Digital twins for electro-physical, chemical, and photonic processes
Manufacturing processes are becoming increasingly data-driven. Integrating manufacturing data and process models in real-time, a digital twin (DT) may function as an autonomous and dynamic digital replica. This, in turn, may enable manufacturers to not only understand and monitor a process but also...
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sg-ntu-dr.10356-1706002023-09-20T02:52:22Z Digital twins for electro-physical, chemical, and photonic processes Guo, Yuebin Klink, Andreas Bartolo, Paulo Guo, Grace Weihong School of Mechanical and Aerospace Engineering Singapore Centre for 3D Printing Engineering::Mechanical engineering Digital Twin Manufacturing Process Manufacturing processes are becoming increasingly data-driven. Integrating manufacturing data and process models in real-time, a digital twin (DT) may function as an autonomous and dynamic digital replica. This, in turn, may enable manufacturers to not only understand and monitor a process but also proactively control it in real-time or a product over its life cycle. This paper examines the DT concept and its evolution and presents a future DT framework. DTs’ key components (e.g., process models) and implementation are focused on additive manufacturing, electrical discharge machining, and electrochemical machining. Furthermore, current challenges and future research directions are summarized. The primary author € would like to thank the US National Science Foundation under CMMI-830922 and CMMI-832822 for contributing to the formulation of the keynote in the context of the digital twin. 2023-09-20T02:52:22Z 2023-09-20T02:52:22Z 2023 Journal Article Guo, Y., Klink, A., Bartolo, P. & Guo, G. W. (2023). Digital twins for electro-physical, chemical, and photonic processes. CIRP Annals - Manufacturing Technology, 72(2), 593-619. https://dx.doi.org/10.1016/j.cirp.2023.05.007 0007-8506 https://hdl.handle.net/10356/170600 10.1016/j.cirp.2023.05.007 2-s2.0-85160707233 2 72 593 619 en CIRP Annals - Manufacturing Technology © 2023 CIRP. Published by Elsevier Ltd. All rights reserved. |
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Engineering::Mechanical engineering Digital Twin Manufacturing Process Guo, Yuebin Klink, Andreas Bartolo, Paulo Guo, Grace Weihong Digital twins for electro-physical, chemical, and photonic processes |
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Manufacturing processes are becoming increasingly data-driven. Integrating manufacturing data and process models in real-time, a digital twin (DT) may function as an autonomous and dynamic digital replica. This, in turn, may enable manufacturers to not only understand and monitor a process but also proactively control it in real-time or a product over its life cycle. This paper examines the DT concept and its evolution and presents a future DT framework. DTs’ key components (e.g., process models) and implementation are focused on additive manufacturing, electrical discharge machining, and electrochemical machining. Furthermore, current challenges and future research directions are summarized. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Guo, Yuebin Klink, Andreas Bartolo, Paulo Guo, Grace Weihong |
format |
Article |
author |
Guo, Yuebin Klink, Andreas Bartolo, Paulo Guo, Grace Weihong |
author_sort |
Guo, Yuebin |
title |
Digital twins for electro-physical, chemical, and photonic processes |
title_short |
Digital twins for electro-physical, chemical, and photonic processes |
title_full |
Digital twins for electro-physical, chemical, and photonic processes |
title_fullStr |
Digital twins for electro-physical, chemical, and photonic processes |
title_full_unstemmed |
Digital twins for electro-physical, chemical, and photonic processes |
title_sort |
digital twins for electro-physical, chemical, and photonic processes |
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2023 |
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https://hdl.handle.net/10356/170600 |
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1779156781492076544 |