用大模型让金属增材制造知识图谱可自然语言查询
Large Language Model Powered Decision Support for a Metal Additive Manufacturing Knowledge Graph
- 用大模型+少量示例实现自然语言转图谱查询
- 涵盖53种金属、9种工艺、4类原料的结构化知识
- 工程师无需懂语法就能获得设计建议，适合制造业决策
金属增材制造涉及工艺、材料、原料与后处理间的复杂关联，但知识分散于文献和静态数据库中，常需专家级查询，限制了在设计与规划中的应用。为此，我们构建了一个新型结构化知识图谱，涵盖7类材料、53种金属与合金、9种增材制造工艺、4类原料及其对应后处理要求。通过少量示例引导的大语言模型接口，用户可直接以自然语言提问，系统将查询归一化并转化为Cypher语句，在知识图谱上执行，返回结构化结果。该系统支持兼容性评估、约束筛选与面向增材制造的设计（DfAM）指导，是首个将领域特定金属增材制造知识图谱与大语言模型接口结合的交互式决策支持系统，实现了可访问且可解释的工程辅助，推动制造知识系统的以人为本设计。
原文摘要 · Abstract (English)
Metal additive manufacturing (AM) involves complex interdependencies among processes, materials, feedstock, and post-processing steps. However, the underlying relationships and domain knowledge remain fragmented across literature and static databases that often require expert-level queries, limiting their applicability in design and planning. To address these limitations, we develop a novel and structured knowledge graph (KG), representing 53 distinct metals and alloys across seven material categories, nine AM processes, four feedstock types, and corresponding post-processing requirements. A large language model (LLM) interface, guided by a few-shot prompting strategy, enables natural language querying without the need for formal query syntax. The system supports a range of tasks, including compatibility evaluation, constraint-based filtering, and design for AM (DfAM) guidance. User queries in natural language are normalized, translated into Cypher, and executed on the KG, with results returned in a structured format. This work introduces the first interactive system that connects a domain-specific metal AM KG with an LLM interface, delivering accessible and explainable decision support for engineers and promoting human-centered tools in manufacturing knowledge systems.
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