Research output: Contribution to journal › Article › peer-review
Optimal error functional for parameter identification in anisotropic finite strain elasto-plasticity. / Shutov, A. V.; Kaygorodtseva, A. A.; Dranishnikov, N. S.
In: Journal of Physics: Conference Series, Vol. 894, No. 1, 012133, 22.10.2017.Research output: Contribution to journal › Article › peer-review
}
TY - JOUR
T1 - Optimal error functional for parameter identification in anisotropic finite strain elasto-plasticity
AU - Shutov, A. V.
AU - Kaygorodtseva, A. A.
AU - Dranishnikov, N. S.
PY - 2017/10/22
Y1 - 2017/10/22
N2 - A problem of parameter identification for a model of finite strain elasto-plasticity is discussed. The utilized phenomenological material model accounts for nonlinear isotropic and kinematic hardening; the model kinematics is described by a nested multiplicative split of the deformation gradient. A hierarchy of optimization problems is considered. First, following the standard procedure, the material parameters are identified through minimization of a certain least square error functional. Next, the focus is placed on finding optimal weighting coefficients which enter the error functional. Toward that end, a stochastic noise with systematic and non-systematic components is introduced to the available measurement results; a superordinate optimization problem seeks to minimize the sensitivity of the resulting material parameters to the introduced noise. The advantage of this approach is that no additional experiments are required; it also provides an insight into the robustness of the identification procedure. As an example, experimental data for the steel 42CrMo4 are considered and a set of weighting coefficients is found, which is optimal in a certain class.
AB - A problem of parameter identification for a model of finite strain elasto-plasticity is discussed. The utilized phenomenological material model accounts for nonlinear isotropic and kinematic hardening; the model kinematics is described by a nested multiplicative split of the deformation gradient. A hierarchy of optimization problems is considered. First, following the standard procedure, the material parameters are identified through minimization of a certain least square error functional. Next, the focus is placed on finding optimal weighting coefficients which enter the error functional. Toward that end, a stochastic noise with systematic and non-systematic components is introduced to the available measurement results; a superordinate optimization problem seeks to minimize the sensitivity of the resulting material parameters to the introduced noise. The advantage of this approach is that no additional experiments are required; it also provides an insight into the robustness of the identification procedure. As an example, experimental data for the steel 42CrMo4 are considered and a set of weighting coefficients is found, which is optimal in a certain class.
KW - INELASTIC CONSTITUTIVE MODELS
KW - VISCOPLASTICITY
KW - INTEGRATION
KW - SIMULATIONS
UR - http://www.scopus.com/inward/record.url?scp=85033216473&partnerID=8YFLogxK
U2 - 10.1088/1742-6596/894/1/012133
DO - 10.1088/1742-6596/894/1/012133
M3 - Article
AN - SCOPUS:85033216473
VL - 894
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
SN - 1742-6588
IS - 1
M1 - 012133
ER -
ID: 9699832