On the use of machine learning for predicting femtosecond laser grooves in tribological applications

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dc.contributor.authorMoles, Luis
dc.contributor.authorLlavori, Iñigo
dc.contributor.authorAginagalde, Andrea
dc.contributor.authorEchegaray, Goretti
dc.contributor.authorBruneel, David
dc.contributor.authorBoto Sánchez, Fernando
dc.contributor.authorZabala, Alaitz
dc.date.accessioned2024-11-12T13:15:50Z
dc.date.available2024-11-12T13:15:50Z
dc.date.issued2024-12
dc.date.updated2024-11-12T13:15:50Z
dc.description.abstractFemtosecond laser surface texturing is gaining increased interest for optimizing tribological behaviour. However, the laser surface texturing parameter selection is often conducted through time-consuming and inefficient trial-and-error processes. Although machine learning emerges as an interesting option, multitude of models exists, and determining the most suitable one for predicting femtosecond laser textures remains uncertain. Furthermore, the absence of open-source implementations and the expertise required for their utilization hinders their adoption within the tribology community. In this study, two novel inverse modelling approaches for the optimal prediction of femtosecond laser parameters are proposed, based on the results of a comparison between six different machine learning models conducted within this research. The entire development relies on open-source tools, and the models employed are shared, with the aim of democratizing these techniques and facilitating their adoption by non-expert users within the tribology community.en
dc.description.sponsorshipThe authors gratefully acknowledge the financial support given by the Basque Government (Eusko Jaurlaritza) under “Programa de apoyo a la investigación colaborativa en áreas estratégicas” (Project BISUM: Ref. KK-2021/00089) programsen
dc.identifier.citationMoles, L., Llavori, I., Aginagalde, A., Echegaray, G., Bruneel, D., Boto, F., & Zabala, A. (2024). On the use of machine learning for predicting femtosecond laser grooves in tribological applications. Tribology International, 200. https://doi.org/10.1016/J.TRIBOINT.2024.110067
dc.identifier.doi10.1016/J.TRIBOINT.2024.110067
dc.identifier.issn0301-679X
dc.identifier.urihttp://hdl.handle.net/20.500.14454/1780
dc.language.isoeng
dc.publisherElsevier Ltd
dc.rights© 2024 The Author(s)
dc.subject.otherFemtosecond laser
dc.subject.otherInverse modelling
dc.subject.otherMachine learning
dc.subject.otherStamping
dc.subject.otherSurface texturing
dc.titleOn the use of machine learning for predicting femtosecond laser grooves in tribological applicationsen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.titleTribology International
oaire.citation.volume200
oaire.licenseConditionhttps://creativecommons.org/licenses/by-nc/4.0/
oaire.versionVoR
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