Identification of geometric parameters of drawbead in metal forming processes

A computational inverse technique is presented for identification of geometric parameters of drawbead in sheet forming processes. The explicit dynamic finite element method (FEM) is employed as the forward solver to calculate the maximal effective stress, maximal effective strain and maximal thinning ratio of sheet thickness for known drawbead geometric parameters. A neural network (NN) is adopted as the inverse operator to determine the geometric parameters of circular drawbead. A sample design method with the strategy of updating training sample set is developed for the fast convergence in the training process of NN model. Once the training sample set is updated, the NN structure will be optimized using the genetic algorithm (GA). The numerical examples are presented to demonstrate the efficiency of the technique.