Experimental and numerical investigation of the deep drawing process for an automobile panel and prediction of appropriate amount of parameters by multi-layer neural network

In this paper, the deep drawing process of an automobile panel in order to select the appro-
priate amount of parameters has been investigated. The parameters include friction between
the blank and die, blank width and length, blank thickness and gap between the blank and
blank-holder. A multi-layer artificial neural network (ANN) trained by finite element ana-
lyses (FEA) is applied in order to improve forming parameters and achieve a better quality.
As the FEA results are used to train the ANN, the FEA results have been verified by three
experiments. Finally, an appropriate amount of each parameter is predicted by the trained
ANN and a FEA has been done based on the ANN prediction to evaluate the accuracy
of the trained ANN. Moreover, it is shown that the ANN could predict results within a
10 percent error. In addition, the proposed method for prediction of the appropriate para-
meters (ANN) is confirmed by comparing with the Taguchi design of experiment prediction.
It is also shown that the model obtained by the former method has lower errors than the
latter one. In this study, the Taguchi model is used to evaluate the effect of parameters on
tearing and wrinkling. Based on the Taguchi design of experiment, while the blank length is
the most effective parameter on tearing, the maximum height of wrinkles on flanged parts
mainly depends on the blank thickness.