A hybrid NN-FE approach to adjust blank holder gap over punch stroke in deep drawing process

Abstract

In deep drawing process, the blank holder plays a key role in adjustment of metal flow into the die cavity. Moreover, the quality of drawn parts is extremely affected by this flow. There are two methods of treating the blank holder in deep drawing and its simulation. One is blank holder force (BHF) and the other is blank holder gap (BHG), defined as the fixed distance between the blank holder and the die surface. In previous studies, a large number of experimental techniques have been used to study BHF; however, the amount of theoretical and numerical simulation work to study BHG is insufficient. In the present study, the concept of BHG profile, i.e., variation of BHG over punch stroke is introduced and it is shown that a properly selected BHG profile can improve the section thickness of formed part and result in the drawing of deeper parts. Here, two methods for the optimization of BHG profile are devised, i.e., the local optimization and the global optimization methods. In the first approach, the best BHG in each punch step is determined and finally, the local optimized BHG profile is achieved. In the second method, however, the empirical model for the prediction of final minimum section thickness in terms of BHG profile is obtained using design of experiments and neural networks. In the next stage, the proposed model is implanted into a simulated annealing optimization procedure to identify a proper BHG profile that can produce the desired blank thickness. Afterward, the BHG profile approach is applied to a variety of initial thicknesses, blank diameters, and materials in order to examine the robustness of method. In this paper, ABAQUS finite element package is used to gather finite element (FE) data and several experiments are performed to verify the FE results.