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

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.