Redefining End-of-Life: Intelligent Automation for Electronics Remanufacturing Systems

TL;DR

This paper reviews how robotics, AI, and control systems can advance intelligent automation in electronics remanufacturing, addressing challenges of uncertainty and variability to support a circular economy.

Contribution

It provides a comprehensive overview of emerging automation techniques and future research directions for scalable, safe, and intelligent EoL electronics remanufacturing.

Findings

Highlights the role of multimodal perception and decision-making under uncertainty.

Discusses the integration of foundation models, human-in-the-loop, and digital twins.

Identifies key challenges and opportunities in intelligent remanufacturing systems.

Abstract

Remanufacturing is fundamentally more challenging than traditional manufacturing due to the significant uncertainty, variability, and incompleteness inherent in end-of-life (EoL) products. At the same time, it has become increasingly essential and urgent for facilitating a circular economy, driven by the growing volume of discarded electronic products and the escalating scarcity of critical materials. In this paper, we review the existing literature and examine the key challenges as well as emerging opportunities in intelligent automation for EoL electronics remanufacturing, providing a comprehensive overview of how robotics, control, and artificial intelligence (AI) can jointly enable scalable, safe, and intelligent remanufacturing systems. This paper starts with the definition, scope, and motivation of remanufacturing within the context of a circular economy, highlighting its societal and environmental significance. Then it delves into intelligent automation approaches for disassembly, inspection, sorting, and component reprocessing in this domain, covering advanced methods for multimodal perception, decision-making under uncertainty, flexible planning algorithms, and force-aware manipulation. The paper further reviews several emerging techniques, including large foundation models, human-in-the-loop integration, and digital twins that have the potential to support future research in this area. By integrating these topics, we aim to illustrate how next-generation remanufacturing systems can achieve robust, adaptable, and efficient operation in the face of complex real-world challenges.