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Towards scalable and reusable predictive models for cyber twins in manufacturing systems

Cinzia Giannetti Orcid Logo, Aniekan Essien

Journal of Intelligent Manufacturing

Swansea University Author: Cinzia Giannetti Orcid Logo

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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...

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Published in: Journal of Intelligent Manufacturing
ISSN: 0956-5515 1572-8145
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa57612
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first_indexed 2021-08-13T09:16:14Z
last_indexed 2021-12-02T04:14:49Z
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spelling 2021-12-01T13:32:16.3186875 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 MECH 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 0 Springer Science and Business Media LLC 0956-5515 1572-8145 Cyber physical systems; Transfer learning; ConvLSTM; Smart manufacturing; Deep learning 29 7 2021 2021-07-29 10.1007/s10845-021-01804-0 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University UKRI EPSRC EP/S001387/1 2021-12-01T13:32:16.3186875 2021-08-13T10:14:15.5035807 College of Engineering 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
author_id_str_mv a8d947a38cb58a8d2dfe6f50cb7eb1c6
author_id_fullname_str_mv a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia, Giannetti_***_0000-0003-0339-5872
author Cinzia, Giannetti
author2 Cinzia Giannetti
Aniekan Essien
format Journal article
container_title Journal of Intelligent Manufacturing
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publishDate 2021
institution Swansea University
issn 0956-5515
1572-8145
doi_str_mv 10.1007/s10845-021-01804-0
publisher Springer Science and Business Media LLC
college_str College of Engineering
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hierarchy_top_id collegeofengineering
hierarchy_top_title College of Engineering
hierarchy_parent_id collegeofengineering
hierarchy_parent_title College of Engineering
department_str Engineering{{{_:::_}}}College of Engineering{{{_:::_}}}Engineering
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description 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 2021-07-29T04:25:08Z
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score 10.852431