Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies
大規模言語モデルによる再製造自動化の変革：ケーススタディを交えた将来展望分析 (AI 翻訳)
Chang Liu, Sara Behdad, Prabhakar Pagilla, Xiao Liang, Minghui Zheng
🤖 gxceed AI 要約
日本語
本論文は、循環経済における再製造工程の自動化に大規模言語モデル（LLM）を活用する可能性を探る。再製造はEoL製品の価値保全に有効だが、専門知識への依存が課題である。LLMの能力を再製造に応用する概念枠組みReManGPTを提案し、EVバッテリー、電子廃棄物、電動モーターの3事例で適用可能性を示す。実装障壁と将来研究方向も議論する。
English
This paper explores the potential of large language models (LLMs) to automate remanufacturing processes within the circular economy. It proposes a conceptual framework, ReManGPT, and illustrates its application through case studies on EV batteries, e-waste, and electric motors. The paper discusses implementation barriers and future research directions, including LLM-assisted human operation and language-action models for robotics.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では、循環経済への移行が政策課題となっており、再製造は資源効率向上に寄与する。本論文のLLM活用は、日本の製造業における熟練技術者不足への対応策として有望であり、サーキュラーエコノミー関連の開示や規制対応にも示唆を与える。
In the global GX context
Globally, remanufacturing is recognized as a key strategy for circular economy and climate mitigation. This paper's framework for LLM-driven automation could enhance efficiency and scalability of remanufacturing, aligning with international sustainability goals and potentially influencing disclosure and reporting standards for circular economy practices.
👥 読者別の含意
🔬研究者:LLMを再製造に応用する研究の先駆けであり、今後の研究方向性を示す。
🏢実務担当者:再製造プロセスの自動化を検討する企業は、LLMの活用可能性を評価するための枠組みを得られる。
🏛政策担当者:循環経済政策の推進において、LLM技術の活用が再製造産業の競争力向上に寄与する可能性を示す。
📄 Abstract（原文）
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.
🔗 Provenance — このレコードを発見したソース
- arXiv https://arxiv.org/abs/2608.04854first seen 2026-08-06 04:11:21
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