Digital twin: Multi-dimensional model reduction method for performance optimization of the virtual entity

Ananda Chakraborti (Corresponding Author), Arttu Heininen, Kari T. Koskinen, Ville Lämsä

    Research output: Contribution to journalArticle in a proceedings journalScientificpeer-review

    4 Citations (Scopus)

    Abstract

    Digital Twin (DT) is an emerging technology that allows manufacturers to simulate and predict states of complex machine systems during operation. This requires that the physical machine state is integrated in a virtual entity, instantaneously. However, if the virtual entity uses computationally demanding models like physics-based finite element models or data driven prediction models, the virtual entity may become asynchronous with its physical entity. This creates an increasing lag between the twins, reducing the effectiveness of the virtual entity. Therefore, in this article, a model reduction method is described for a graph-based representation of multi-dimensional DT model based on spectral clustering and graph centrality metric. This method identifies and optimizes high-importance variables from computationally demanding models to minimize the total number of variables required for improving the performance of the DT.

    Original languageEnglish
    Pages (from-to)240-245
    JournalProcedia CIRP
    Volume93
    DOIs
    Publication statusPublished - 2020
    MoE publication typeA4 Article in a conference publication
    Event53rd CIRP Conference on Manufacturing Systems, CMS 2020 - Chicago, United States
    Duration: 1 Jul 20203 Jul 2020

    Keywords

    • Digital twins
    • Model fusion
    • Model reduction
    • Multi-physics simulation
    • Spectral clustering

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