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 |
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Title of host publication | Proceedings of the 27th International Conference on Systems Engineering |
Editors | Henry Selvaraj, Grzegorz Chmaj, Dawid Zydek |
Publisher | Springer |
Publication date | 2021 |
Pages | 445-454 |
ISBN (Print) | 9783030657956 |
DOIs | |
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 |
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Number | 27 |
Country/Territory | United States |
City | Las Vegas |
Period | 14/12/2020 → 16/12/2020 |
Series | Lecture Notes in Networks and Systems |
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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