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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

Details

Original languageEnglish
Title of host publication53rd CIRP Conference on Manufacturing Systems 2020
EditorsRobert X. Gao, Kornel Ehmann
PublisherElsevier
Pages240-245
Number of pages8
DOIs
Publication statusPublished - 22 Sep 2020
Publication typeA4 Article in a conference publication
EventCIRP Conference on Manufacturing Systems - Chicago, United States
Duration: 1 Jul 20203 Jul 2020
https://cirp-cms2020.northwestern.edu/

Publication series

NameProcedia CIRP
PublisherElsevier
Volume93
ISSN (Electronic)2212-8271

Conference

ConferenceCIRP Conference on Manufacturing Systems
Abbreviated titleCIRPCMS
CountryUnited States
CityChicago
Period1/07/203/07/20
Internet address

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.

Publication forum classification

Field of science, Statistics Finland