用大模型+记忆机制自动合并3D打印任务，提升生产效率。
A Memory-Augmented LLM-Driven Method for Autonomous Merging of 3D Printing Work Orders
- 大模型读取任务与设备特征，生成自然语言提示进行匹配。
- 引入自记忆学习，准确率与精度显著提升。
- 适合智能制造中需高效调度的个性化生产场景。
随着3D打印技术快速发展，生产线对个性化定制生产的需求持续上升。高效合并打印任务可显著提升生产效率。本文提出一种基于大语言模型（LLM）的自主任务合并方法，结合记忆增强学习策略。在工业场景中，将设备与订单特征建模为LLM可读的自然语言提示模板，并开发了订单-设备匹配工具与合并干扰检测模块。通过引入自记忆学习策略，构建了智能代理实现自主任务合并，提升了任务分配的准确率与精确度。该方法有效发挥大模型在工业应用中的优势，同时降低了幻觉风险。
原文摘要 · Abstract (English)
With the rapid development of 3D printing, the demand for personalized and customized production on the manufacturing line is steadily increasing. Efficient merging of printing workpieces can significantly enhance the processing efficiency of the production line. Addressing the challenge, a Large Language Model (LLM)-driven method is established in this paper for the autonomous merging of 3D printing work orders, integrated with a memory-augmented learning strategy. In industrial scenarios, both device and order features are modeled into LLM-readable natural language prompt templates, and develop an order-device matching tool along with a merging interference checking module. By incorporating a self-memory learning strategy, an intelligent agent for autonomous order merging is constructed, resulting in improved accuracy and precision in order allocation. The proposed method effectively leverages the strengths of LLMs in industrial applications while reducing hallucination.
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