Identification of viscoplastic material parameters from spherical indentation data: Part I. Neural networks

2006 ◽  
Vol 21 (3) ◽  
pp. 664-676 ◽  
Author(s):  
E. Tyulyukovskiy ◽  
N. Huber
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Phenomenological Model ◽  
Numerical Solutions ◽  
Element Model ◽  
Finite Element Simulations ◽  
Initial Slope ◽  
Spherical Indentation ◽  
Viscoplastic Material ◽  
Material Parameters

In this paper, a new method for the identification of material parameters is presented. Neural networks, which are trained on the basis of finite element simulations, are used to solve the inverse problem. The material parameters to be identified are part of a viscoplasticity model that has been formulated for finite deformations and implemented in the finite element code ABAQUS. A proper multi-creep loading history was developed in a previous paper using a phenomenological model for viscoplastic spherical indentation. Now, this phenomenological model is replaced by a more realistic finite element model, which provides fast computation and numerical solutions of high accuracy at the same time. As a consequence, existing neural networks developed for the phenomenological model have been extended from a power law hardening with two material parameters to an Armstrong–Frederick hardening rule with three parameters. These are the yield stress, the initial slope of work hardening, and maximum hardening stress of the equilibrium response. In addition, elastic deformation is taken into account. The viscous part is based on a Chaboche-like overstress model, consisting of two material parameters determining velocity dependence and overstress as a function of the strain rate. The method has been verified by additional finite element simulations. Its application for various metals will be presented in Part II, [J. Mater. Res.21, 677 (2006)].

Simulation of Deep Spherical Indentation Using Eulerian Finite Element Methods

2011 ◽  
Vol 133 (2) ◽  
Author(s):  
D. Anderson ◽  
A. Warkentin ◽  
R. Bauer
Keyword(s):  
Finite Element ◽  
Finite Element Methods ◽  
Large Deformations ◽  
Radial Strain ◽  
Element Model ◽  
Spherical Indentation ◽  
Eulerian Formulation ◽  
Pile Up ◽  
Deep Indentation ◽  
General Material

Simulation of deep indentation, and the associated pile-up effects, requires a robust and accurate finite element model capable of naturally handling the large deformations present. This work successfully demonstrates that the Eulerian formulation is capable of accurately reproducing the forces and general material response of deep indentation. It was found that, in the absence of friction, sink-in dominates at indentation depths less than 1.1% of the indenter radius, there is a transition from sink-in to pile-up from 1.1% to 2.3% of the indenter radius, and pile-up is fully developed at indentation depths larger than 13.2% of the indenter radius for the 4340 steel workpiece and the 0.508 mm radius indenter presented in this work. Friction tended to marginally increase the sink-in and transition depths as well as reduce the material height at the onset of fully developed pile-up due to a reduction in the tensile radial strain directly under the indenter.

scholarly journals Biaxial estimation of biomechanical constitutive parameters of passive porcine sclera soft tissue

2021 ◽  
Author(s):  
Zwelihle Ndlovu ◽  
Dawood Desai ◽  
Thanyani Pandelani ◽  
Harry Ngwangwa ◽  
Fulufhelo Nemavhola
Keyword(s):  
Finite Element ◽  
Soft Tissue ◽  
Finite Element Model ◽  
Test Data ◽  
Element Model ◽  
Finite Element Models ◽  
Anisotropic Model ◽  
Material Models ◽  
Material Parameters ◽  
Constitutive Parameters

This study assesses the modelling capabilities of four constitutive hyperplastic material models to fit the experimental data of the porcine sclera soft tissue. It further estimates the material parameters and discusses their applicability to a finite element model by examining the statistical dispersion measured through the standard deviation. Fifteen sclera tissues were harvested from porcine’ slaughtered at an abattoir and were subjected to equi-biaxial testing. The results show that all the four material models yielded very good correlations at correlations above 96 %. The polynomial (anisotropic) model gave the best correlation of 98 %. However, the estimated material parameters varied widely from one test to another such that there would be needed to normalise the test data to avoid long optimisation processes after applying the average material parameters to finite element models. However, for application of the estimated material parameters to finite element models, there would be needed to consider normalising the test data to reduce the search region for the optimisation algorithms. Although the polynomial (anisotropic) model yielded the best correlation, it was found that the Choi-Vito had the least variation in the estimated material parameters thereby making it an easier option for application of its material parameters to a finite element model and also requiring minimum effort in the optimisation procedure. For the porcine sclera tissue, it was found that the anisotropy more influenced by the fiber-related properties than the background material matrix related properties.

