RAISE: A Robot-Assisted Selective Disassembly and Sorting System for End-of-Life Phones

Liu, Chang, Balasubramaniam, Badrinath, Yancey, Neal, Severson, Michael, Shine, Adam, Bove, Philip, Li, Beiwen, Liang, Xiao, Zheng, Minghui

arXiv.org Artificial Intelligence 

Abstract--End-of-Life (EoL) phones significantly exacerbate global e-waste challenges due to their high production volumes and short lifecycles. Disassembly is among the most critical processes in EoL phone recycling. However, it relies heavily on human labor due to product variability. Consequently, the manual process is both labor-intensive and time-consuming. In this paper, we propose a low-cost, easily deployable automated and selective disassembly and sorting system for EoL phones, consisting of three subsystems: an adaptive cutting system, a vision-based robotic sorting system, and a battery removal system. The system can process over 120 phones per hour with an average disassembly success rate of 98.9%, efficiently delivering selected high-value components to downstream processing. It provides a reliable and scalable automated solution to the pressing challenge of EoL phone disassembly. Additionally, the automated system can enhance disassembly economics, converting a previously unprofitable process into one that yields a net profit per unit weight of EoL phones. E-waste presents a global challenge due to its rapid growth, high resource value, and the severe environmental and health risks from improper recycling and hazardous substances [1-3]. Global e-waste surged to a record 62 million tonnes in 2022 and is expected to reach 82 million tonnes by 2030 [4]. Recycling converts e-waste components into valuable raw materials, which is critical for addressing the escalating e-waste problem and supporting a sustainable circular economy [5-10]. Nevertheless, only 22.3 % of e-waste was recorded as recycled in 2022 [4]. The high human labor cost and health risk concerns are the major challenges associated with the recycling process [11]. This material is based upon work supported by the REMADE Institute, USA (21-01-RM-5083).