Towards Data-Driven Digital Twins for Smart Manufacturing

Deena P. Francis; Sanja Lazarova-Molnar; Nader Mohamed

doi:10.1007/978-3-030-65796-3_43

Towards Data-Driven Digital Twins for Smart Manufacturing

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 27^th International Conference on Systems Engineering
Editors	Henry Selvaraj, Grzegorz Chmaj, Dawid Zydek
Publisher	Springer
Publication date	2021
Pages	445-454
ISBN (Print)	9783030657956
DOIs	https://doi.org/10.1007/978-3-030-65796-3_43
Publication status	Published - 2021
Event	27th International Conference on Systems Engineering - Las Vegas, United States Duration: 14 Dec 2020 → 16 Dec 2020 Conference number: 27

Conference

Conference	27th International Conference on Systems Engineering
Number	27
Country/Territory	United States
City	Las Vegas
Period	14/12/2020 → 16/12/2020

Series	Lecture Notes in Networks and Systems
Volume	182
ISSN	2367-3370

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Data-driven
Digital twin
Machine learning
Process mining
Reconfigurable manufacturing
Simulation
Smart factory

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-030-65796-3_43

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title = "Towards Data-Driven Digital Twins for Smart Manufacturing",

abstract = "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.",

keywords = "Data-driven, Digital twin, Machine learning, Process mining, Reconfigurable manufacturing, Simulation, Smart factory",

author = "Francis, {Deena P.} and Sanja Lazarova-Molnar and Nader Mohamed",

note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 27th International Conference on Systems Engineering, ICSEng 2020 ; Conference date: 14-12-2020 Through 16-12-2020",

year = "2021",

doi = "10.1007/978-3-030-65796-3_43",

language = "English",

isbn = "9783030657956",

series = "Lecture Notes in Networks and Systems",

publisher = "Springer",

pages = "445--454",

editor = "Henry Selvaraj and Grzegorz Chmaj and Dawid Zydek",

booktitle = "Proceedings of the 27th International Conference on Systems Engineering",

}

Francis, DP, Lazarova-Molnar, S & Mohamed, N 2021, Towards Data-Driven Digital Twins for Smart Manufacturing. in H Selvaraj, G Chmaj & D Zydek (eds), Proceedings of the 27^th International Conference on Systems Engineering. Springer, Lecture Notes in Networks and Systems, vol. 182, pp. 445-454, 27th International Conference on Systems Engineering, Las Vegas, United States, 14/12/2020. https://doi.org/10.1007/978-3-030-65796-3_43

Towards Data-Driven Digital Twins for Smart Manufacturing. / Francis, Deena P.; Lazarova-Molnar, Sanja; Mohamed, Nader.
Proceedings of the 27^th International Conference on Systems Engineering. ed. / Henry Selvaraj; Grzegorz Chmaj; Dawid Zydek. Springer, 2021. p. 445-454 (Lecture Notes in Networks and Systems, Vol. 182).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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T1 - Towards Data-Driven Digital Twins for Smart Manufacturing

AU - Francis, Deena P.

AU - Lazarova-Molnar, Sanja

AU - Mohamed, Nader

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AB - 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.

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KW - Digital twin

KW - Machine learning

KW - Process mining

KW - Reconfigurable manufacturing

KW - Simulation

KW - Smart factory

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SN - 9783030657956

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ER -

Towards Data-Driven Digital Twins for Smart Manufacturing

Abstract

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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Cite this