Standard

Optimal error functional for parameter identification in anisotropic finite strain elasto-plasticity. / Shutov, A. V.; Kaygorodtseva, A. A.; Dranishnikov, N. S.

в: Journal of Physics: Conference Series, Том 894, № 1, 012133, 22.10.2017.

Результаты исследований: Научные публикации в периодических изданияхстатьяРецензирование

Harvard

APA

Vancouver

Shutov AV, Kaygorodtseva AA, Dranishnikov NS. Optimal error functional for parameter identification in anisotropic finite strain elasto-plasticity. Journal of Physics: Conference Series. 2017 окт. 22;894(1):012133. doi: 10.1088/1742-6596/894/1/012133

Author

Shutov, A. V. ; Kaygorodtseva, A. A. ; Dranishnikov, N. S. / Optimal error functional for parameter identification in anisotropic finite strain elasto-plasticity. в: Journal of Physics: Conference Series. 2017 ; Том 894, № 1.

BibTeX

@article{319f420d21af49fbbf46d9ecbb75a279,
title = "Optimal error functional for parameter identification in anisotropic finite strain elasto-plasticity",
abstract = "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.",
keywords = "INELASTIC CONSTITUTIVE MODELS, VISCOPLASTICITY, INTEGRATION, SIMULATIONS",
author = "Shutov, {A. V.} and Kaygorodtseva, {A. A.} and Dranishnikov, {N. S.}",
year = "2017",
month = oct,
day = "22",
doi = "10.1088/1742-6596/894/1/012133",
language = "English",
volume = "894",
journal = "Journal of Physics: Conference Series",
issn = "1742-6588",
publisher = "IOP Publishing Ltd.",
number = "1",

}

RIS

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