Towards neural network models for describing the large deformation behavior of sheet metal

Neural networks provide a potentially viable alternative to a differential equation based constitutive models. Here, a neural network model is developed to describe the large deformation response of a Levy-von Mises sheet material with isotropic strain hardening. Using a conventional return-mapping scheme, virtual experiments are performed to generate stress-strain data for random monotonic biaxial loading paths (up to strains of 0.2). Subsequently, a basic feedforward neural network model is trained and validated using the results from virtual experiments. The results for a shallow network with only two hidden layers show remarkably good agreement with all experimental data. The identified neural network model is implemented into a user material subroutine and used in basic structural simulations such as uniaxial tensile and notched tension experiments. In addition to demonstrating the potential of neural networks for modeling the rate-independent plasticity of metals, their application to more complex problems involving strain-rate and temperature effects is discussed.ISSN:1757-8981ISSN:1757-899