@article{(Open Science Index):https://publications.waset.org/pdf/5724,
	  title     = {Prediction the Deformation in Upsetting Process by Neural Network and Finite Element},
	  author    = {H.Mohammadi Majd and  M.Jalali Azizpour  and  Foad Saadi},
	  country	= {},
	  institution	= {},
	  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},
	    journal   = {International Journal of Mechanical and Mechatronics Engineering},
	  volume    = {5},
	  number    = {2},
	  year      = {2011},
	  pages     = {345 - 348},
	  ee        = {https://publications.waset.org/pdf/5724},
	  url   	= {https://publications.waset.org/vol/50},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 50, 2011},