MetalMind: A knowledge graph-driven human-centric knowledge system for metal additive manufacturing
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| 發表在: | NPJ Advanced Manufacturing vol. 2, no. 1 (Dec 2025), p. 25 |
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| 主要作者: | |
| 其他作者: | , , , , , , |
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Nature Publishing Group
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| 在線閱讀: | Citation/Abstract Full Text Full Text - PDF |
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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. |
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| ISSN: | 3004-8621 |
| DOI: | 10.1038/s44334-025-00038-9 |
| Fuente: | Materials Science Database |