A neural network tool for identifying the material parameters of a finite deformation viscoplasticity model with static recovery

摘要

In the present paper, the inverse problem of parameter identification is solved by using neural networks. In contrast to the com- monly used optimization methods, neural networks represent an explicit relation between the measured strain, stress, time and the material parameters to be identified. The constitutive model under consideration describes finite deformation viscoplasticity and ex- hibits static recovery in both the isotropic and the kinematic hardening laws. To train the neural networks, a loading history is utilized, which consists of a homogeneous uniaxial deformation including cyclic loading and relaxation phases.