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

Wedi'i Gadw mewn:
Manylion Llyfryddiaeth
Cyhoeddwyd yn:NPJ Advanced Manufacturing vol. 2, no. 1 (Dec 2025), p. 25
Prif Awdur: Fan, Haolin
Awduron Eraill: Fan, Zhen, Liu, Chenshu, Zhu, Jianhao, Gibbs, Tom, Fuh, Jerry Ying Hsi, Lu, Wen Feng, Li, Bingbing
Cyhoeddwyd:
Nature Publishing Group
Pynciau:
Mynediad Ar-lein:Citation/Abstract
Full Text
Full Text - PDF
Tagiau: Ychwanegu Tag
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MARC

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022 |a 3004-8621 
024 7 |a 10.1038/s44334-025-00038-9  |2 doi 
035 |a 3225848779 
045 2 |b d20251201  |b d20251231 
100 1 |a Fan, Haolin  |u California State University Northridge, Autonomy Research Center for STEAHM (ARCS), Northridge, USA (GRID:grid.253563.4) (ISNI:0000 0001 0657 9381); National University of Singapore, Department of Mechanical Engineering, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431) 
245 1 |a MetalMind: A knowledge graph-driven human-centric knowledge system for metal additive manufacturing 
260 |b Nature Publishing Group  |c Dec 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Construction 
653 |a Accuracy 
653 |a Technological change 
653 |a Datasets 
653 |a Collaboration 
653 |a Large language models 
653 |a Image retrieval 
653 |a Retrieval 
653 |a Knowledge management 
653 |a Explicit knowledge 
653 |a Natural language processing 
653 |a Automation 
653 |a Manufacturing 
653 |a Real time 
653 |a Performance evaluation 
653 |a Knowledge representation 
653 |a Additive manufacturing 
653 |a Efficiency 
700 1 |a Fan, Zhen  |u National University of Singapore, Department of Mechanical Engineering, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431) 
700 1 |a Liu, Chenshu  |u California State University Northridge, Autonomy Research Center for STEAHM (ARCS), Northridge, USA (GRID:grid.253563.4) (ISNI:0000 0001 0657 9381) 
700 1 |a Zhu, Jianhao  |u California State University Northridge, Autonomy Research Center for STEAHM (ARCS), Northridge, USA (GRID:grid.253563.4) (ISNI:0000 0001 0657 9381) 
700 1 |a Gibbs, Tom  |u Nvidia Inc, Santa Clara, USA (GRID:grid.451133.1) (ISNI:0000 0004 0458 4453) 
700 1 |a Fuh, Jerry Ying Hsi  |u National University of Singapore, Department of Mechanical Engineering, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431) 
700 1 |a Lu, Wen Feng  |u National University of Singapore, Department of Mechanical Engineering, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431) 
700 1 |a Li, Bingbing  |u California State University Northridge, Autonomy Research Center for STEAHM (ARCS), Northridge, USA (GRID:grid.253563.4) (ISNI:0000 0001 0657 9381); University of California Los Angeles, Department of Mechanical and Aerospace Engineering, Los Angeles, USA (GRID:grid.19006.3e) (ISNI:0000 0001 2167 8097) 
773 0 |t NPJ Advanced Manufacturing  |g vol. 2, no. 1 (Dec 2025), p. 25 
786 0 |d ProQuest  |t Materials Science Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3225848779/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text  |u https://www.proquest.com/docview/3225848779/fulltext/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3225848779/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch