CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin

Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data...

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Autores principales:	Evgeny Zotov, Visakan Kadirkamanathan
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	knowledge transfer transfer learning domain adaptation incremental learning artificial intelligence deep learning Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/fe7dc4dbca884776b83c306bda6c97ef
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spelling	oai:doaj.org-article:fe7dc4dbca884776b83c306bda6c97ef2021-12-01T03:35:36ZCycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin2624-821210.3389/frai.2021.767451https://doaj.org/article/fe7dc4dbca884776b83c306bda6c97ef2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/frai.2021.767451/fullhttps://doaj.org/toc/2624-8212Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data into actionable insights. Although a variety of frameworks and conceptual models addressing the requirements and advantages of digital twins has been suggested in the academic literature, their implementation has received less attention. The work presented in this paper aims to make a proposition that considers the novel challenges introduced for data analysis in the presence of heterogeneous and dynamic cyber-physical systems in Industry 4.0. The proposed approach defines a digital twin simulation tool that captures the dynamics of a machining vibration signal from a source model and adapts them to a given target environment. This constitutes a flexible approach to knowledge extraction from the existing manufacturing simulation models, as information from both physics-based and data-driven solutions can be elicited this way. Therefore, an opportunity to reuse the costly established systems is made available to the manufacturing businesses, and the paper presents a process optimisation framework for such use case. The proposed approach is implemented as a domain adaptation algorithm based on the generative adversarial network model. The novel CycleStyleGAN architecture extends the CycleGAN model with a style-based signal encoding. The implemented model is validated in an experimental scenario that aims to replicate a real-world manufacturing knowledge transfer problem. The experiment shows that the transferred information enables the reduction of the required target domain data by one order of magnitude.Evgeny ZotovVisakan KadirkamanathanFrontiers Media S.A.articleknowledge transfertransfer learningdomain adaptationincremental learningartificial intelligencedeep learningElectronic computers. Computer scienceQA75.5-76.95ENFrontiers in Artificial Intelligence, Vol 4 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	knowledge transfer transfer learning domain adaptation incremental learning artificial intelligence deep learning Electronic computers. Computer science QA75.5-76.95
spellingShingle	knowledge transfer transfer learning domain adaptation incremental learning artificial intelligence deep learning Electronic computers. Computer science QA75.5-76.95 Evgeny Zotov Visakan Kadirkamanathan CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin
description	Digitalisation of manufacturing is a crucial component of the Industry 4.0 transformation. The digital twin is an important tool for enabling real-time digital access to precise information about physical systems and for supporting process optimisation via the translation of the associated big data into actionable insights. Although a variety of frameworks and conceptual models addressing the requirements and advantages of digital twins has been suggested in the academic literature, their implementation has received less attention. The work presented in this paper aims to make a proposition that considers the novel challenges introduced for data analysis in the presence of heterogeneous and dynamic cyber-physical systems in Industry 4.0. The proposed approach defines a digital twin simulation tool that captures the dynamics of a machining vibration signal from a source model and adapts them to a given target environment. This constitutes a flexible approach to knowledge extraction from the existing manufacturing simulation models, as information from both physics-based and data-driven solutions can be elicited this way. Therefore, an opportunity to reuse the costly established systems is made available to the manufacturing businesses, and the paper presents a process optimisation framework for such use case. The proposed approach is implemented as a domain adaptation algorithm based on the generative adversarial network model. The novel CycleStyleGAN architecture extends the CycleGAN model with a style-based signal encoding. The implemented model is validated in an experimental scenario that aims to replicate a real-world manufacturing knowledge transfer problem. The experiment shows that the transferred information enables the reduction of the required target domain data by one order of magnitude.
format	article
author	Evgeny Zotov Visakan Kadirkamanathan
author_facet	Evgeny Zotov Visakan Kadirkamanathan
author_sort	Evgeny Zotov
title	CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin
title_short	CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin
title_full	CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin
title_fullStr	CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin
title_full_unstemmed	CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin
title_sort	cyclestylegan-based knowledge transfer for a machining digital twin
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/fe7dc4dbca884776b83c306bda6c97ef
work_keys_str_mv	AT evgenyzotov cyclestyleganbasedknowledgetransferforamachiningdigitaltwin AT visakankadirkamanathan cyclestyleganbasedknowledgetransferforamachiningdigitaltwin
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CycleStyleGAN-Based Knowledge Transfer for a Machining Digital Twin

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