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dc.contributor.authorMendikute, Julen
dc.contributor.authorBaskaran, Maider
dc.contributor.authorLlavori, Inigo
dc.contributor.authorZugasti, Ekhi
dc.contributor.authorAretxabaleta, Laurentzi
dc.contributor.authorAurrekoetxea, Jon
dc.date.accessioned2024-02-02T08:53:11Z
dc.date.available2024-02-02T08:53:11Z
dc.date.issued2023
dc.identifier.issn1359-8368
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=173001
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6215
dc.description.abstractThe main objective of the present paper is to demonstrate the feasibility of machine-learning-based surrogate models for predicting low-velocity impact behaviour considering void content and location generated during the resin transfer moulding process. Generating reliable experimental datasets for training those models is almost impossible, therefore an adapted finite element model was implemented providing reliable results to generate the synthetic datasets. The optimum hyperparameter combination for training the Random Forest model was found based on the grid search technique. The accuracy of the classification, single-output regression and multi-output regression models was sufficient. It was concluded that the multi-output regression model, which predicts the force-time, displacement-time, and energy-time curves, provides the best information, and is sufficiently accurate (R2 > 0.995) and fast (5 s per sample) as an online structural performance monitoring tool.
dc.language.isoeng
dc.publisherElsevier
dc.rights© 2023 Elsevier
dc.subjectPolymer-matrix composites (PMCs)
dc.subjectImpact behaviour
dc.subjectSurrogate model
dc.subjectResin transfer moulding (RTM)
dc.titlePredicting the effect of voids generated during RTM on the low-velocity impact behaviour by machine learning-based surrogate models
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cf
dcterms.sourceComposites Part B: Engineering
local.contributor.groupTecnología de plásticos y compuestos
local.description.peerreviewedtrue
local.identifier.doihttps://doi.org/10.1016/j.compositesb.2023.110790
local.embargo.enddate2143-12-31
local.source.detailsVol. 260. N. art. 110790
oaire.format.mimetypeapplication/pdf
oaire.file$DSPACE\assetstore
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
oaire.funderNameEusko Jaurlaritza = Gobierno Vasco
oaire.funderNameEusko Jaurlaritza = Gobierno Vasco
oaire.funderNameEusko Jaurlaritza = Gobierno Vasco
oaire.funderIdentifierhttps://ror.org/00pz2fp31 http://data.crossref.org/fundingdata/funder/10.13039/501100003086
oaire.funderIdentifierhttps://ror.org/00pz2fp31 http://data.crossref.org/fundingdata/funder/10.13039/501100003086
oaire.funderIdentifierhttps://ror.org/00pz2fp31 http://data.crossref.org/fundingdata/funder/10.13039/501100003086
oaire.fundingStreamPrograma predoctoral de formación del personal investigador no doctor 2018-2019
oaire.fundingStreamIkertalde Convocatoria 2022-2025
oaire.fundingStreamElkartek 2018
oaire.awardNumberPRE_2018_1_0338
oaire.awardNumberIT1613-22
oaire.awardNumberKK-2017-00062
oaire.awardTitleSin información
oaire.awardTitleFabricación avanzada de composites
oaire.awardTitleComposites para automoción fabricados mediante RTM adaptada a filosofía Industry 4.0 (RTM4.0)
oaire.awardURISin información
oaire.awardURISin información
oaire.awardURISin información


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