用多智能体LLM整合工具调用与生成模型，解决注塑行业知识传承难题。
IM-Chat: A Multi-agent LLM Framework Integrating Tool-Calling and Diffusion Modeling for Knowledge Transfer in Injection Molding Industry
- 构建多智能体框架，结合检索增强生成与工具调用，无需微调即可适应任务。
- 在100个单工具和60个混合任务中，性能优于微调的单智能体模型，尤其在定量推理上表现突出。
- 适合制造业知识转移、跨语言协作场景，对经验传承有实际价值。
注塑行业面临资深工人退休与多语言沟通障碍导致的知识流失问题。本文提出IM-Chat，一种基于大语言模型（LLMs）的多智能体框架，用于促进注塑工艺中的知识传递。该框架融合有限文档知识（如故障排查表、手册）与通过数据驱动过程条件生成器推断最优制造参数的现场数据，依据温度、湿度等环境输入实现上下文感知的任务求解。采用检索增强生成（RAG）策略与模块化架构中的工具调用智能体，确保系统可扩展性且无需微调。由领域专家基于10分制评分体系（聚焦相关性与正确性）评估了GPT-4o、GPT-4o-mini和GPT-3.5-turbo在100个单工具任务和60个混合任务上的表现，并辅以经领域适配指令提示的GPT-4o自动化评估。结果表明，更强大模型在复杂、多工具场景中准确性更高；相比微调的单智能体模型，IM-Chat在定量推理上更具优势，且能更好整合多源信息。研究验证了多智能体LLM系统在工业知识工作流中的可行性，确立了IM-Chat作为可扩展、通用的智能制造决策支持方案。
原文摘要 · Abstract (English)
The injection molding industry faces critical challenges in preserving and transferring field knowledge, particularly as experienced workers retire and multilingual barriers hinder effective communication. This study introduces IM-Chat, a multi-agent framework based on large language models (LLMs), designed to facilitate knowledge transfer in injection molding. IM-Chat integrates both limited documented knowledge (e.g., troubleshooting tables, manuals) and extensive field data modeled through a data-driven process condition generator that infers optimal manufacturing settings from environmental inputs such as temperature and humidity, enabling robust and context-aware task resolution. By adopting a retrieval-augmented generation (RAG) strategy and tool-calling agents within a modular architecture, IM-Chat ensures adaptability without the need for fine-tuning. Performance was assessed across 100 single-tool and 60 hybrid tasks for GPT-4o, GPT-4o-mini, and GPT-3.5-turbo by domain experts using a 10-point rubric focused on relevance and correctness, and was further supplemented by automated evaluation using GPT-4o guided by a domain-adapted instruction prompt. The evaluation results indicate that more capable models tend to achieve higher accuracy, particularly in complex, tool-integrated scenarios. In addition, compared with the fine-tuned single-agent LLM, IM-Chat demonstrated superior accuracy, particularly in quantitative reasoning, and greater scalability in handling multiple information sources. Overall, these findings demonstrate the viability of multi-agent LLM systems for industrial knowledge workflows and establish IM-Chat as a scalable and generalizable approach to AI-assisted decision support in manufacturing.
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