Li, BingbingFan, HaolinFan, ZhenErkoyuncu, John AhmetZhang, Hong-ChaoHuang, Haihong2025-07-152025-07-152025Li B, Fan H, Fan Z, et al., (2025) Sim2Know: new paradigm of digital twins to design and inform human-centric knowledge system. CIRP Annals, Volume 74, Issue 1, 2025, pp. 215-2190007-8506https://doi.org/10.1016/j.cirp.2025.04.028https://dspace.lib.cranfield.ac.uk/handle/1826/24049The novel framework, Sim2Know, tackles two major challenges in adaptively designing and informing a human-centric knowledge system: the lack of labeled real-world training data and the difficulty of capturing implicit knowledge. First, a digital twin demonstrator is developed to generate high-quality synthetic training data. Next, we propose a hybrid training approach that combines transfer learning from pre-trained self-supervised models with synthetic data augmentation, achieving a precision rate of 90.31 % in identifying 11 essential human action patterns in metal additive manufacturing. Finally, the human-centric knowledge system is designed to capture implicit knowledge through contextualizing human machine interaction beyond explicit domain knowledge.215-219enAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/4014 Manufacturing Engineering40 Engineering3 Good Health and Well BeingIndustrial Engineering & Automation4017 Mechanical engineeringDigital twinArtificial intelligenceHuman-centric knowledgeArticle1726-0604673118741