A Knowledge-Driven LLM-Based Decision-Support System for Explainable Defect Analysis and Mitigation Guidance in Laser Powder Bed Fusion
A Knowledge-Driven LLM-Based Decision-Support System for Explainable Defect Analysis and Mitigation Guidance in Laser Powder Bed Fusion
基于知识驱动的大语言模型决策支持系统：激光粉末床熔融工艺中的可解释缺陷分析与缓解指导
Abstract: This work presents a knowledge-driven decision-support system that integrates structured defect knowledge with LLM-based reasoning to provide explainable defect diagnosis and mitigation guidance in manufacturing, using LPBF as a representative, safety-critical case study. 摘要： 本研究提出了一种知识驱动的决策支持系统，该系统将结构化缺陷知识与基于大语言模型（LLM）的推理相结合，旨在制造业中提供可解释的缺陷诊断和缓解指导，并以激光粉末床熔融（LPBF）这一具有代表性的安全关键型工艺作为案例研究。
The proposed ontology-integrated LLM-based decision support system for LPBF defect analysis and mitigation guidance is built on a knowledge base containing 27 known LPBF defect types organized into hierarchical categories and causal relationships. 该系统将本体论集成至基于大语言模型的决策支持框架中，用于LPBF缺陷分析与缓解指导。其构建基础是一个包含27种已知LPBF缺陷类型的知识库，这些缺陷按层级类别和因果关系进行了组织。
The developed system supports fuzzy natural language queries for systematic knowledge retrieval, literature-supported explanation of defects, and guidance on defect causes and mitigation strategies derived from encoded process knowledge. 该系统支持模糊自然语言查询，以实现系统化的知识检索；提供基于文献支持的缺陷解释；并根据编码的工艺知识，提供关于缺陷成因及缓解策略的指导。
Furthermore, a multimodal image-assessment module based on foundation models enables descriptor-guided interpretation of representative microscopic defect images through semantic alignment scoring. 此外，一个基于基础模型的多模态图像评估模块，通过语义对齐评分，实现了对代表性微观缺陷图像的描述符引导式解读。
The proposed framework was evaluated through qualitative comparisons with general-purpose vision-language models, an ablation study, and an inter-rater reliability analysis. 该框架通过与通用视觉-语言模型的定性比较、消融研究以及评分者间信度分析进行了评估。
Evaluation on the literature-derived dataset showed that the fully integrated configuration outperformed the other three evaluated system configurations, achieving a macro-average F1 score of 0.808. 在基于文献数据集上的评估显示，完全集成的配置优于其他三种评估系统配置，宏平均F1分数达到了0.808。
Additionally, inter-rater reliability analysis using Cohen’s kappa indicated substantial agreement between the model outputs and the literature-derived reference labels. 此外，使用Cohen’s kappa进行的评分者间信度分析表明，模型输出与文献参考标签之间具有高度的一致性。
These findings suggest that ontology-guided knowledge representation can improve the consistency, interpretability, and practical usefulness of LLM-assisted LPBF defect analysis. 这些研究结果表明，本体引导的知识表示能够提高大语言模型辅助LPBF缺陷分析的一致性、可解释性和实际应用价值。