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Production Process Modelling Architecture to Support Improved Cyber-Physical Production Systems

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dc.contributor.authorSeixas-Lopes, Fabio
dc.contributor.authorFerreira, José
dc.contributor.authorAgostinho, Carlos
dc.contributor.authorJardim-Gonçalves, Ricardo
dc.contributor.institutionDEE - Departamento de Engenharia Electrotécnica e de Computadores
dc.contributor.institutionCTS - Centro de Tecnologia e Sistemas
dc.contributor.institutionUNINOVA-Instituto de Desenvolvimento de Novas Tecnologias
dc.coverage.spatialCham
dc.date.accessioned2021-07-21T22:10:38Z
dc.date.available2021-07-21T22:10:38Z
dc.date.issued2020
dc.description
dc.description.abstractWith the proliferation of intelligent networks in industrial environments, manufacturing SME’s have been in a continuous search for integrating and retrofitting existing assets with modern technologies that could provide low-cost solutions for optimizations in their production processes. Their willingness to support a technological evolution is firmly based on the perception that, in the future, better tools will guarantee process control, surveillance and maintenance. For this to happen, the digitalization of valuable and extractable information must be held in a cost-effective manner, through contemporary approaches such as IoT, creating the required fluidity between hardware and software, for implementing Cyber-Physical modules in the manufacturing process. The goal of this work is to develop an architecture that will support companies to digitize their machines and processes through an MDA approach, by modeling their production processes and physical resources, and transforming into an implementation model, using contemporary CPS and IoT concepts, to be continuously improved using forecasting/predictive algorithms and analytics.en
dc.description.versionauthorsversion
dc.description.versionpublished
dc.format.extent8
dc.format.extent256264
dc.identifier.doi10.1007/978-3-030-45124-0_19
dc.identifier.isbn978-3-030-45123-3
dc.identifier.isbn978-3-030-45124-0
dc.identifier.issn1868-4238
dc.identifier.otherPURE: 32316755
dc.identifier.otherPURE UUID: 6a2eb6a4-9831-466f-99f7-bc78ebe1cbac
dc.identifier.otherScopus: 85084792766
dc.identifier.urihttp://hdl.handle.net/10362/121398
dc.identifier.urlhttps://www.scopus.com/pages/publications/85084792766
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer
dc.relationFunding Information: info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00066%2F2020/PT
dc.subjectArtificial Intelligence
dc.subjectCyber-physical systems
dc.subjectInternet of Things
dc.subjectInteroperability
dc.subjectModel driven architecture
dc.subjectProcess modelling
dc.subjectInformation Systems
dc.subjectComputer Networks and Communications
dc.subjectInformation Systems and Management
dc.titleProduction Process Modelling Architecture to Support Improved Cyber-Physical Production Systemsen
dc.typeconference object
degois.publication.firstPage206
degois.publication.lastPage213
degois.publication.titleTechnological Innovation for Life Improvement - 11th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020, Proceedings
degois.publication.title11th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2020
dspace.entity.typePublication
rcaap.rightsopenAccess

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