Parameter identification of an elasto-plastic behaviour using artificial neural networks–genetic algorithm method

The simulation of the metal forming processes requires accurate constitutive models to describe the material behaviour at finite strain taking into account several conditions. The choice of a rheological model and the determination of its parameters should be made from a test that generates such conditions. The major difficulty encountered is that there is no experimental test satisfying all these criteria. The use of more than one test seems more and more essential, and it is utilized to characterize the rheological behaviour at operating conditions that correspond to metal forming applications. An Inverse analysis is then considered. Therefore, the difficulty lies within the long computing time taken when an optimization procedure is coupled with a finite element computation (FEC) to identify the material parameters. In order to solve the computing time problem, this paper proposes a hybrid identification method based on finite elements, neural network computations and genetic algorithm (GA) of an elasto-plastic behaviour model. The strategy suggested is then applied to identify the Karafillis and Boyce criterion and the Voce parameters model of the Stainless Steel AISI 304 using two tests (plane tensile test and bulge test with a circular die) at the same time.