Prediction the Limiting Drawing Ratio in Deep Drawing Process by Back Propagation Artificial Neural Network
In this paper back-propagation artificial neural
network (BPANN) with Levenberg–Marquardt algorithm is
employed to predict the limiting drawing ratio (LDR) of the deep
drawing process. To prepare a training set for BPANN, some finite
element simulations were carried out. die and punch radius, die arc
radius, friction coefficient, thickness, yield strength of sheet and
strain hardening exponent were used as the input data and the LDR
as the specified output used in the training of neural network. As a
result of the specified parameters, the program will be able to
estimate the LDR for any new given condition. Comparing FEM and
BPANN results, an acceptable correlation was found.
 J.W. Chan, "A study of limitation of sheet metal stretching and
drawing processes", Ph.D. Thesis, Department of Mechanical
Engineering and Technology, National Taiwan Institute of
Technology, Taipei, Taiwan, 1995.
 Leu DK ,"Prediction of the limiting drawing ratio and the maximum
drawing load in cup drawing". Int J Machine Tools Manufacture.
vol 37(2),pp 201-213. 1997
 Tung-sheng yang " The application of abductive networks and FEM
to predict the limiting drawing ratio in sheet metal forming
processes ", The International Journal of Advanced Manufacturing
Technology, February 2007, pp 58-69
 Daw-Kwei Leu," The limiting drawing ratio for plastic instability of
the cup-drawing process",Journal of Materials Processing
Technology, vol 86 ,pp168-176. 1999
 H. Mohammadi Majd, M. Poursina, K. H. Shirazi," Determination
of barreling curve in upsetting process by artificial neural
networks", 9th WSEAS international conference on Simulation,
modelling and optimization, Budapest, Hungary, 2009, pp 271-274
 Elman, J. L., "Finding structure in time", Cognitive Science, vol.
 j.SI," theory and application of supervised learning method based
on gradiant algorithms", J tsinghau univ.vol 37,1997.
 M.T.HAGEN,"training feed forward network with the levenbergmarquardt
algorithm", IEEE, pp 989-993, 1994.