IM-Chat: A Multi-agent LLM-based Framework for Knowledge Transfer in Injection Molding Industry

• URL: http://arxiv.org/abs/2507.15268v1
• Date: Mon, 21 Jul 2025 06:13:53 GMT
• Title: IM-Chat: A Multi-agent LLM-based Framework for Knowledge Transfer in Injection Molding Industry
• Authors: Junhyeong Lee, Joon-Young Kim, Heekyu Kim, Inhyo Lee, Seunghwa Ryu,
• Abstract summary: This study introduces IM-Chat, a multi-agent framework based on large language models (LLMs)<n> IM-Chat integrates both limited documented knowledge (e.g., troubleshooting tables, manuals) and extensive field data modeled through a data-driven process condition generator.<n>Performance was assessed across 100 single-tool and 60 hybrid tasks for GPT-4o, GPT-4o-mini, and GPT-3.5-turboizable.
• Score: 1.3369318110920576
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. 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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