🤖 AI Summary
This study addresses the challenges of defect diagnosis, limited interpretability, and insufficient mitigation guidance in laser powder bed fusion (LPBF) manufacturing by proposing a decision support system that integrates a structured defect ontology with a large language model (LLM). The system enables natural language querying, provides explanations of defect root causes, and recommends mitigation strategies, while incorporating a vision–language model to semantically interpret microstructural defects. Innovatively, it employs an ontology-driven knowledge representation framework coupled with a semantic alignment scoring mechanism to facilitate joint visual–textual reasoning and causal modeling. Evaluated on a literature-derived dataset, the approach achieves a macro-averaged F1 score of 0.808, and Cohen’s kappa indicates substantial agreement between model outputs and expert annotations, significantly outperforming baseline methods.
📝 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. 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. 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. Furthermore, a multimodal image-assessment module based on foundation models enables descriptor-guided interpretation of representative microscopic defect images through semantic alignment scoring. The proposed framework was evaluated through qualitative comparisons with general-purpose vision-language models, an ablation study, and an inter-rater reliability analysis. 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. Additionally, inter-rater reliability analysis using Cohen's kappa indicated substantial agreement between the model outputs and the literature-derived reference labels. These findings suggest that ontology-guided knowledge representation can improve the consistency, interpretability, and practical usefulness of LLM-assisted LPBF defect analysis.