Towards Data-Driven Digital Twins for Smart Manufacturing

Deena P. Francis*, Sanja Lazarova-Molnar, Nader Mohamed

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Abstrakt

The adoption of a digital twin for a smart factory offers several advantages, such as improved production and reduced costs, and energy consumption. Due to the growing demands of the market, factories have adopted the reconfigurable manufacturing paradigm, wherein the structure of the factory is constantly changing. This situation presents a unique challenge to traditional modeling and simulation approaches. To deal with this scenario, we propose a generic data-driven framework for automated construction of digital twins for smart factories. The novel aspects of our proposed framework include a pure data-driven approach incorporating machine learning and process mining techniques, and continuous model improvement and validation.

OriginalsprogEngelsk
TitelProceedings of the 27th International Conference on Systems Engineering, ICSEng 2020
RedaktørerHenry Selvaraj, Grzegorz Chmaj, Dawid Zydek
Antal sider10
ForlagSpringer
Publikationsdato2021
Sider445-454
ISBN (Trykt)9783030657956
DOI
StatusUdgivet - 2021
Begivenhed27th International Conference on Systems Engineering, ICSEng 2020 - Las Vegas, USA
Varighed: 14. dec. 202016. dec. 2020

Konference

Konference27th International Conference on Systems Engineering, ICSEng 2020
Land/OmrådeUSA
ByLas Vegas
Periode14/12/202016/12/2020
NavnLecture Notes in Networks and Systems
Vol/bind182
ISSN2367-3370

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