.1016/j.rcim.2025.103032

Iñaki Díaz , Diego Borro , Olatz Iparraguirre , Martxel Eizaguirre , Frank A. Ricardo , Nicolás Muñoz , Jorge Juan Gil

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Abstract

The increasing volume of electronic Waste from Electrical and Electronic Equipment (WEEE) presents significant environmental and economic challenges. Efficient recycling requires the disassembly of electronic devices, a process that is currently labor-intensive and costly. In this paper, we present a robotic system designed to automate the disassembly process, focusing on the task of unscrewing fasteners commonly found in electronic devices. The system integrates a Universal Robots UR10e robotic arm, an Intel RealSense D405 vision camera, and a custom-designed mechatronic screwdriver. The vision system trains several object detection models to assist robotic control and identify four different types of screw heads. The robot employs force-sensing techniques to align the screwdriver tip with the screw head before unscrewing. Validation is carried out on a real recycled hoverboard, demonstrating the system’s efficiency in automating unscrewing processes. The results can be generalized to other unscrewing operations in various industries.

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自动拆卸电子废物的机器人系统：旋开

来自电气和电子设备（WEEE）的电子废物量的增加提出了重大的环境和经济挑战。有效的回收需要拆解电子设备，这一过程目前是劳动密集型的，而且成本很高。在本文中，我们提出了一个机器人系统，设计用于自动化拆卸过程，重点是在电子设备中常见的紧固件的拆卸任务。该系统集成了Universal Robots UR10e机械臂、Intel RealSense D405视觉摄像头和定制设计的机电螺丝刀。视觉系统训练了几个目标检测模型来辅助机器人控制和识别四种不同类型的螺钉头。该机器人采用力感应技术，在拧开螺丝之前将螺丝刀的尖端与螺丝头对齐。在一个真实的回收悬浮板上进行了验证，证明了该系统在自动拆卸过程中的效率。研究结果可推广到其他行业的旋开操作中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Computer-integrated Manufacturing 工程技术-工程：制造

CiteScore

24.10

自引率

13.50%

发文量

160

审稿时长

50 days

期刊介绍： The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.