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dc.rights.licenseAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.contributor.authorCernuda, Carlos
dc.contributor.otherLobato, Héctor
dc.contributor.otherZulueta Uriondo, Kepa
dc.contributor.otherArriaga, Aitor
dc.contributor.otherMatxain, Jon M.
dc.contributor.otherBurgoa, Aizeti
dc.date.accessioned2024-10-10T06:16:34Z
dc.date.available2024-10-10T06:16:34Z
dc.date.issued2024
dc.identifier.issn0020-7683en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178122en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6653
dc.description.abstractThe prediction of creep behavior plays a critical role in the design of thermoplastic materials intended for prolonged use. The creep modulus, which describes the relationship between stress and strain that a material experiences over time, is a key property to determine the long-term thermo-mechanical performance of thermoplastics. Due to the time-consuming and resource-intensive nature of testing for this property, the present work investigates the potential of data-driven techniques as an alternative approach. To accomplish this, a dataset comprising more than 400 distinct thermoplastic grades was obtained from CAMPUS® online open database. Then, various interpretable machine learning models (linear regression, decision trees, random forests, XGBoost, and LightGBM) were evaluated to predict the long-term creep modulus with data from brief tests. To accurately assess the models’ ability to generalize to new data, rigorous model evaluation techniques such as cross-validation and group-splitting were employed, showing that various algorithms can predict the creep modulus with scores above 0.99. Interestingly, linear regression not only matches but, in some cases, also surpasses the performance of more complex models, while being the most simple and interpretable. The present work demonstrates that machine learning can bypass the most lengthy creep tests; reducing costs, energy consumption, material waste, and product development time.en
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2024 Elsevieren
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectcreepen
dc.subjectMachine learningen
dc.subjectlinear regressionen
dc.subjectthermoplastic materialsen
dc.subjectmaterials informaticsen
dc.subjectODS 9 Industria, innovación e infraestructuraes
dc.subjectODS 12 Producción y consumo responsableses
dc.titlePrediction of long-term creep modulus of thermoplastics using brief tests and interpretable machine learningen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceInternational Journal of Solids and Structuresen
local.contributor.groupAnálisis de datos y ciberseguridades
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.ijsolstr.2024.113014en
local.embargo.enddate2026-11-30
local.contributor.otherinstitutionhttps://ror.org/02e24yw40en
local.contributor.otherinstitutionhttps://ror.org/000xsnr85en
local.source.detailsVol. 304. N. art. 113014, 2024
oaire.format.mimetypeapplication/pdfen
oaire.file$DSPACE\assetstoreen
oaire.resourceTypehttp://purl.org/coar/resource_type/c_6501en
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aaen


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Attribution-NonCommercial-NoDerivatives 4.0 International
Excepto si se señala otra cosa, la licencia del ítem se describe como Attribution-NonCommercial-NoDerivatives 4.0 International