AI 中文总结
本文通过前瞻性分析与案例研究，提出ReManGPT概念框架，探讨大语言模型在再制造自动化中的应用，以应对报废产品变异性等挑战，同时分析其部署障碍与未来方向。
AI 中文摘要
随着资源稀缺和环境退化问题日益受到关注，循环经济中对报废（EoL）产品的再制造正受到越来越多的重视。再制造可保留大部分原始制造价值与材料，将报废产品翻新至新品状态。但报废产品的变异性与不确定性使得再制造高度依赖人类专业知识。近期，大语言模型（LLM）展现出从海量非结构化数据中学习、在各类任务中生成专家级输出、以自然语言与人类沟通解读的卓越能力，这些优势与再制造的复杂需求高度契合，可减少对专业知识的依赖。不过，LLM在该领域的作用与研究进展仍未得到充分探索。本文基于对现有与再制造相关的LLM研究的简要批判性综述，对LLM在再制造自动化中的作用展开前瞻性综述与分析；在此基础上，引入ReManGPT作为概念框架，并通过三个代表性案例研究说明该框架在实际再制造场景中选定模块的应用；还分析了电动汽车电池、电子废弃物、电动机这三类代表性再制造应用，以说明所提框架如何应对其领域特定挑战；最后，探讨了将该框架实际部署的当前障碍，并概述未来研究方向，包括LLM辅助人类操作、面向机器人自动化的语言-动作模型。
英文摘要
With growing concerns about resource scarcity and environmental degradation, remanufacturing of end-of-life (EoL) products within the circular economy is attracting increasing attention. Remanufacturing can preserve most of the original manufacturing value and materials while transforming EoL products into like-new condition. However, the variability and uncertainty of EoL products make remanufacturing highly dependent on human expertise. Recently, large language models (LLMs) have demonstrated remarkable capabilities in learning from massive, unstructured datasets, generating expert-level output across various tasks, and communicating with humans in natural language for interpretation. These advantages can align closely with the complex demands of remanufacturing, thereby mitigating the reliance on specialized expertise. However, their roles and research progress in this domain remain underexplored. In this paper, we present a forward-looking review and analysis of the role of LLMs in remanufacturing automation, grounded in a brief critical review of existing LLM-related studies relevant to remanufacturing. Building on this foundation, we introduce ReManGPT as a conceptual framework and use three representative case studies to illustrate selected modules of the framework in practical remanufacturing scenarios. We also analyze three representative remanufacturing applications, electric vehicle batteries, electronic waste, and electric motors, to illustrate how the proposed framework could address their domain-specific challenges. Finally, we discuss the current barriers to deploying this framework in practice and outline future research directions, including LLM-assisted human operation and language-action models for robotic automation.
Journal refRobotics and Computer-Integrated Manufacturing 2027
DOI：10.1016/j.rcim.2026.103361
