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Indirect model for roughness in rough honing processes based on artificial neural networks
dc.contributor.author | Sivatte Adroer, Mauricio |
dc.contributor.author | Llanas Parra, Francesc Xavier |
dc.contributor.author | Buj Corral, Irene |
dc.contributor.author | Vivancos Calvet, Joan |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria Mecànica |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial |
dc.date.accessioned | 2016-02-12T09:34:29Z |
dc.date.available | 2016-02-12T09:34:29Z |
dc.date.issued | 2016-01-01 |
dc.identifier.citation | Sivatte, M., Llanas, F., Buj, I., Vivancos, J. Indirect model for roughness in rough honing processes based on artificial neural networks. "Precisionn engineering - Journal of the American Society for Precision Engineering (ASPE)", 01 Gener 2016, vol. 43, p. 505-513. |
dc.identifier.issn | 0141-6359 |
dc.identifier.uri | http://hdl.handle.net/2117/82884 |
dc.description.abstract | In the present paper an indirect model based on neural networks is presented for modelling the rough honing process. It allows obtaining values to be set for different process variables (linear speed, tangential speed, pressure of abrasive stones, grain size of abrasive and density of abrasive) as a function of required average roughness Ra. A multilayer perceptron (feedforward) with a backpropagation (BP) training system was used for defining neural networks. Several configurations were tested with different number of layers, number of neurons and type of transfer function. Best configuration for the network was searched by means of two different methods, trial and error and Taguchi design of experiments (DOE). Once best configuration was found, a network was defined by means of trial and error method for roughness parameters related to Abbott-Firestone curve, Rk, Rpk and Rvk. © 2015 Elsevier Inc. All rights reserved |
dc.format.extent | 9 p. |
dc.language.iso | eng |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Enginyeria mecànica::Processos de fabricació mecànica::Fabricació assistida per ordinador |
dc.subject.lcsh | Surface roughness |
dc.subject.lcsh | Neural networks (Computer science) |
dc.subject.lcsh | Honing machines |
dc.subject.other | Artificial neural networks |
dc.subject.other | Honing |
dc.subject.other | Indirect model |
dc.subject.other | Surface roughness |
dc.title | Indirect model for roughness in rough honing processes based on artificial neural networks |
dc.type | Article |
dc.subject.lemac | Brunyiment |
dc.subject.lemac | Enginyeria mecànica -- Informàtica |
dc.contributor.group | Universitat Politècnica de Catalunya. TECNOFAB - Grup de Recerca en Tecnologies de Fabricació |
dc.contributor.group | Universitat Politècnica de Catalunya. CETpD -Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma |
dc.identifier.doi | 10.1016/j.precisioneng.2015.09.004 |
dc.description.peerreviewed | Peer Reviewed |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S0141635915001658 |
dc.rights.access | Open Access |
local.identifier.drac | 17420386 |
dc.description.version | Postprint (published version) |
local.citation.author | Sivatte, M.; Llanas, F.; Buj, I.; Vivancos, J. |
local.citation.publicationName | Precisionn engineering - Journal of the American Society for Precision Engineering (ASPE) |
local.citation.volume | 43 |
local.citation.startingPage | 505 |
local.citation.endingPage | 513 |
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