Prediction of Yoshida Uemori model parameters by The Bees Algorithm and Genetic Algorithm for 5xxx series aluminium alloys

In sheet metal forming processes, springback is a very important issue in the view of excellent quality design. Several mathematical models have been developed to estimate more accurately, including various material parameters. this study, model parameters Yoshida-Uemori two surface plasticity model, which can well predict for different loading conditions, determined using The Bees Algorithm and Genetic are frequently used recently optimization nonlinear problems. addition, performances algorithms frequency experimental data, dense-sparse, sparse-dense, dense-dense sparse-sparse elastic plastic regions. According results, although values, fitting found similar both Algorithm. However, data frequency, appropriate results obtained from set (Case 3).