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MetalMind: A knowledge graph-driven human-centric knowledge system for metal additive manufacturing

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Publicado en:NPJ Advanced Manufacturing vol. 2, no. 1 (Dec 2025), p. 25
Autor principal: Fan, Haolin
Otros Autores: Fan, Zhen, Liu, Chenshu, Zhu, Jianhao, Gibbs, Tom, Fuh, Jerry Ying Hsi, Lu, Wen Feng, Li, Bingbing
Publicado:
Nature Publishing Group
Materias:
Construction
Accuracy
Technological change
Datasets
Collaboration
Large language models
Image retrieval
Retrieval
Knowledge management
Explicit knowledge
Natural language processing
Automation
Manufacturing
Real time
Performance evaluation
Knowledge representation
Additive manufacturing
Efficiency
Acceso en línea:Citation/Abstract
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Resumen:In the Industry 5.0 era, increasing manufacturing complexity and fragmented knowledge pose challenges for decision-making and workforce development. To tackle this, we present a human-centric knowledge system that integrates explicit knowledge from formal sources and implicit knowledge from expert insights. The system features three core innovations: (1) an automated KG construction pipeline leveraging large language models (LLMs) with collaborative verification to enhance knowledge extraction accuracy and minimize hallucinations; (2) a hybrid retrieval framework that combines vector-based, graph-based, and hybrid retrieval strategies for comprehensive knowledge access, achieving a 336.61% improvement over vector-based retrieval and a 68.04% improvement over graph-based retrieval in global understanding; and (3) an MR-enhanced interface that supports immersive, real-time interaction and continuous knowledge capture. Demonstrated through a metal additive manufacturing (AM) case study, this approach enriches domain expertise, improves knowledge representation and retrieval, and fosters enhanced human-machine collaboration, ultimately supporting adaptive upskilling in smart manufacturing.
ISSN:3004-8621
DOI:10.1038/s44334-025-00038-9
Fuente:Materials Science Database