A survey on potentials, pathways and challenges of large language models in new-generation intelligent manufacturing

IF 9.1 1区计算机科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Robotics and Computer-integrated Manufacturing Pub Date : 2024-09-26 DOI:10.1016/j.rcim.2024.102883

Chao Zhang , Qingfeng Xu , Yongrui Yu , Guanghui Zhou , Keyan Zeng , Fengtian Chang , Kai Ding

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引用次数: 0

Abstract

Nowadays, Industry 5.0 starts to gain attention, which advocates that intelligent manufacturing should adequately consider the roles and needs of humans. In this context, how to enhance human capabilities or even liberate humans from the processes of perception, learning, decision-making, and execution has been one of the key issues to be addressed in intelligent manufacturing. Large language models (LLMs), as the breakthrough in new-generation artificial intelligence, could provide human-like interaction, reasoning, and replies suitable for various application scenarios, thus demonstrating significant potential to address the above issues by providing aid or becoming partners for humans in perception, learning, decision-making, and execution in intelligent manufacturing. The combination of LLMs and intelligent manufacturing has inherent advantages and is expected to become the next research hotspot. Hence, this paper primarily conducts a systematic literature review on the application of LLMs in intelligent manufacturing to identify the promising research topics with high potential for further investigations. Firstly, this paper reveals the concept, connotation, and foundational architecture of LLMs. Then, several typical and trending interdisciplinary LLM applications, such as healthcare, drug discovery, social & economic, education, and software development, are summarized, on which an LLM-enabled intelligent manufacturing architecture is designed to provide a reference for applying LLMs in intelligent manufacturing. Thirdly, the specific pathways for applying LLMs in intelligent manufacturing are explored from the perspectives of design, production, and service. Finally, this paper identifies the limitations, barriers, and challenges that will be encountered during the research and application of LLMs in intelligent manufacturing, while providing potential research directions to address these limitations, barriers, and challenges.

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新一代智能制造中大型语言模型的潜力、途径和挑战调查

如今，工业 5.0 开始受到关注，它主张智能制造应充分考虑人的作用和需求。在此背景下，如何增强人的能力，甚至将人从感知、学习、决策和执行等过程中解放出来，成为智能制造需要解决的关键问题之一。大型语言模型（LLMs）作为新一代人工智能的突破口，可以提供适合各种应用场景的类人交互、推理和回复，从而在智能制造的感知、学习、决策和执行过程中为人类提供帮助或成为人类的伙伴，在解决上述问题方面展现出巨大的潜力。LLM 与智能制造的结合具有先天优势，有望成为下一个研究热点。因此，本文主要对 LLMs 在智能制造中的应用进行了系统的文献综述，以确定具有较大研究潜力的研究课题。首先，本文揭示了 LLM 的概念、内涵和基础架构。然后，总结了几个典型的、趋势性的跨学科 LLM 应用，如医疗保健、药物发现、社会&；经济、教育、软件开发等，并在此基础上设计了一个 LLM 支持的智能制造架构，为 LLM 在智能制造中的应用提供参考。第三，从设计、生产和服务的角度探讨了在智能制造中应用 LLM 的具体途径。最后，本文指出了在智能制造中研究和应用 LLM 会遇到的限制、障碍和挑战，同时提供了解决这些限制、障碍和挑战的潜在研究方向。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Robotics and Computer-integrated Manufacturing 工程技术-工程：制造

CiteScore

24.10

自引率

13.50%

发文量

160

审稿时长

50 days

期刊介绍： The journal, Robotics and Computer-Integrated Manufacturing, focuses on sharing research applications that contribute to the development of new or enhanced robotics, manufacturing technologies, and innovative manufacturing strategies that are relevant to industry. Papers that combine theory and experimental validation are preferred, while review papers on current robotics and manufacturing issues are also considered. However, papers on traditional machining processes, modeling and simulation, supply chain management, and resource optimization are generally not within the scope of the journal, as there are more appropriate journals for these topics. Similarly, papers that are overly theoretical or mathematical will be directed to other suitable journals. The journal welcomes original papers in areas such as industrial robotics, human-robot collaboration in manufacturing, cloud-based manufacturing, cyber-physical production systems, big data analytics in manufacturing, smart mechatronics, machine learning, adaptive and sustainable manufacturing, and other fields involving unique manufacturing technologies.