A proposed methodology to develop digital twin framework for plasma processing

Plasma processing presents complex behavioural characteristics that are created by different generation and processing methods. In this context, the digital twin (DT) method offers a visualisation and simulation techniques that enable better understanding of plasma complexities, control, and precisi...

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Main Authors: Alasdair Mitchell, Xinyang Wei, Rongyan Sun, Kazuya Yamamura, Long Ye, Jonathan Corney, Nan Yu
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Results in Engineering
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Online Access:http://www.sciencedirect.com/science/article/pii/S2590123024017146
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author Alasdair Mitchell
Xinyang Wei
Rongyan Sun
Kazuya Yamamura
Long Ye
Jonathan Corney
Nan Yu
author_facet Alasdair Mitchell
Xinyang Wei
Rongyan Sun
Kazuya Yamamura
Long Ye
Jonathan Corney
Nan Yu
author_sort Alasdair Mitchell
collection DOAJ
description Plasma processing presents complex behavioural characteristics that are created by different generation and processing methods. In this context, the digital twin (DT) method offers a visualisation and simulation techniques that enable better understanding of plasma complexities, control, and precision. With the rise of Industry 4.0, DT applications have gained increased attention across engineering sectors. However, despite growing interest in this digital technique, DT characteristics and basic principles vary within the literature, leading to confusion about the architectures and capabilities of this method. This study proposes a comprehensive DT framework tailored for plasma processing aimed at bridging the gap in current literature. Two frameworks are introduced. The first introduces a modular approach at a sub-component-level, incorporating the elements required for successful DT integration in practice. The second focuses on a DT method that offer data acquisition and machine learning integration for parameter optimisation at the plasma process-level, presenting a foundation for future real-time developments. These frameworks are built upon successful DT practices in advanced manufacturing technologies and offer a flexible premise adaptable to various plasma processing techniques. However, integration of enabling technology remains the major limitation, as case studies remain conceptual, therefore requiring future physical implementation, specifically for a goal-oriented DT.
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series Results in Engineering
spelling doaj-art-bcb2f75af218458392756ce2f5cdd1932025-08-20T02:34:40ZengElsevierResults in Engineering2590-12302024-12-012410346210.1016/j.rineng.2024.103462A proposed methodology to develop digital twin framework for plasma processingAlasdair Mitchell0Xinyang Wei1Rongyan Sun2Kazuya Yamamura3Long Ye4Jonathan Corney5Nan Yu6Institute of Materials and Processes, The University of Edinburgh, United KingdomResearch Center for Precision Engineering, Graduate School of Engineering, Osaka University, JapanResearch Center for Precision Engineering, Graduate School of Engineering, Osaka University, JapanResearch Center for Precision Engineering, Graduate School of Engineering, Osaka University, JapanInstitute of Materials and Processes, The University of Edinburgh, United Kingdom; Corresponding authors.Institute of Materials and Processes, The University of Edinburgh, United KingdomInstitute of Materials and Processes, The University of Edinburgh, United Kingdom; Corresponding authors.Plasma processing presents complex behavioural characteristics that are created by different generation and processing methods. In this context, the digital twin (DT) method offers a visualisation and simulation techniques that enable better understanding of plasma complexities, control, and precision. With the rise of Industry 4.0, DT applications have gained increased attention across engineering sectors. However, despite growing interest in this digital technique, DT characteristics and basic principles vary within the literature, leading to confusion about the architectures and capabilities of this method. This study proposes a comprehensive DT framework tailored for plasma processing aimed at bridging the gap in current literature. Two frameworks are introduced. The first introduces a modular approach at a sub-component-level, incorporating the elements required for successful DT integration in practice. The second focuses on a DT method that offer data acquisition and machine learning integration for parameter optimisation at the plasma process-level, presenting a foundation for future real-time developments. These frameworks are built upon successful DT practices in advanced manufacturing technologies and offer a flexible premise adaptable to various plasma processing techniques. However, integration of enabling technology remains the major limitation, as case studies remain conceptual, therefore requiring future physical implementation, specifically for a goal-oriented DT.http://www.sciencedirect.com/science/article/pii/S2590123024017146Plasma processingDigital twinDigital manufacturingAdditive manufacturingSubtractive manufacturing
spellingShingle Alasdair Mitchell
Xinyang Wei
Rongyan Sun
Kazuya Yamamura
Long Ye
Jonathan Corney
Nan Yu
A proposed methodology to develop digital twin framework for plasma processing
Results in Engineering
Plasma processing
Digital twin
Digital manufacturing
Additive manufacturing
Subtractive manufacturing
title A proposed methodology to develop digital twin framework for plasma processing
title_full A proposed methodology to develop digital twin framework for plasma processing
title_fullStr A proposed methodology to develop digital twin framework for plasma processing
title_full_unstemmed A proposed methodology to develop digital twin framework for plasma processing
title_short A proposed methodology to develop digital twin framework for plasma processing
title_sort proposed methodology to develop digital twin framework for plasma processing
topic Plasma processing
Digital twin
Digital manufacturing
Additive manufacturing
Subtractive manufacturing
url http://www.sciencedirect.com/science/article/pii/S2590123024017146
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