Determination of constitutive properties from spherical indentation data using neural networks. Part I: the case of pure kinematic hardening in plasticity laws

In this paper the power of neural networks in identifying material paramete rs from data obtained by spherical indentation is demonstrated for an acade mic problem (pure kinematic hardening, given Young's modulus). To obtain a data basis for the training and validation of the neural network, numerous finite element simulations were carried out for various sets of material pa rameters. The constitutive model describing finite deformation plasticity i s formulated with nonlinear kinematic hardening of Armstrong-Frederick type . It was shown by Huber and Tsakmakis (1998a) that the depth-load response of a cyclic indentation process, consisting of loading, unloading and reloa ding of the indenter displays a typical hysteresis loop for given material parameters. The inverse problem of leading the depth-load response back to the related parameters in the constitutive equations is solved using a neut ral network. (C) 1999 Elsevier Science Ltd. All rights reserved.