scholarly journals Corrigendum to “Constrained mixture modeling affects material parameter identification from planar biaxial tests” [J. Mech. Behav. Biomed. Mater. 95 (2019) 124–135]

2021 ◽  
pp. 104635
Author(s):  
Lauranne Maes ◽  
Heleen Fehervary ◽  
Julie Vastmans ◽  
S. Jamaleddin Mousavi ◽  
Stéphane Avril ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Material Parameter ◽  
Mixture Modeling ◽  
Material Parameter Identification ◽  
Biomed Mater ◽  
Constrained Mixture ◽  
Biaxial Tests

scholarly journals Constrained mixture modeling affects material parameter identification from planar biaxial tests

2019 ◽  
Vol 95 ◽  
pp. 124-135 ◽  
Author(s):  
Lauranne Maes ◽  
Heleen Fehervary ◽  
Julie Vastmans ◽  
S. Jamaleddin Mousavi ◽  
Stéphane Avril ◽  
...  
Keyword(s):  
Parameter Identification ◽  
Material Parameter ◽  
Mixture Modeling ◽  
Material Parameter Identification ◽  
Constrained Mixture ◽  
Biaxial Tests

scholarly journals Material parameter identification using finite elements with time-adaptive higher-order time integration and experimental full-field strain information

2021 ◽  
Author(s):  
Stefan Hartmann ◽  
Rose Rogin Gilbert
Keyword(s):  
Experimental Data ◽  
Finite Elements ◽  
Parameter Identification ◽  
Material Parameter ◽  
Measurement Data ◽  
Least Square ◽  
Image Correlation ◽  
Field Strain ◽  
Material Parameter Identification ◽  
Full Field

AbstractIn this article, we follow a thorough matrix presentation of material parameter identification using a least-square approach, where the model is given by non-linear finite elements, and the experimental data is provided by both force data as well as full-field strain measurement data based on digital image correlation. First, the rigorous concept of semi-discretization for the direct problem is chosen, where—in the first step—the spatial discretization yields a large system of differential-algebraic equation (DAE-system). This is solved using a time-adaptive, high-order, singly diagonally-implicit Runge–Kutta method. Second, to study the fully analytical versus fully numerical determination of the sensitivities, required in a gradient-based optimization scheme, the force determination using the Lagrange-multiplier method and the strain computation must be provided explicitly. The consideration of the strains is necessary to circumvent the influence of rigid body motions occurring in the experimental data. This is done by applying an external strain determination tool which is based on the nodal displacements of the finite element program. Third, we apply the concept of local identifiability on the entire parameter identification procedure and show its influence on the choice of the parameters of the rate-type constitutive model. As a test example, a finite strain viscoelasticity model and biaxial tensile tests applied to a rubber-like material are chosen.

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