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arXiv · 2406.01893

Large Language Model-Enabled Multi-Agent Manufacturing Systems

Jonghan Lim·Birgit Vogel-Heuser·Ilya Kovalenko
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Abstract

Traditional manufacturing faces challenges adapting to dynamic environments and quickly responding to manufacturing changes. The use of multi-agent systems has improved adaptability and coordination but requires further advancements in rapid human instruction comprehension, operational adaptability, and coordination through natural language integration. Large language models like GPT-3.5 and GPT-4 enhance multi-agent manufacturing systems by enabling agents to communicate in natural language and interpret human instructions for decision-making. This research introduces a novel framework where large language models enhance the capabilities of agents in manufacturing, making them more adaptable, and capable of processing context-specific instructions. A case study demonstrates the practical application of this framework, showing how agents can effectively communicate, understand tasks, and execute manufacturing processes, including precise G-code allocation among agents. The findings highlight the importance of continuous large language model integration into multi-agent manufacturing systems and the development of sophisticated agent communication protocols for a more flexible manufacturing system.

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Jonghan Lim, Birgit Vogel-Heuser, Ilya Kovalenko. 2024-06-04. Large Language Model-Enabled Multi-Agent Manufacturing Systems. https://arxiv.org/abs/2406.01893

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