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Towards scalable and reusable predictive models for cyber twins in manufacturing systems
Journal of Intelligent Manufacturing, Volume: 33, Issue: 2, Pages: 441 - 455
Swansea University Author:
Cinzia Giannetti
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DOI (Published version): 10.1007/s10845-021-01804-0
Abstract
Smart factories are intelligent, fully-connected and flexible systems that can continuously monitor and analyse data streams from interconnected systems to make decisions and dynamically adapt to new circumstances. The implementation of smart factories represents a leap forward compared to tradition...
Published in: | Journal of Intelligent Manufacturing |
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ISSN: | 0956-5515 1572-8145 |
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Springer Science and Business Media LLC
2022
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URI: | https://cronfa.swan.ac.uk/Record/cronfa57612 |
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2022-06-23T14:11:39.4506826 v2 57612 2021-08-13 Towards scalable and reusable predictive models for cyber twins in manufacturing systems a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 2021-08-13 ACEM Smart factories are intelligent, fully-connected and flexible systems that can continuously monitor and analyse data streams from interconnected systems to make decisions and dynamically adapt to new circumstances. The implementation of smart factories represents a leap forward compared to traditional automation. It is underpinned by the deployment of cyberphysical systems that, through the application of Artificial Intelligence, integrate predictive capabilities and foster rapid decision-making. Deep Learning (DL) is a key enabler for the development of smart factories. However, the implementation of DL in smart factories is hindered by its reliance on large amounts of data and extreme computational demand. To address this challenge, Transfer Learning (TL) has been proposed to promote the efficient training of models by enabling the reuse of previously trained models. In this paper, by means of a specific example in aluminium can manufacturing, an empirical study is presented, which demonstrates the potential of TL to achieve fast deployment of scalable and reusable predictive models for Cyber Manufacturing Systems. Through extensive experiments, the value of TL is demonstrated to achieve better generalisation and model performance, especially with limited datasets. This research provides a pragmatic approach towards predictive model building for cyber twins, paving the way towards the realisation of smart factories. Journal Article Journal of Intelligent Manufacturing 33 2 441 455 Springer Science and Business Media LLC 0956-5515 1572-8145 Cyber physical systems; Transfer learning; ConvLSTM; Smart manufacturing; Deep learning 1 2 2022 2022-02-01 10.1007/s10845-021-01804-0 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University SU Library paid the OA fee (TA Institutional Deal) This work was supported by the UK Engineering and Physical Sciences Research Council (EPSRC) project EP/S001387/1 and we acknowledge the support of the IMPACT and Supercomputing Wales projects, which are part-funded by the European Regional Development Fund (ERDF) via Welsh Government. 2022-06-23T14:11:39.4506826 2021-08-13T10:14:15.5035807 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Cinzia Giannetti 0000-0003-0339-5872 1 Aniekan Essien 2 57612__20617__6fedc83e11ee4961b2de25568653f29c.pdf 57612.pdf 2021-08-13T10:15:59.2859911 Output 2058503 application/pdf Version of Record true © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License true eng http://creativecommons.org/licenses/by/4.0/ |
title |
Towards scalable and reusable predictive models for cyber twins in manufacturing systems |
spellingShingle |
Towards scalable and reusable predictive models for cyber twins in manufacturing systems Cinzia Giannetti |
title_short |
Towards scalable and reusable predictive models for cyber twins in manufacturing systems |
title_full |
Towards scalable and reusable predictive models for cyber twins in manufacturing systems |
title_fullStr |
Towards scalable and reusable predictive models for cyber twins in manufacturing systems |
title_full_unstemmed |
Towards scalable and reusable predictive models for cyber twins in manufacturing systems |
title_sort |
Towards scalable and reusable predictive models for cyber twins in manufacturing systems |
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a8d947a38cb58a8d2dfe6f50cb7eb1c6 |
author_id_fullname_str_mv |
a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia Giannetti |
author |
Cinzia Giannetti |
author2 |
Cinzia Giannetti Aniekan Essien |
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Journal article |
container_title |
Journal of Intelligent Manufacturing |
container_volume |
33 |
container_issue |
2 |
container_start_page |
441 |
publishDate |
2022 |
institution |
Swansea University |
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0956-5515 1572-8145 |
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10.1007/s10845-021-01804-0 |
publisher |
Springer Science and Business Media LLC |
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Faculty of Science and Engineering |
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School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering |
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Smart factories are intelligent, fully-connected and flexible systems that can continuously monitor and analyse data streams from interconnected systems to make decisions and dynamically adapt to new circumstances. The implementation of smart factories represents a leap forward compared to traditional automation. It is underpinned by the deployment of cyberphysical systems that, through the application of Artificial Intelligence, integrate predictive capabilities and foster rapid decision-making. Deep Learning (DL) is a key enabler for the development of smart factories. However, the implementation of DL in smart factories is hindered by its reliance on large amounts of data and extreme computational demand. To address this challenge, Transfer Learning (TL) has been proposed to promote the efficient training of models by enabling the reuse of previously trained models. In this paper, by means of a specific example in aluminium can manufacturing, an empirical study is presented, which demonstrates the potential of TL to achieve fast deployment of scalable and reusable predictive models for Cyber Manufacturing Systems. Through extensive experiments, the value of TL is demonstrated to achieve better generalisation and model performance, especially with limited datasets. This research provides a pragmatic approach towards predictive model building for cyber twins, paving the way towards the realisation of smart factories. |
published_date |
2022-02-01T08:17:41Z |
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11.051391 |