material parameter identification
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Materials ◽  
2022 ◽  
Vol 15 (2) ◽  
pp. 643
Author(s):  
Paul Meißner ◽  
Jens Winter ◽  
Thomas Vietor

A neural network (NN)-based method is presented in this paper which allows the identification of parameters for material cards used in Finite Element simulations. Contrary to the conventionally used computationally intensive material parameter identification (MPI) by numerical optimization with internal or commercial software, a machine learning (ML)-based method is time saving when used repeatedly. Within this article, a self-developed ML-based Python framework is presented, which offers advantages, especially in the development of structural components in early development phases. In this procedure, different machine learning methods are used and adapted to the specific MPI problem considered herein. Using the developed NN-based and the common optimization-based method with LS-OPT, the material parameters of the LS-DYNA material card MAT_187_SAMP-1 and the failure model GISSMO were exemplarily calibrated for a virtually generated test dataset. Parameters for the description of elasticity, plasticity, tension–compression asymmetry, variable plastic Poisson’s ratio (VPPR), strain rate dependency and failure were taken into account. The focus of this paper is on performing a comparative study of the two different MPI methods with varying settings (algorithms, hyperparameters, etc.). Furthermore, the applicability of the NN-based procedure for the specific usage of both material cards was investigated. The studies reveal the general applicability for the calibration of a complex material card by the example of the used MAT_187_SAMP-1.


Author(s):  
Carlos Rojas-Ulloa ◽  
Marian Valenzuela ◽  
Víctor Tuninetti ◽  
Anne-Marie Habraken

In this research, the Stewart-Cazacu micromechanics coupled damage model is extended and validated adding nucleation and coalescence models as new damage mechanisms. The Ti–6Al–4V titanium alloy is chosen as a suitable hcp ductile material to be modeled using this extended damage law. The characterization of the damage evolution in this alloy is addressed throughout a quasi-static experimental campaign. Damage characterization relies on in situ X-ray tomography data and scanning electron microscopy imaging technique. The validation procedure consists in the implementation into the finite element research software Lagamine of ULiège and in the comparison of numerical predictions and experimental results. Load–displacement curves and damage-related state variables at fracture configuration from smooth and notched bar specimens submitted to tensile tests are analyzed. The nucleation and coalescence model extensions as well as an accurate elastoplastic and damage material parameter identification for Ti–6Al–4V samples are essential features to reach a validated model. The prediction capabilities exhibited for large strains are in good agreement with experimental results, while the near-fracture strains can still be improved.


Author(s):  
Stefan Hartmann ◽  
Rose Rogin Gilbert

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.


Author(s):  
Paulina Stempin ◽  
Wojciech Sumelka

AbstractIn this study, the static bending behaviour of a size-dependent thick beam is considered including FGM (Functionally Graded Materials) effects. The presented theory is a further development and extension of the space-fractional (non-local) Euler–Bernoulli beam model (s-FEBB) to space-fractional Timoshenko beam (s-FTB) one by proper taking into account shear deformation. Furthermore, a detailed parametric study on the influence of length scale and order of fractional continua for different boundary conditions demonstrates, how the non-locality affects the static bending response of the s-FTB model. The differences in results between s-FTB and s-FEBB models are shown as well to indicate when shear deformations need to be considered. Finally, material parameter identification and validation based on the bending of SU-8 polymer microbeams confirm the effectiveness of the presented model.


2021 ◽  
Vol 91 (2) ◽  
pp. 687-712
Author(s):  
Stefan Hartmann ◽  
Rose Rogin Gilbert ◽  
Ali Kheiri Marghzar ◽  
Chris Leistner ◽  
Pranav Kumar Dileep

AbstractIn this article, several aspects of material parameter identification are addressed. We compare several methods to identify material parameters of a constitutive model for small strain, linear elastic transverse isotropy based on experimental data of specimens made from composite plates. These approaches range from identifying the five material parameters from purely analytical considerations to the fully numerical identification on the basis of finite elements and various data provided by digital image correlation (DIC). The underlying experimental tests range from purely uniaxial tensile tests with varying fiber orientation to shear and compression tests. A specific measuring instrument has been developed for the latter tests to obtain unique material parameters—motivated by the concept of local identifiability. Besides, we compare the numerical differentiation, which is the common procedure in parameter identification, with the fully analytical derivation of sensitivities within the DIC/FEM approach.


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