A deep neural network based model for a kind of magnetorheological dampers

Carlos A. Duchanoy, Marco A. Moreno-Armendáriz, Juan C. Moreno-Torres, Carlos A. Cruz-Villar

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, a deep neural network based model for a set of small-scale magnetorheological dampers (MRD) is developed where relevant parameters that have a physical meaning are inputs to the model. An experimental platform and a 3D-printing rapid prototyping facility provided a set of different conditions including MRD filled with two different MR fluids, which were used to train a Deep Neural Network (DNN), which is the core of the proposed model. Testing results indicate the model could forecast the hysteretic response of magnetorheological dampers for different load conditions and various physical configurations.

Original languageEnglish
Article number1333
JournalSensors (Switzerland)
Volume19
Issue number6
DOIs
StatePublished - 2 Mar 2019

Fingerprint

Neural Networks (Computer)
dampers
rapid prototyping
Rapid prototyping
printing
forecasting
Printing
platforms
Fluids
Three Dimensional Printing
Deep neural networks
fluids
Testing
configurations

Keywords

  • Automotive applications
  • Computational modeling
  • Neural networks

Cite this

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A deep neural network based model for a kind of magnetorheological dampers. / Duchanoy, Carlos A.; Moreno-Armendáriz, Marco A.; Moreno-Torres, Juan C.; Cruz-Villar, Carlos A.

In: Sensors (Switzerland), Vol. 19, No. 6, 1333, 02.03.2019.

Research output: Contribution to journalArticle

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