mlsanityData to 7 Oct 2026

Towards Agentic Defect Reasoning: A Graph-Assisted Retrieval Framework for Laser Powder Bed Fusion

arXiv 2604.04208 ↗First posted 5 Apr 2026cs.LGcited by 0

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Abstract (arXiv)

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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References · 5

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  2. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  3. 2024Graph Retrieval-Augmented Generation: A Surveycited by 320
  4. 2023Extracting Accurate Materials Data from Research Papers with Conversational Language Models and Prompt Engineeringcited by 86
  5. 2021Looking Through Glass: Knowledge Discovery from Materials Science Literature using Natural Language Processingcited by 16

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  3. 2026Mathematical Knowledge Graph-Driven Framework for Equation-Based Predictive and Reliable Additive Manufacturing
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  5. 2026In-situ process monitoring for defect detection in wire-arc additive manufacturing: an agentic AI approach
  6. 2026LLM-ADAM: A Generalizable LLM Agent Framework for Pre-Print Anomaly Detection in Additive Manufacturing
  7. 2026Task-Driven 3D Printability Assistance via Geometry- and Knowledge-Grounded LLM Reasoning
  8. 2025Agentic Additive Manufacturing Alloy Evaluation