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Neural network modelling of Abbott-Firestone roughness parameters in honing processes

info:eu-repo/semantics/openAccess ;ISSN: 1749-785X ;EISSN: 1749-7868 ;DOI: 10.1504/IJSURFSE.2017.088973

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Title:
Neural network modelling of Abbott-Firestone roughness parameters in honing processes
Author: Sivatte Adroer, Mauricio ; Buj Corral, Irene ; Llanas Parra, Francesc Xavier
Subjects: Abbott-Firestone roughness parameters ; ANN ; Artificial neural networks ; Back propagation algorithm ; Backpropagation algorithm ; Brunyiment ; Density of abrasive ; Enginyeria mecànica ; Fabricació assistida per ordinador ; Grain size ; Honing ; Honing machines ; Informàtica ; Linear speed ; Neural networks (Computer science) ; Pressure ; Processos de fabricació mecànica ; Surface roughness ; Tangential speed ; Àrees temàtiques de la UPC
Description: In present study, three roughness parameters defined in the Abbott-Firestone or bearing area curve, Rk, Rpk and Rvk, were modelled for rough honing processes by means of artificial neural networks (ANN). Input variables were grain size and density of abrasive, pressure of abrasive stones on the workpiece's surface, tangential or rotation speed of the workpiece and linear speed of the honing head. Two strategies were considered, either use of one network for modelling the three parameters at the same time or use of three networks, one for each parameter. Overall best neural network consists of three networks, one for each roughness parameter, with one hidden layer having 25, nine and five neurons for Rk, Rpk and Rvk respectively. However, use of one network for the three roughness parameters would allow addressing an indirect model. In this case, best solution corresponds to two hidden layers having 26 and 11 neurons. Peer Reviewed
Creation Date: 2017
Language: English
Identifier: ISSN: 1749-785X
EISSN: 1749-7868
DOI: 10.1504/IJSURFSE.2017.088973
Source: Recercat

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Neural network modelling of Abbott-Firestone roughness parameters in honing processes

info:eu-repo/semantics/openAccess ;ISSN: 1749-785X ;EISSN: 1749-7868 ;DOI: 10.1504/IJSURFSE.2017.088973

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