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dc.rights.licenseAttribution 4.0 International*
dc.contributor.authorUgarte Querejeta, Miriam
dc.contributor.authorIllarramendi, Miren
dc.contributor.otherMo, Fan
dc.contributor.otherHellewell, Joseph
dc.contributor.otherRehman, Hamood Ur
dc.contributor.otherChaplin, Jack C.
dc.contributor.otherSanderson, David
dc.contributor.otherRatchev, Svetan
dc.date.accessioned2024-04-15T14:43:46Z
dc.date.available2024-04-15T14:43:46Z
dc.date.issued2023
dc.identifier.issn0278-6125en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=174282en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6353
dc.description.abstractCurrent approaches to manufacturing must evolve to respond to increasing demands for short product life cycles and customised products. Adaptive manufacturing systems integrate advanced technologies, automation, and data-driven methodologies to develop adaptable, efficient, and responsive production processes. Central to this concept is the emphasis on human involvement and fostering synergy between human operators and the manufacturing system. Significant changes to the system’s controller are required to achieve adaptivity, with programmable logic controllers (PLCs) being a common controller type. After the necessary changes to the configuration of the manufacturing system, the PLC should be reconfigured to orchestrate the new required behaviour. Automated reconfiguration is vital to rapidly responding to change, but some changes cannot be entirely achieved without human input in collaboration with automated methods. Conventional practices in PLC programming include manual, repetitive coding practices subject to errors. As a result, to ensure operational safety, the changes must be tested before being deployed to operations, ensuring it is error-free. This paper presents a methodology to automatically reconfigure the simulation environment and controller in response to a new product request. We automate the PLC code generation and testing practices to support and free up the operators when performing repetitive manufacturing reconfiguration tasks. The methodology is based on human learning, software automation, customised program development, knowledge graphs, and Graph Neural Networks (GNNs). The presented solution is a generic, vendor-agnostic, and interoperable solution that facilitates information exchange among multiple heterogeneous environments. Lastly, we have validated the methodology as a proof of concept at an adaptive assembly cell at the University of Nottingham in the United Kingdom.en
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2023 The Authorsen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectPLCen
dc.subjectAutomatic program generationen
dc.subjectStructural testingen
dc.subjectAdaptive manufacturing systemsen
dc.subjectIndustry 4.0en
dc.subjectKnowledge graphen
dc.subjectGraph neural networken
dc.subjectODS 9 Industria, innovación e infraestructura
dc.titlePLC orchestration automation to enhance human-machine integration in adaptive manufacturing systemsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_abf2en
dcterms.sourceJournal of Manufacturing Systemsen
local.contributor.groupIngeniería del software y sistemases
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.jmsy.2023.07.015en
local.contributor.otherinstitutionTQC Automation Ltd.en
local.contributor.otherinstitutionhttps://ror.org/01ee9ar58en
local.source.detailsVol. 71. Pp. 172-187
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85en
oaire.funderNameComisión Europeaen
oaire.funderNameUK Engineering and Physical Science Research Councilen
oaire.funderIdentifierhttps://ror.org/00k4n6c32 / http://data.crossref.org/fundingdata/funder/10.13039/501100000780en
oaire.fundingStreamH2020en
oaire.fundingStreamSin informaciónen
oaire.awardNumber814078en
oaire.awardNumberEP/T024429/1en
oaire.awardTitleDigital Manufacturing and Design Training Network. DiManDen
oaire.awardTitleElastic Manufacturing Systems projecten
oaire.awardURIhttps://doi.org/10.3030/814078en
oaire.awardURISin informaciónen


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