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Title:	Adaptive indirect neural network model for roughness in honing processes
Author:	Buj Corral, Irene; Sivatte Adroer, Mauricio; Llanas Parra, Francesc Xavier
Other authors:	Universitat Politècnica de Catalunya. Departament d'Enginyeria Mecànica; Universitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial; Universitat Politècnica de Catalunya. TECNOFAB - Grup de Recerca en Tecnologies de Fabricació
Abstract:	Honing processes provide a crosshatch pattern that allows oil flow, for example in combustion engine cylinders. This paper provides an adaptive neural network model for predicting roughness as a function of process parameters. Input variables are three parameters from the Abbott-Firestone curve, Rk, Rpk and Rvk. Output parameters are grain size, density of abrasive, pressure, linear speed and tangential speed. The model consists of applying a direct and an indirect model consecutively, with one convergence parameter and one error parameter. The indirect model has one network with 48 neurons and the direct model has three networks having 25, 9 and 5 neurons respectively. The adaptive one allows selecting discrete values for some variables like grain size or density.
Subject(s):	-Àrees temàtiques de la UPC::Enginyeria mecànica -Àrees temàtiques de la UPC::Enginyeria mecànica::Motors -Engines -Internal combustion engines -Neural networks (Computer science -Honing -Surface roughness -Artificial neural networks -Adaptive control -Motors (Mecànica) -Motors de combustió interna -Xarxes neuronals (Informàtica)
Rights:	Attribution-NonCommercial-NoDerivs 3.0 Spain http://creativecommons.org/licenses/by-nc-nd/3.0/es/
Document type:	Article - Submitted version Article
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