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.

传统制造业面临适应动态环境和快速响应制造变化的挑战。多智能体系统的使用提高了适应性和协调能力，但需要进一步发展快速理解人类指令、操作适应性和自然语言整合的能力。大型语言模型如 GPT-3.5 和 GPT-4 通过使智能体能够用自然语言进行沟通并解释人类指令来增强多智能体制造系统，从而使其更具适应性，并能够处理特定上下文的指令。一项案例研究展示了该框架的实际应用，显示智能体可以有效地沟通、理解任务和执行制造过程，包括智能体之间精确的 G 代码分配。研究结果凸显了将大型语言模型不断整合到多智能体制造系统中以及开发复杂智能体通信协议的重要性，以实现更灵活的制造系统。