2026-04-20T14:28:50Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/66852024-10-26T06:15:29Zcom_20.500.11984_473col_20.500.11984_478

Llavori, Inigo Aginagalde, Andrea Zabala, Alaitz 2024-10-24T14:21:48Z 2024-10-24T14:21:48Z 2024 0301-679X https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=178123 https://hdl.handle.net/20.500.11984/6685 Femtosecond 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. eng Attribution-NonCommercial 4.0 International http://creativecommons.org/licenses/by-nc/4.0/ © 2024 The Authors laser Inverse modelling Machine learning Stamping surface texturing On the use of machine learning for predicting femtosecond laser grooves in tribological applications http://purl.org/coar/resource_type/c_6501