Using the artificial neural network to investigate the effect of parameters in square cup deep drawing of aluminum-steel laminated sheets

Document Type : Research Paper


1 Faculty of Mechanical Engineering, Semnan University, Semnan, Iran

2 Department of Materials and Metallurgy Engineering, Birjand University of Technology, Birjand, Iran



In this study, the effective parameters involved in the deep drawing of double-layer metal sheets in a die of
square cross-section were investigated through artificial neural network (ANN) modeling. For this purpose,
first, the deep drawing of double-layer (Al1200 / ST14) sheets was carried out experimentally. Also, the finite
element simulation of the process was performed, and the results validated through experimental tests. A set
of 46 different experimental data were employed in this paper. The ANN was trained by using a mean square
error of 10-4. The input parameters, i.e., punch radius, die radius, blank holder force, clearance, and the permutation
layers were set to the network. The surface response method (RSM); was employed to evaluate the
results of the ANN model, and the input parameters of the deep drawing process on the thinning of Al1200
and ST14 composite layers were analyzed. The obtained results indicate that the punch edge radius has the
most significant influence on the thinning of the Al1200 layer. Increasing the gap between the punch and die
to 1/4 of the sheet thickness, increased the cup wall layers thickness of the Al1200 and ST14 respectively by
3.38% and 0.5%. The performance of the ANN model demonstrates that it can estimate the amount of thinning
in the composite layers with satisfactory accuracy.


Main Subjects

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