Simple record

dc.contributor.authorLegaristi Labajos, Jon
dc.contributor.authorLarrinaga, Felix
dc.contributor.authorZugasti, Ekhi
dc.contributor.authorCuenca, Javier
dc.contributor.otherIñigo, Michel
dc.contributor.otherKremer, Blanca
dc.contributor.otherAyuso, Mikel
dc.contributor.otherMontejo, Elena
dc.contributor.otherEstepa, Daniel
dc.date.accessioned2024-03-26T14:27:51Z
dc.date.available2024-03-26T14:27:51Z
dc.date.issued2023
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=176353en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6323
dc.description.abstractThis article develops an architecture for the implementation of Artificial Intelligence in the manufacturing value chain based on standard technologies and data spaces. The standards considered are IEC 63278 “Asset Administration Shell (AAS) for industrial applications” and DIN SPEC 27070:2020 – “Requirements and reference architecture of a security gateway for the exchange of industry data and services“ by IDSA. The architecture provides a data space that allows MONDRAGON industrial cooperatives to use data for the execution of advanced data analytics, Artificial Intelligence (AI) algorithms and interoperability between assets and IoT-platforms. The development of knowledge in this field allows, on the one hand, to optimise the consolidation of data as a strategic factor and, on the other hand, to increase collaboration between manufacturing companies, suppliers and technology providers. The article also explores specific Artificial Intelligence technologies with a wide application in industrial environments. In particular, the study has focused on research into Low/No Code, Explainability (XAI) tools and incremental learning algorithms. The contributions of this paper are summarised in 1) creating an IDS-AAS based architecture and data space that allows the exploitation of AI use cases, either by directly downloading models or by using AI as a service, 2) identifying useful AI tools for industry such as AutoML, No/Low code, XAI or incremental learning, 3) implementing a use case where different AI use alternatives are implemented.en
dc.language.isoengen
dc.rights© 2023 The Authorsen
dc.subjectAI applications in manufacturing systemsen
dc.subjectData spacesen
dc.subjectAsset Administration Shell (AAS)en
dc.subjectDigital platformsen
dc.titleTowards an Advanced Artificial Intelligence Architecture through Asset Administration Shell and Industrial Data Spacesen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.source1st European Symposium on Artificial Intelligence in Manufacturing (ESAIM2023)en
local.contributor.groupIngeniería del software y sistemases
local.description.peerreviewedfalseen
local.embargo.enddate2144-01-01
local.contributor.otherinstitutionhttps://ror.org/03hp1m080es
local.contributor.otherinstitutionhttps://ror.org/04z0p3077es
local.contributor.otherinstitutionhttps://ror.org/003qafx79es
local.source.details19 September 2023. Kaiserslautern, Germanyen
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_c94fen
oaire.versionhttp://purl.org/coar/version/c_b1a7d7d4d402bcceen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Simple record