Tube hydroforming optimization using a surrogate modeling approach and Genetic Algorithm

ABSTRACT The quality of a product obtained by hydroforming process is influenced by the geometrical, material and process parameters. In this paper, to predict an acceptable T-shaped tube with minimum wall thickness variations, and accomplishes the industrial requirements, a methodology based on the coupling of three-dimensional finite element incremental simulation based on Explicit Dynamic approach and an automatic surrogate model are proposed. The surrogate model is based on an adaptive moving target zone, and both Moving Least Square (MLS) and the Kriging technique. The optimization results are presented and compared in term of efficiency. Five quality criteria are used, an objective function defining the thickness variation with four nonlinear constraints functions, to reduce the risk of necking and to fulfil the industrial requirements. The proposed approach will provide a numerical estimation of the ``best'' tool dimensions and ideal punch stroke in order to obtain a final feasible workpiece.