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Digital Twin: Multi-dimensional Model Reduction Method for Performance Optimization of the Virtual Entity

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

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Digital Twin : Multi-dimensional Model Reduction Method for Performance Optimization of the Virtual Entity. / Chakraborti, Ananda; Heininen, Arttu; Koskinen, Kari T.; Lämsä, Ville.

53rd CIRP Conference on Manufacturing Systems 2020. ed. / Robert X. Gao; Kornel Ehmann. Elsevier, 2020. p. 240-245 (Procedia CIRP; Vol. 93).

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Harvard

Chakraborti, A, Heininen, A, Koskinen, KT & Lämsä, V 2020, Digital Twin: Multi-dimensional Model Reduction Method for Performance Optimization of the Virtual Entity. in RX Gao & K Ehmann (eds), 53rd CIRP Conference on Manufacturing Systems 2020. Procedia CIRP, vol. 93, Elsevier, pp. 240-245, CIRP Conference on Manufacturing Systems, Chicago, United States, 1/07/20. https://doi.org/10.1016/j.procir.2020.04.050

APA

Chakraborti, A., Heininen, A., Koskinen, K. T., & Lämsä, V. (2020). Digital Twin: Multi-dimensional Model Reduction Method for Performance Optimization of the Virtual Entity. In R. X. Gao, & K. Ehmann (Eds.), 53rd CIRP Conference on Manufacturing Systems 2020 (pp. 240-245). (Procedia CIRP; Vol. 93). Elsevier. https://doi.org/10.1016/j.procir.2020.04.050

Vancouver

Chakraborti A, Heininen A, Koskinen KT, Lämsä V. Digital Twin: Multi-dimensional Model Reduction Method for Performance Optimization of the Virtual Entity. In Gao RX, Ehmann K, editors, 53rd CIRP Conference on Manufacturing Systems 2020. Elsevier. 2020. p. 240-245. (Procedia CIRP). https://doi.org/10.1016/j.procir.2020.04.050

Author

Chakraborti, Ananda ; Heininen, Arttu ; Koskinen, Kari T. ; Lämsä, Ville. / Digital Twin : Multi-dimensional Model Reduction Method for Performance Optimization of the Virtual Entity. 53rd CIRP Conference on Manufacturing Systems 2020. editor / Robert X. Gao ; Kornel Ehmann. Elsevier, 2020. pp. 240-245 (Procedia CIRP).

Bibtex - Download

@inproceedings{b26ef3d768244e2d9d1559f30102ad0a,
title = "Digital Twin: Multi-dimensional Model Reduction Method for Performance Optimization of the Virtual Entity",
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.",
author = "Ananda Chakraborti and Arttu Heininen and Koskinen, {Kari T.} and Ville L{\"a}ms{\"a}",
note = "JUFOID=76433",
year = "2020",
month = "9",
day = "22",
doi = "10.1016/j.procir.2020.04.050",
language = "English",
series = "Procedia CIRP",
publisher = "Elsevier",
pages = "240--245",
editor = "Gao, {Robert X.} and Ehmann, {Kornel }",
booktitle = "53rd CIRP Conference on Manufacturing Systems 2020",
address = "Netherlands",

}

RIS (suitable for import to EndNote) - Download

TY - GEN

T1 - Digital Twin

T2 - Multi-dimensional Model Reduction Method for Performance Optimization of the Virtual Entity

AU - Chakraborti, Ananda

AU - Heininen, Arttu

AU - Koskinen, Kari T.

AU - Lämsä, Ville

N1 - JUFOID=76433

PY - 2020/9/22

Y1 - 2020/9/22

N2 - 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.

AB - 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.

U2 - 10.1016/j.procir.2020.04.050

DO - 10.1016/j.procir.2020.04.050

M3 - Conference contribution

T3 - Procedia CIRP

SP - 240

EP - 245

BT - 53rd CIRP Conference on Manufacturing Systems 2020

A2 - Gao, Robert X.

A2 - Ehmann, Kornel

PB - Elsevier

ER -