用知识图谱帮激光3D打印找缺陷根源，让工艺优化有据可依。
Towards Agentic Defect Reasoning: A Graph-Assisted Retrieval Framework for Laser Powder Bed Fusion
- 把论文变成带关系的知识图谱，连接参数、机制与缺陷
- 检索准确率和召回率均达0.9667，能精准定位相关证据
- 适合材料工艺研发人员，提升增材制造的可解释性
激光粉末床熔融（LPBF）对工艺参数高度敏感，缺陷形成受复杂热力与流体机制影响。然而缺陷相关知识分散于文献中，难以系统理解。本研究提出一种面向缺陷推理的图辅助检索框架，以Ti6Al4V为案例。将科学文献转化为结构化表示，将参数、机制与缺陷间的关系编码为证据关联知识图谱。框架融合语义与图检索，并通过轻量级代理推理层构建可解释的缺陷路径。评估显示检索准确率（0.9667）与召回率（0.9667）均很高，有效识别相关缺陷证据。该框架可生成从工艺参数到缺陷的透明推理链，为增材制造提供可查询、可解释的知识资源。
原文摘要 · Abstract (English)
Laser Powder Bed Fusion (LPBF) is highly sensitive to process parameters, which influence defect formation through complex thermal and fluid mechanisms. However, defect-related knowledge is dispersed across the literature, limiting systematic understanding. This study presents a graph-assisted retrieval framework for defect reasoning in LPBF, using Ti6Al4V as a case study. Scientific publications are transformed into a structured representation, and relationships between parameters, mechanisms, and defects are encoded into an evidence-linked knowledge graph. The framework integrates semantic and graph-based retrieval, supported by a lightweight agent-based reasoning layer to construct interpretable defect pathways. Evaluation shows high retrieval accuracy (0.9667) and recall (0.9667), demonstrating effective identification of relevant defect related evidence. The framework enables transparent reasoning chains linking process parameters to defects. This work provides a scalable approach for converting unstructured literature into a query able and interpretable knowledge resource for additive manufacturing.
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