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dc.contributor.authorSingh, Maulshree
dc.contributor.authorSrivastava, Rupal
dc.contributor.authorFuenmayor, Evert
dc.contributor.authorKuts, Vladimir
dc.contributor.authorQiao, Yuansong
dc.contributor.authorMurray, Niall
dc.contributor.authorDevine, Declan
dc.date.accessioned2022-08-29T09:11:07Z
dc.date.available2022-08-29T09:11:07Z
dc.date.copyright2022
dc.date.issued2022-06-14
dc.identifier.citationSingh, M.; Srivastava, R.; Fuenmayor, E.; Kuts, V.; Qiao, Y.; Murray, N.; Devine, D. Applications of Digital Twin across Industries: A Review. Applied Sciences. 2022. 12. 5727. https://doi.org/10.3390/app12115727en_US
dc.identifier.urihttp://research.thea.ie/handle/20.500.12065/4025
dc.description.abstractOne of the most promising technologies that is driving digitalization in several industries is Digital Twin (DT). DT refers to the digital replica or model of any physical object (physical twin). What differentiates DT from simulation and other digital or CAD models is the automatic bidirectional exchange of data between digital and physical twins in real-time. The benefits of implementing DT in any sector include reduced operational costs and time, increased productivity, better decision making, improved predictive/preventive maintenance, etc. As a result, its implementation is expected to grow exponentially in the coming decades as, with the advent of Industry 4.0, products and systems have become more intelligent, relaying on collection and storing incremental amounts of data. Connecting that data effectively to DTs can open up many new opportunities and this paper explores different industrial sectors where the implementation of DT is taking advantage of these opportunities and how these opportunities are taking the industry forward. The paper covers the applications of DT in 13 different industries including the manufacturing, agriculture, education, construction, medicine, and retail, along with the industrial use case in these industries/en_US
dc.formatPDFen_US
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.ispartofApplied Sciencesen_US
dc.rightsAttribution 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectDigital Twinen_US
dc.subjectIndustry 4.0en_US
dc.subjectSystem optimizationen_US
dc.subjectPredictive maintenanceen_US
dc.titleApplications of Digital Twin across industries: a reviewen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.contributor.affiliationTechnological University of the Shannon Midlands Midwesten_US
dc.contributor.sponsorThe APC was funded by 692 Science Foundation Irelanden_US
dc.description.peerreviewyesen_US
dc.identifier.doi10.3390/app12115727en_US
dc.identifier.eissn2076-3417
dc.identifier.orcidhttps://orcid.org/0000-0003-4788-1231en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-3127-4982en_US
dc.identifier.orcidhttps://orcid.org/0000-0001-8982-7845en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-8134-8636en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1543-1589en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-5919-0596en_US
dc.identifier.orcidhttps://orcid.org/0000-0002-1364-5583en_US
dc.identifier.volume12en_US
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessen_US
dc.subject.departmentMaterials Research Institute TUS:MMen_US
dc.type.versioninfo:eu-repo/semantics/publishedVersionen_US
dc.relation.projectid16/RC/3918en_US


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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International