scholarly journals Elasto-plastic material parameter identification by inverse methods: Calculation of the sensitivity matrix

2007 ◽  
Vol 44 (13) ◽  
pp. 4329-4341 ◽  
Author(s):  
S. Cooreman ◽  
D. Lecompte ◽  
H. Sol ◽  
J. Vantomme ◽  
D. Debruyne
Keyword(s):  
Parameter Identification ◽  
Material Parameter ◽  
Plastic Material ◽  
Inverse Methods ◽  
Sensitivity Matrix ◽  
Material Parameter Identification

Optimal Measurement Placement for Material Parameter Identification of Orthotropic Composites by the Finite Element Method

2011 ◽  
Vol 94-96 ◽  
pp. 1723-1728
Author(s):  
Li Xin Huang ◽  
Xiang Wu Guo ◽  
Bo Tao Du ◽  
Xiao Jun Zhou ◽  
Yu Yin Liu
Keyword(s):  
Finite Element ◽  
Parameter Identification ◽  
Material Parameter ◽  
Information Matrix ◽  
Sensitivity Matrix ◽  
Material Parameter Identification ◽  
Optimal Measurement ◽  
System Sensitivity ◽  
Orthotropic Composites ◽  
Measurement Placement

An algorithm of the optimal measurement placement is proposed for the material parameter identification of two-dimensional orthotropic composites, which is modeled by the finite element. From the analysis of the system sensitivity matrix of the parameter identification processes using the Levenberg-Marquardt method, A-optimality criterion related with the Fisher Information Matrix (FIM) is selected for the criterion of the optimal measurement placement. Thus, the algorithm for selecting the optimal measurement placement can be constructed. A numerical example is given to demonstrate the effectiveness of the proposed algorithm. The example reveals that the measurement placement has a significant influence on the identification results.

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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