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Memorias de investigación
Research Publications in journals:
Artificial neural network approach to predict the lubricated friction
Year:2014
Research Areas
  • Mechanical engineering,
  • Lubrication
Information
Abstract
This paper analyses the applicability of artificial neural networks for predicting the lubricated friction coefficient. We will consider their use as faster and simpler alternatives to simulations based on theoretical behaviour equations. The development of several different artificial neural networks is presented. They have been trained through tribological tests on a mini-traction-machine, which furnishes the friction coefficient in point contacts. Once the training has been completed the networks are applied as tools for predicting the results in different operating conditions. Their advantages and disadvantages are analysed compared with conventional simulation tools
International
Si
JCR
No
Title
LUBRICATION SCIENCE
ISBN
1557-6833
Impact factor JCR
Impact info
Volume
DOI: 10.1002/ls.1238
Journal number
From page
141
To page
162
Month
SIN MES
Ranking
Participants
  • Autor: Javier Echavarri Otero (UPM)
  • Autor: Eduardo de la Guerra Ochoa (TALGO)
  • Autor: Enrique Chacon Tanarro (UPM)
  • Autor: Pilar Lafont Morgado (UPM)
  • Autor: Andres Diaz Lantada (UPM)
  • Autor: Juan Manuel Muñoz Guijosa (UPM)
  • Autor: Jose Luis Muñoz Sanz (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: GI-IM: Grupo de Investigación en Ingeniería de Máquinas
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