Abstract
AI is increasingly penetrating the production industry. Today, however, AI is still used in a limited way in a production environment, often focusing on a single production step and using out-of-the-box AI algorithms. AI models that use information spanning a complete production line and even larger parts of the product lifecycle could add significant value for production companies. In this paper, we suggest a digital twin architecture to support the complete AI lifecycle (discovering correlations, learning, deploying and validating), based on a knowledge graph that centralizes all information. We show how this digital twin could ease information access to different heterogenous data sources and pose opportunities for a wider application of AI in production industry. We illustrate this approach using a simplified industrial example of a compressor housing production, leading to preliminary results that show how a data scientist can efficiently access, through the knowledge graph, all necessary data for the creation of an AI model.
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Acknowledgments
This research was supported by Flanders Make, the strategic research center for the manufacturing industry. This paper was partially funded by the DTDesign ICON (Flanders Innovation & Entrepreneurship FM/ICON :: HBC.2019.0079) project. We would also like to acknowledge the European Commission for funding through the ASSISTANT project, grant number 101000165.
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Lietaert, P., Meyers, B., Van Noten, J., Sips, J., Gadeyne, K. (2021). Knowledge Graphs in Digital Twins for AI in Production. In: Dolgui, A., Bernard, A., Lemoine, D., von Cieminski, G., Romero, D. (eds) Advances in Production Management Systems. Artificial Intelligence for Sustainable and Resilient Production Systems. APMS 2021. IFIP Advances in Information and Communication Technology, vol 630. Springer, Cham. https://doi.org/10.1007/978-3-030-85874-2_26
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DOI: https://doi.org/10.1007/978-3-030-85874-2_26
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