Knowledge Graphs in Digital Twins for AI in Production - Artificial Intelligence for Sustainable and Resilient Production Systems PART IV, IFIP WG 5.7 International Conference, APMS 2021 Access content directly
Conference Papers Year : 2021

Knowledge Graphs in Digital Twins for AI in Production

Pieter Lietaert
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Bart Meyers
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Johan Van Noten
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Joren Sips
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Klaas Gadeyne
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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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hal-04030403 , version 1 (16-03-2023)

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Pieter Lietaert, Bart Meyers, Johan Van Noten, Joren Sips, Klaas Gadeyne. Knowledge Graphs in Digital Twins for AI in Production. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2021, Nantes, France. pp.249-257, ⟨10.1007/978-3-030-85874-2_26⟩. ⟨hal-04030403⟩
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