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dc.contributor.authorGuinea, Ainhoa
dc.contributor.authorAginagalde, Andrea
dc.contributor.authorMendiguren, Joseba
dc.contributor.authorSáenz de Argandoña, Eneko
dc.contributor.authorLlavori, Inigo
dc.contributor.authorZabala, Alaitz
dc.date.accessioned2025-02-17T09:20:44Z
dc.date.available2025-02-17T09:20:44Z
dc.date.issued2025
dc.identifier.issn0257-8972en
dc.identifier.otherhttps://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=180107en
dc.identifier.urihttps://hdl.handle.net/20.500.11984/6901
dc.description.abstractThe appropriate selection of ultrashort laser processing parameters is a complex task in which prediction models based on the logarithmic ablation law derived from the Two-Temperature model are commonly used to minimize experimental effort. Although the model's accuracy depends on the threshold fluence ( ) and energy penetration depth ( ) values used, which evolve with the number of pulses (Np) due to the incubation effect, there is no consensus on the optimal determination method or incubation description. A benchmark on ablation threshold determination methods and incubation models was conducted on stainless steel to (i) analyse their effects on and and (ii) evaluate the impact on diameter and depth prediction errors of multi-shot dimples. Four ablation threshold determination methods and four incubation models, along with their variants, were identified. This resulted in a total of 24 combinations, referred to as estimation approaches. For a single pulse, varied up to 73 % depending on the estimation approach, whereas was less sensitive, with differences up to 40 %. Regarding prediction errors, they remained below 12 % for diameter across all estimation approaches. Depth prediction errors exhibited greater sensitivity, ranging from 11 % to 33 %. This study emphasizes the importance of the estimation approach used for both ablation threshold and incubation parameter determination and its impact on the multi-shot dimple prediction accuracy.en
dc.language.isoengen
dc.publisherElsevieren
dc.rights© 2025 Elsevieren
dc.subjectUltrashort laseren
dc.subjectprediction modelsen
dc.subjectMulti-shot ablationen
dc.subjectAblation thresholden
dc.subjectIncubation modelen
dc.subjectTwo-temperature modelen
dc.titleDimple prediction modelling in ultrashort laser processing: A benchmark study on ablation threshold determination methods and incubation modelsen
dcterms.accessRightshttp://purl.org/coar/access_right/c_f1cfen
dcterms.sourceSurface and Coatings Technologyen
local.contributor.groupTecnologías de superficieses
local.description.peerreviewedtrueen
local.identifier.doihttps://doi.org/10.1016/j.surfcoat.2024.131712en
local.embargo.enddate2027-02-26
local.source.detailsVol. 497. N. art. 131712, 2025en
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
dc.unesco.tesaurohttp://vocabularies.unesco.org/thesaurus/concept5015en
oaire.funderNameDiputación Foral de Gipuzkoaen
oaire.funderNameDiputación Foral de Gipuzkoaen
oaire.funderIdentifierhttps://ror.org/05bvkb649 = http://data.crossref.org/fundingdata/funder/10.13039/501100019124en
oaire.funderIdentifierhttps://ror.org/05bvkb649 = http://data.crossref.org/fundingdata/funder/10.13039/501100019124en
oaire.fundingStreamPrograma de Red Guipuzcoana de Ciencia, Tecnología e Innovación 2021en
oaire.fundingStreamPrograma de Red Guipuzcoana de Ciencia, Tecnología e Innovación 2023en
oaire.awardNumber2021-000291-01-Ben
oaire.awardNumber2023-CIEN-000037-01en
oaire.awardTitleDigitalización de Procesos de Funcionalización Superficial Láser Bioinspirada para Conformado Sostenible (DIGILAS)en
oaire.awardTitleEficiencia energética en componentes de la industria del H2 a través del diseño y caracterización de materiales y superficies avanzadas (ENERGY EH2)en
oaire.awardURISin informaciónen
oaire.awardURISin informaciónen
dc.unesco.clasificacionhttp://skos.um.es/unesco6/3312en


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