TY - JOUR
T1 - A deep neural network based model for a kind of magnetorheological dampers
AU - Duchanoy, Carlos A.
AU - Moreno-Armendáriz, Marco A.
AU - Moreno-Torres, Juan C.
AU - Cruz-Villar, Carlos A.
N1 - Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019/3/2
Y1 - 2019/3/2
N2 - 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.
AB - 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.
KW - Automotive applications
KW - Computational modeling
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85063280719&partnerID=8YFLogxK
U2 - 10.3390/s19061333
DO - 10.3390/s19061333
M3 - Artículo
C2 - 30884877
AN - SCOPUS:85063280719
SN - 1424-8220
VL - 19
JO - Sensors (Switzerland)
JF - Sensors (Switzerland)
IS - 6
M1 - 1333
ER -