用大模型+检索增强，帮工厂快速选对自动化设备。
Designing an LLM-Based Copilot for Manufacturing Equipment Selection
- 结合大模型与结构化知识检索，构建可追踪的决策流程
- 22个测试案例中19个正确匹配需求，6个完全满足所有条件
- 适合自动化工程师快速应对新品导入时的设备选型难题
自动化设备选型的有效决策对缩短投产周期、保障生产质量至关重要，尤其在产品多样化和市场需求加剧的背景下。然而，专业人才不足与资源限制常导致新产线投产阶段效率低下。现有方法缺乏系统化、定制化的支持，难以有效缩短投产时间，往往以牺牲质量为代价。本研究探讨大型语言模型（LLM）结合检索增强生成（RAG）是否能辅助优化投产规划中的设备选型。我们提出一个基于事实驱动的协作助手，整合LLM与结构化/半结构化知识检索，针对机器人、供料器和视觉系统三类核心组件，提供引导式、可追溯的状态机决策流程。该系统已向工业合作伙伴演示，并在其内部三个场景中验证。反馈表明，系统能提供逻辑清晰且可操作的设备建议。具体而言，在22个设备选择请求中，19个能正确考虑多数需求；其中6个案例完整满足所有要求。
原文摘要 · Abstract (English)
Effective decision-making in automation equipment selection is critical for reducing ramp-up time and maintaining production quality, especially in the face of increasing product variation and market demands. However, limited expertise and resource constraints often result in inefficiencies during the ramp-up phase when new products are integrated into production lines. Existing methods often lack structured and tailored solutions to support automation engineers in reducing ramp-up time, leading to compromises in quality. This research investigates whether large-language models (LLMs), combined with Retrieval-Augmented Generation (RAG), can assist in streamlining equipment selection in ramp-up planning. We propose a factual-driven copilot integrating LLMs with structured and semi-structured knowledge retrieval for three component types (robots, feeders and vision systems), providing a guided and traceable state-machine process for decision-making in automation equipment selection. The system was demonstrated to an industrial partner, who tested it on three internal use-cases. Their feedback affirmed its capability to provide logical and actionable recommendations for automation equipment. More specifically, among 22 equipment prompts analyzed, 19 involved selecting the correct equipment while considering most requirements, and in 6 cases, all requirements were fully met.
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