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dc.contributor.authorJohansson, Joel
dc.contributor.authorContero, Manuel
dc.contributor.authorCompany, Pedro
dc.contributor.authorElgh, Fredrik
dc.date.accessioned2018-07-30T10:10:40Z
dc.date.available2018-07-30T10:10:40Z
dc.date.issued2018-10
dc.identifier.urihttp://hdl.handle.net/10234/175860
dc.description.abstractMass-customization has forced manufacturing companies to put significant efforts to digitize and automate their engineering and production processes. When new products are to be developed and introduced the production is not alone to be automated. The application of knowledge regarding how the product should be designed and produced based on customer requirements also must be automated. One big academic challenge is helping industry to make sure that the background knowledge of the automated engineering processes still can be understood by its stakeholders throughout the product life cycle. The research presented in this paper aims to build an infrastructure to support a connectivistic view on knowledge in knowledge based engineering. Fundamental concepts in connectivism include network formation and contextualization, which are here addressed by using graph theory together with information filtering techniques and quality assurance of CAD-models. The paper shows how engineering knowledge contained in spreadsheets, knowledge-bases and CAD-models can be penetrated and represented as filtered graphs to support a connectivistic working approach. Three software demonstrators developed to extract filtered graphs are presented and discussed in the paper.ca_CA
dc.format.extent12 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherElsevierca_CA
dc.rights© 2018 Elsevier Ltd. All rights reserved.ca_CA
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/*
dc.subjectconnectivismca_CA
dc.subjectmass-customizationca_CA
dc.subjectknowledge managementca_CA
dc.subjectknowledge-based engineeringca_CA
dc.subjectgraph theoryca_CA
dc.titleSupporting Connectivism in Knowledge Based Engineering with Graph Theory, Filtering Techniques and Model Quality Assuranceca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.1016/j.aei.2018.07.005
dc.relation.projectIDMINECO/AEI/FEDER, UE, project CAL-MBE (grant DPI2017-84526-R )ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://www.sciencedirect.com/science/article/pii/S1474034617304755ca_CA
dc.contributor.funderSwedish Knowledge Foundation (IMPACT project)ca_CA
dc.type.versioninfo:eu-repo/semantics/draftca_CA


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