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Indirect model for roughness in rough honing processes based on artificial neural networks

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hdl:2117/82884
Document typeArticle
Defense date2016-01-01
Rights accessOpen Access
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Attribution-NonCommercial-NoDerivs 3.0 Spain
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
CitationSivatte, 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.
ISSN0141-6359
Publisher versionhttp://www.sciencedirect.com/science/article/pii/S0141635915001658
Collections
- CETpD -Centre d'Estudis Tecnològics per a l'Atenció a la Dependència i la Vida Autònoma - Articles de revista [31]
- Departament d'Enginyeria mecànica - Articles de revista [676]
- Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial - Articles de revista [1.541]
- TECNOFAB - Grup de Recerca en Tecnologies de Fabricació - Articles de revista [225]
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