Determination of constitutive properties from spherical indentation data using neural networks. Part II: plasticity with nonlinear isotropic and kinematic hardening

We consider materials which can be described by plasticity laws exhibiting nonlinear kinematic and nonlinear isotropic hardening effects. The aim is to show that the material parameters governing the constitutive behavior may be determined from data obtained by spherical indentation. Note that only the measurable global quantities (load and indentation depth) should be utilized, which are available, e.g. from depth-sensing indentation tests. For this goal use is made of the method of neural networks. The developed neural networks apply also to the case of pure kinematic as well as pure isotropic hardening. Moreover it is shown how a monotonic strain-stress curve can be assigned to the spherical indentation test. (C) 1999 Elsevier Science Ltd. All rights reserved.