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dc.contributor.authorMeneses Freire, Antonio
dc.contributor.authorNaya, Salvador
dc.contributor.authorFrancisco-Fernández, Mario
dc.contributor.authorLópez-Beceiro, Jorge
dc.contributor.authorGracia-Fernández, Carlos
dc.contributor.authorTarrío-Saavedra, Javier
dc.date.accessioned2023-11-27T17:27:48Z
dc.date.available2023-11-27T17:27:48Z
dc.date.issued2023
dc.identifier.citationMeneses, A. et al. (2023) ‘TTS package: Computational tools for the application of the time temperature superposition principle’, Heliyon, 9(5). doi:10.1016/j.heliyon.2023.e15816.es_ES
dc.identifier.issn2405-8440
dc.identifier.urihttp://hdl.handle.net/2183/34346
dc.description.abstract[Abstract]: The TTS package has been developed in R software to predict the mechanical properties of viscoelastic materials, at short and long observation times/frequencies by applying the Time Temperature Superposition (TTS) principle. TTS is a physical principle used in material science to estimate mechanical properties beyond the experimental range of observed times/frequencies by shifting data curves obtained at other temperatures relative to a reference temperature in the dataset. It is a methodology related to accelerated life-tests and reliability, whereas the TTS library is one of the first open source computational tool to apply the TTS principle. This R package provides free computational tools to obtain master curves that characterize materials from a thermal-mechanical approach. The TTS package also proposes, implements and explains our own method to obtain the shift factors and the master curve in a TTS analysis, based on horizontal shifting of the first derivative function of viscoelastic properties. This procedure provides shift factors estimates and smooth master curve estimates using B-spline fitting, in a fully automatic way, without assuming any parametric expression. Williams-Landel-Ferry (WLF) and Arrhenius TTS parametric models are also implemented in the TTS package. They can be fitted from shifts obtained by the our first derivative based method.es_ES
dc.description.sponsorshipThis work has been supported by Campus Industrial de Ferrol (Universidade da Coruña), Ministerio de Ciencia e Innovación grant PID2020-113578RB-100 and by the Xunta de Galicia (Grupos de Referencia Competitiva ED431C-2016-015 and Centro Singular de Investigación de Galicia ED431G/01), all of them through the ERDF. The authors would like to express their gratitude to the two reviewers of this article, whose contributions and suggestions have made a significant contribution to providing users with a truly useful tool.es_ES
dc.description.sponsorshipXunta de Galicia; ED431C-2016-015es_ES
dc.description.sponsorshipXunta de Galicia; ED431G/01es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-113578RB-I00/ES/METODOS ESTADISTICOS FLEXIBLES EN CIENCIA DE DATOS PARA DATOS COMPLEJOS Y DE GRAN VOLUMEN: TEORIA Y APLICACIONESes_ES
dc.relation.urihttps://doi.org/10.1016/j.heliyon.2023.e15816es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 Españaes_ES
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/es_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectTime Temperature Superpositiones_ES
dc.subjectThermal analysises_ES
dc.subjectStatistical modelinges_ES
dc.subjectR softwarees_ES
dc.titleTTS package: Computational tools for the application of the Time Temperature Superposition principlees_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleHeliyones_ES
UDC.volume9es_ES
UDC.issue5es_ES
dc.identifier.doi10.1016/j.heliyon.2023.e15816


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