A new predictive model based on the PSO-optimized support vector machine approach for predicting the milling tool wear from milling runs experimental data

dc.contributor.author	García Nieto, Paulino José
dc.contributor.author	García Gonzalo, María Esperanza
dc.contributor.author	Vilán Vilán, José Antonio
dc.contributor.author	Segade Robleda, Abraham
dc.date.accessioned	2016-05-06T09:27:43Z
dc.date.available	2016-05-06T09:27:43Z
dc.date.issued	2015
dc.identifier.citation	International Journal of Advanced Manufacturing Technology, 86(1), p. 1-12 (2015); doi:10.1007/s00170-015-8148-1
dc.identifier.issn	0268-3768
dc.identifier.uri	http://hdl.handle.net/10651/36852
dc.format.extent	p. 1-12
dc.language.iso	eng
dc.relation.ispartof	International Journal of Advanced Manufacturing Technology
dc.rights	©,
dc.source	Scopus
dc.source.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-84951749724&partnerID=40&md5=8cdf83029210629bfaeef9782f0468df
dc.title	A new predictive model based on the PSO-optimized support vector machine approach for predicting the milling tool wear from milling runs experimental data	eng
dc.type	journal article
dc.identifier.doi	10.1007/s00170-015-8148-1
dc.relation.publisherversion	http://dx.doi.org/10.1007/s00170-015-8148-1

Ficheros en el ítem

Ficheros	Tamaño	Formato	Ver
No hay ficheros asociados a este ítem.