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

2025-09-26

AI Summary

Manual disassembly of end-of-life smartphones suffers from low efficiency and high operational costs due to model heterogeneity and labor dependency. Method: This study proposes a low-cost, rapidly deployable modular “triple-integrated” automation system comprising adaptive cutting, vision-guided robotic sorting, and fully automated battery extraction. Contribution/Results: It achieves, for the first time, high-precision selective disassembly across mixed smartphone models—overcoming limitations of conventional model-specific solutions. Leveraging lightweight mechanical design and intelligent control algorithms, the system processes over 120 units per hour with an average disassembly success rate of 98.9%. Critically, it transforms previously unprofitable disassembly operations into economically viable ones, significantly enhancing material recovery rates and economic feasibility. The system demonstrates strong scalability and readiness for industrial deployment.

Technology Category

• Intelligent Robots: Manipulation
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Computer Vision: Multi-modal Vision

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

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.

Problem

• Automating selective disassembly of end-of-life phones to reduce manual labor
• Addressing high variability in phone models for efficient component recovery
• Transforming unprofitable manual disassembly into economically viable automated process

Innovation

• Adaptive cutting system for selective disassembly
• Vision-based robotic sorting system for components
• Automated battery removal system for safety