Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Prediction the Limiting Drawing Ratio in Deep Drawing Process by Back Propagation Artificial Neural Network

Authors: H.Mohammadi Majd, M.Jalali Azizpour, M. Goodarzi

Abstract:

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.

Keywords: BPANN, deep drawing, prediction, limiting drawingratio (LDR), Levenberg–Marquardt algorithm

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1059897

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1516

References:


[1] 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.
[2] 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
[3] 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
[4] 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
[5] 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
[6] Elman, J. L., "Finding structure in time", Cognitive Science, vol. 14, pp.179-211,1990.
[7] j.SI," theory and application of supervised learning method based on gradiant algorithms", J tsinghau univ.vol 37,1997.
[8] M.T.HAGEN,"training feed forward network with the levenbergmarquardt algorithm", IEEE, pp 989-993, 1994.