Prediction the Deformation in Upsetting Process by Neural Network and Finite Element
In this paper back-propagation artificial neural network
(BPANN) is employed to predict the deformation of the upsetting
process. To prepare a training set for BPANN, some finite element
simulations were carried out. The input data for the artificial neural
network are a set of parameters generated randomly (aspect ratio d/h,
material properties, temperature and coefficient of friction). The
output data are the coefficient of polynomial that fitted on barreling
curves. Neural network was trained using barreling curves generated
by finite element simulations of the upsetting and the corresponding
material parameters. This technique was tested for three different
specimens and can be successfully employed to predict the
deformation of the upsetting process
[1] N. Selvakumar, P. Ganesan, P. Radha, , R. Narayanasamy, and K.S.
Pandey, "Modelling the effect of particle size and iron content on
forming of Al-Fe composite preforms using neural network", Materials
& Design, Volume 28, Issue 1. 2007, Pages 119-130
[2] Siamak Serajzade, "Prediction of temperature distribution and required
energy in hot forging process by coupling neural networks and finite
element analysis",,MaterialsLetters Volume 61, Issues 14-15
[3] Siamak Serajzadeh"Prediction of thermo- mechanical behavior during
hot upsetting using neural networks" Materials Science and Engineering
in press 2008, Pages 140-147
[4] Zhihong Huanga, Margaret Lucasa, Michael J. Adam Modelling wall
boundary conditions in an elasto-viscoplastic material forming
processJournal of Materials Processing Technology 107 (2000) 267-275.
[5] Elman, J. L., "Finding structure in time", Cognitive Science, vol. 14,
pp.179-211,1990
[6] j.SI," theory and application of supervised learning method based on
gradiant algorithms", J tsinghau univ.vol 37,1997
[7] M.T.HAGEN,"training feed forward network with the levenbergmarquardt
algorithm", IEEE, pp 989-993, 1994.
[1] N. Selvakumar, P. Ganesan, P. Radha, , R. Narayanasamy, and K.S.
Pandey, "Modelling the effect of particle size and iron content on
forming of Al-Fe composite preforms using neural network", Materials
& Design, Volume 28, Issue 1. 2007, Pages 119-130
[2] Siamak Serajzade, "Prediction of temperature distribution and required
energy in hot forging process by coupling neural networks and finite
element analysis",,MaterialsLetters Volume 61, Issues 14-15
[3] Siamak Serajzadeh"Prediction of thermo- mechanical behavior during
hot upsetting using neural networks" Materials Science and Engineering
in press 2008, Pages 140-147
[4] Zhihong Huanga, Margaret Lucasa, Michael J. Adam Modelling wall
boundary conditions in an elasto-viscoplastic material forming
processJournal of Materials Processing Technology 107 (2000) 267-275.
[5] Elman, J. L., "Finding structure in time", Cognitive Science, vol. 14,
pp.179-211,1990
[6] j.SI," theory and application of supervised learning method based on
gradiant algorithms", J tsinghau univ.vol 37,1997
[7] M.T.HAGEN,"training feed forward network with the levenbergmarquardt
algorithm", IEEE, pp 989-993, 1994.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:54813", author = "H.Mohammadi Majd and M.Jalali Azizpour and Foad Saadi", title = "Prediction the Deformation in Upsetting Process by Neural Network and Finite Element", abstract = "In this paper back-propagation artificial neural network
(BPANN) is employed to predict the deformation of the upsetting
process. To prepare a training set for BPANN, some finite element
simulations were carried out. The input data for the artificial neural
network are a set of parameters generated randomly (aspect ratio d/h,
material properties, temperature and coefficient of friction). The
output data are the coefficient of polynomial that fitted on barreling
curves. Neural network was trained using barreling curves generated
by finite element simulations of the upsetting and the corresponding
material parameters. This technique was tested for three different
specimens and can be successfully employed to predict the
deformation of the upsetting process", keywords = "Back-propagation artificial neural network(BPANN), prediction, upsetting", volume = "5", number = "2", pages = "353-4", }