Repository logo
 
Publication

A New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Future

2020Journal article Open access
Simple item page
dc.contributor.authorBécue, Adrien
dc.contributor.authorMaia, Eva
dc.contributor.authorFeeken, Linda
dc.contributor.authorBorchers, Philipp
dc.contributor.authorPraça, Isabel
dc.date.accessioned2022-01-11T15:45:27Z
dc.date.available2022-01-11T15:45:27Z
dc.date.issued2020
dc.description.abstractIn the context of Industry 4.0, a growing use is being made of simulation-based decision-support tools commonly named Digital Twins. Digital Twins are replicas of the physical manufacturing assets, providing means for the monitoring and control of individual assets. Although extensive research on Digital Twins and their applications has been carried out, the majority of existing approaches are asset specific. Little consideration is made of human factors and interdependencies between different production assets are commonly ignored. In this paper, we address those limitations and propose innovations for cognitive modeling and co-simulation which may unleash novel uses of Digital Twins in Factories of the Future. We introduce a holistic Digital Twin approach, in which the factory is not represented by a set of separated Digital Twins but by a comprehensive modeling and simulation capacity embracing the full manufacturing process including external network dependencies. Furthermore, we introduce novel approaches for integrating models of human behavior and capacities for security testing with Digital Twins and show how the holistic Digital Twin can enable new services for the optimization and resilience of Factories of the Future. To illustrate this approach, we introduce a specific use-case implemented in field of Aerospace System Manufacturing.pt_PT
dc.description.sponsorshipThe present work was developed under the EUREKA–ITEA3 Project CyberFactory#1 (ITEA-17032), co-funded by Project CyberFactory#1PT (ANI|P2020 40124), from FEDER Funds through NORTE2020 program and from National Funds through FCT under the project UID/EEA/00760/2019 and by the Federal Ministry of Education and Research (BMBF, Germany, funding No. 01IS18061C).pt_PT
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.3390/app10134482pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.22/19399
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relationANI|P2020 40124pt_PT
dc.relation.publisherversionhttps://www.mdpi.com/2076-3417/10/13/4482pt_PT
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/pt_PT
dc.subjectFactory of the Futurept_PT
dc.subjectDigital Twinpt_PT
dc.subjectCyber-Rangept_PT
dc.subjectProcess optimizationpt_PT
dc.subjectCyber-resiliencept_PT
dc.subjectModeling and simulationpt_PT
dc.subjectCyber-physical system modelingpt_PT
dc.subjectHuman behavior modelingpt_PT
dc.subjectCo-simulationpt_PT
dc.subjectAnomaly detectionpt_PT
dc.subjectAttack detectionpt_PT
dc.titleA New Concept of Digital Twin Supporting Optimization and Resilience of Factories of the Futurept_PT
dc.typejournal article
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/5876/UID%2FEEA%2F00760%2F2013/PT
oaire.citation.issue13pt_PT
oaire.citation.startPage4482pt_PT
oaire.citation.titleApplied Sciencespt_PT
oaire.citation.volume10pt_PT
oaire.fundingStream5876
person.familyNameMaia
person.familyNamePraça
person.givenNameEva
person.givenNameIsabel
person.identifier299522
person.identifier.ciencia-id4F14-EF83-C4B9
person.identifier.ciencia-idC710-4218-1BFF
person.identifier.orcid0000-0002-8075-531X
person.identifier.orcid0000-0002-2519-9859
person.identifier.ridK-8430-2014
person.identifier.scopus-author-id22734900800
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublication47a108c4-cf8a-46f3-8954-90624174e8fc
relation.isAuthorOfPublicationee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isAuthorOfPublication.latestForDiscoveryee4ecacd-c6c6-41e8-bca1-21a60ff05f50
relation.isProjectOfPublication237af9d5-70ed-4e45-9f10-3853d860255e
relation.isProjectOfPublication.latestForDiscovery237af9d5-70ed-4e45-9f10-3853d860255e

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
ART_GECAD_applsci_2020.pdf
Size:
3.46 MB
Format:
Adobe Portable Document Format
Download

Collections

ISEP – GECAD – Artigos