Predicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology

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dc.catalogadoraua
dc.contributor.authorOmar Barrionuevo, German
dc.contributor.authorLuis Mullo, Jose
dc.contributor.authorRamos Grez, Jorge
dc.date.accessioned2024-03-04T15:34:11Z
dc.date.available2024-03-04T15:34:11Z
dc.date.issued2021
dc.fechaingreso.objetodigital2024-12-17
dc.fuente.origenORCID
dc.identifier.doi10.1007/S00170-021-07469-6
dc.identifier.urihttps://doi.org/10.1007/S00170-021-07469-6
dc.identifier.urihttps://publons.com/wos-op/publon/48326150/
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/83125
dc.identifier.wosidWOS:000668054100001
dc.information.autorucEscuela de Ingeniería; Ramos Grez, Jorge; 0000-0002-9293-3275; 81538
dc.language.isoen
dc.nota.accesocontenido parcial
dc.rightsacceso restringido
dc.titlePredicting the ultimate tensile strength of AISI 1045 steel and 2017-T4 aluminum alloy joints in a laser-assisted rotary friction welding process using machine learning: a comparison with response surface methodology
dc.typeartículo
sipa.codpersvinculados81538
sipa.trazabilidadORCID;2024-01-22
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