Hysteresis loop curves are highly important for numerical simulations of materials deformation under cyclic loadings. The models mainly take account of only the tensile half of the stabilized cycle in hysteresis loop for identification of the constants which don't vary with accumulation of plastic strain and strain range of the hysteresis loop. This approach may be quite erroneous particularly if the mean stress is not small and the effect of isotropic hardening is large. A strain dependent cyclic plasticity model which considers the variation of material constants versus strain range and accumulation of plastic strain has been proposed and experimentally investigated by the authors. In this paper it is proved that their proposed model is accurate for simulating all cycles of the hysteresis loop regardless of the strain range of the test. It is shown in this work that artificial neural network (ANN) model, if designed and trained properly, can be used for interpolating and extrapolating the experimental data. The results of this work are compared with two well-known cyclic plasticity models. The results also indicate that there is a remarkable agreement between the proposed model and ANN within and outside the strain ranges used in the experiments. © 2017, Materials and Energy Research Center. All rights reserved.
A strain range dependent cyclic plasticity model
BONORA, Nicola
2017-01-01
Abstract
Hysteresis loop curves are highly important for numerical simulations of materials deformation under cyclic loadings. The models mainly take account of only the tensile half of the stabilized cycle in hysteresis loop for identification of the constants which don't vary with accumulation of plastic strain and strain range of the hysteresis loop. This approach may be quite erroneous particularly if the mean stress is not small and the effect of isotropic hardening is large. A strain dependent cyclic plasticity model which considers the variation of material constants versus strain range and accumulation of plastic strain has been proposed and experimentally investigated by the authors. In this paper it is proved that their proposed model is accurate for simulating all cycles of the hysteresis loop regardless of the strain range of the test. It is shown in this work that artificial neural network (ANN) model, if designed and trained properly, can be used for interpolating and extrapolating the experimental data. The results of this work are compared with two well-known cyclic plasticity models. The results also indicate that there is a remarkable agreement between the proposed model and ANN within and outside the strain ranges used in the experiments. © 2017, Materials and Energy Research Center. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