Stochastic Dynamic Analysis of Structures with Spatially Uncertain Material Parameters

2014 ◽  
Vol 14 (08) ◽  
pp. 1440029 ◽  
Author(s):  
Kheirollah Sepahvand ◽  
Steffen Marburg
Keyword(s):  
Finite Element ◽  
Modal Analysis ◽  
Polynomial Chaos ◽  
Element Model ◽  
Stochastic Dynamic ◽  
Stochastic Field ◽  
Structural Dynamic ◽  
Material Parameters ◽  
Material Uncertainties ◽  
Stochastic Dynamic Analysis

This paper investigates the uncertainty quantification in structural dynamic problems with spatially random variation in material and damping parameters. Uncertain and locally varying material parameters are represented as stochastic field by means of the Karhunen–Loève (KL) expansion. The stiffness and damping properties of the structure are considered uncertain. Stochastic finite element of structural modal analysis is performed in which modal responses are represented using the generalized polynomial chaos (gPC) expansion. Knowing the KL expansions of the random parameters, the nonintrusive technique is employed on a set of random collocation points where the structure deterministic finite element model is executed to estimate the unknown coefficients of the polynomial chaos expansions. A numerical case study is presented for a cantilever beam with random Young's modulus involving spatial variation. The proportional damping constants are estimated from the experimental modal analysis. The expected value, standard deviation, and probability distribution of the random eigenfrequencies and the damping ratios are evaluated. The results show high accuracy compared to the Monte-Carlo (MC) simulations with 3000 realizations. It is also demonstrated that the eigenfrequencies and the damping ratios are equally affected from material uncertainties.

Damage quantification in foam core sandwich composites via finite element model updating and artificial neural networks

2020 ◽  
Vol 234 (21) ◽  
pp. 4288-4304
Author(s):  
Ali Mardanshahi ◽  
Masoud Mardanshahi ◽  
Ahmad Izadi
Keyword(s):  
Neural Networks ◽  
Finite Element ◽  
Artificial Neural Networks ◽  
Finite Element Model ◽  
Model Updating ◽  
Sandwich Structures ◽  
Element Model ◽  
Foam Core ◽  
Damage Quantification ◽  
Artificial Neural

The main idea of this paper is to propose a nondestructive evaluation (NDE) system for two types of damages, core cracking and skin/core debonding, in fiberglass/foam core sandwich structures based on the inverse eigensensitivity-based finite element model updating using the modal test results, and the artificial neural networks. First, the modal testing was conducted on the fabricated fiberglass/foam core sandwich specimens, in the intact and damaged states, and the natural frequencies were extracted. Finite element modeling and inverse eigensensitivity-based model updating of the intact and damaged sandwich structures were conducted and the parameters of the models were identified. Afterward, the updated finite element models were employed to generate a large dataset of the first five harmonic frequencies of the damaged sandwich structures with different damage sizes and locations. This dataset was adopted to train the machine learning models for detection, localization, and size estimation of the core cracking and skin/core debonding damages. A multilayer perceptron neural network classification model was used for detection of types of damages and also a multilayer perceptron neural network regression model was fitted to the dataset for automatically estimation of the locations and dimensions of damages. This intelligent system of damage quantification was also used to make predictions on real damaged specimens not seen by the system. The results indicated that the extracted natural frequencies from the accurate finite element model, in coordination with the experimental data, and using the artificial neural networks can provide an effective system for nondestructive evaluation of foam core sandwich structures.

Accuracy and Robustness Analysis of Geometric Finite Element Model Updating Approach for Material Parameters Identification in Transient Dynamic

2018 ◽  
Vol 16 (01) ◽  
pp. 1850084 ◽  
Author(s):  
Clément Touzeau ◽  
Benoit Magnain ◽  
Quentin Serra ◽  
Éric Florentin
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Model Updating ◽  
Numerical Test ◽  
Robustness Analysis ◽  
Element Model ◽  
Finite Element Model Updating ◽  
Transient Dynamic ◽  
Material Parameters ◽  
Material Parameters Identification

We study the accuracy and the robustness of the Geometrical Finite Element Model Updating method proposed in Touzeau et al. [Touzeau, C., Magnain, B., Emile, B., Laurent, H. and Florentin, E. (2016) “Identification in transient dynamic using a geometry-based cost function in finite element model updating method,” Finite Elements Anal. Des. 122, 49–60]. In this work, the method is applied to transient dynamic in finite transformations to identify mechanical material parameters. A stochastic approach is performed to determine accuracy and robustness. The method is illustrated on numerical test cases and compared to a classical FEMU method. Uncertainties on the loading are taken into account in the identification using an original approach.

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