Abstract
Digital twins are revolutionizing smart manufacturing by facilitating real-time monitoring, simulation, and optimization of physical processes. This paper introduces the SINDIT framework, a comprehensive approach tailored for developing knowledge graph-based digital twins. By seamlessly integrating cognitive capabilities, SINDIT enhances decision-making and operational efficiency within manufacturing systems. Central to its architecture is a robust data pipeline, adept at organizing and linking vast amounts of heterogeneous data, thereby enabling advanced data analytics and reasoning.
Case studies from the pilots of the COGNIMAN project underscore the practical utility and benefits of the SINDIT framework. These studies showcase notable enhancements in predictive maintenance, process optimization, and overall productivity. By harnessing the power of knowledge graphs and cognitive capabilities, SINDIT represents a promising avenue for driving innovation and efficiency in smart manufacturing. Through this framework, manufacturers can achieve a higher level of operational insight and agility, leading to improved performance and competitiveness in the market.
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Acknowledgments
This work has been co-funded by the European Commission Project COGNIMAN (grant agreement No. 101058477) and the SINTEF SEP Project SINDIT 2.0. The work on the Fischertechnik factory was primarily conducted by Timo Peter during his internship at SINTEF, under the supervision of Maryna Waszak.
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Lam, A.N. et al. (2025). SINDIT: A Framework for Knowledge Graph-Based Digital Twins in Smart Manufacturing. In: Rey, G., Tigli, JY., Franquet, E. (eds) Internet of Things. 7th IFIPIoT 2024 International IFIP WG 5.5 Workshops. IFIPIoT 2024. IFIP Advances in Information and Communication Technology, vol 738. Springer, Cham. https://doi.org/10.1007/978-3-031-82065-6_4
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