| 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 | |
