[2605.01100] A Knowledge-Driven LLM-Based Decision-Support System for Explainable Defect Analysis and Mitigation Guidance in Laser Powder Bed Fusion
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Computer Science > Artificial Intelligence
arXiv:2605.01100 (cs)
[Submitted on 1 May 2026]
Title:A Knowledge-Driven LLM-Based Decision-Support System for Explainable Defect Analysis and Mitigation Guidance in Laser Powder Bed Fusion
Authors:Basit Mahmud Shahriar, Md Habibor Rahman
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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.
Comments: 28 pages, 15 figures
Subjects:
Artificial Intelligence (cs.AI)
Cite as: arXiv:2605.01100 [cs.AI]
(or arXiv:2605.01100v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.01100
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arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Md Habibor Rahman [view email] [v1] Fri, 1 May 2026 21:12:51 UTC (5,043 KB)
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