Erregistro soila

dc.contributor.authorAranburu, Aritz
dc.contributor.authorCotillas Villa, Josu
dc.contributor.authorJustel Lozano, Daniel
dc.contributor.otherContero, Manuel
dc.contributor.otherDorribo Camba, Jorge
dc.date.accessioned2022-11-10T13:57:53Z
dc.date.available2022-11-10T13:57:53Z
dc.date.issued2022
dc.identifier.issn0010-4485en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=168131en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/5829
dc.description.abstractThe robustness and flexibility of a feature-based parametric CAD model determines the extent to which the geometry can be modified and reused in other design scenarios. The ability of a model to successfully adapt to changes depends on the type and sequence of the modeling operations selected to build the geometry, the parent–child dependencies defined during the modeling process, and the type and scope of the desired geometric change. Several formal modeling methodologies have been proposed to maximize model reusability, which have been shown to outperform unstructured approaches when designers need to manually modify the geometry. However, the effect of these parametric model strategies on the generation of valid solutions in heavily automated tasks has not yet been investigated. In this paper, we compare and analyze the performance of three well-established parametric modeling methodologies in various design optimization scenarios that involve the automatic generation of a large number of geometric variations. We discuss the results of a study with four parametric models of varying complexity and identify the limitations of each strategy in relation to the internal structure of the model. Our results show that explicit references and resilient modeling strategies are relatively robust for simple parts, but their effectiveness decreases significantly as the complexity of the model increases. In addition, we introduce the concept of intrinsic variability, which impacts the effectiveness of the methodology, and thus the quality of the parametric model, based on how the methodology is interpreted and executed.en
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2022 Elsevieren
dc.subjectCAD reusabilityen
dc.subjectParametric modeling methodologiesen
dc.subjectDesign intenten
dc.subjectCAD qualityen
dc.titleHow Does the Modeling Strategy Influence Design Optimization and the Automatic Generation of Parametric Geometry Variations?en
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceComputer-Aided Designen
local.contributor.groupCentro de Innovación en Diseñoes
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.cad.2022.103364en
local.embargo.enddate2024-10-31
local.contributor.otherinstitutionhttps://ror.org/01460j859ca
local.contributor.otherinstitutionhttps://ror.org/05p8z3f47en
local.source.detailsVol. 151. Artículo 103364. October, 2022en
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


Item honetako fitxategiak

Thumbnail

Item hau honako bilduma honetan/hauetan agertzen da

Erregistro soila