Numerical and experimental aluminum foam microstructure testing with the use of computed tomography

Miedzińska, Danuta; Niezgoda, T.; Gieleta, Roman

doi:10.1016/j.commatsci.2012.02.021

Cited by 27 publications

(14 citation statements)

References 7 publications

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“…A similar effort is necessary when a geometrical structure has to be represented. This is the case when a complete geometry of an assembly is investigated [6,7] or if a microstructure is examined, for example, for composites [8][9][10] or foams [11].…”

Section: Introductionmentioning

confidence: 99%

Identifying Elastic and Viscoelastic Material Parameters by Means of a Tikhonov Regularization

Diebels

Scheffer

Schuster

et al. 2018

Mathematical Problems in Engineering

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For studying the interaction of displacements, stresses, and acting forces for elastic and viscoelastic materials, it is of utmost importance to have a decent mathematical model available. Usually such a model consists of a coupled set of nonlinear differential equations together with appropriate boundary conditions. However, since the different material classes vary significantly with respect to their physical and mechanical behavior, the parameters which appear in these equations are unknown and therefore have to be determined before the equations can be used for further investigations or simulations. It is this very step which is addressed in this article where we consider elastic as well as viscoelastic material behavior. The idea is to compute the parameters as solutions of a minimization problem for Tikhonov functionals. Tikhonov regularization is a well-established solution technique for tackling inverse problems. On the one hand, it assures a computation that is stable with respect to noisy input data, and on the other hand, it involves desired a priori information on the solution. In this article we develop problem adapted Tikhonov functionals and prove that a Tikhonov regularization improves the accuracy especially when the underlying system is ill-conditioned.

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Section: Introductionmentioning

confidence: 99%

Identifying Elastic and Viscoelastic Material Parameters by Means of a Tikhonov Regularization

Diebels

Scheffer

Schuster

et al. 2018

Mathematical Problems in Engineering

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show abstract

“…For example, Vehyl et al [6] used CT data of M-Pore sponge and closed-cell Alporas foams to create FE models of foam. Miedzin´ska et al [7] used µ-CT data for modeling open-cell aluminum foams and observed good correlation between the numerical results obtained from their model and the experimental compressive tests. Bock and Jacobi [8] employed X-ray µ-CT to acquire the geometric data for open-cell aluminum foams.…”

Section: Introductionmentioning

confidence: 97%

CT-Based Micro-Mechanical Approach to Predict Response of Closed-Cell Porous Biomaterials to Low-Velocity Impact

et al. 2018

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Abstract:In this study, a new numerical approach based on CT-scan images and finite element (FE) method has been used to predict the mechanical behavior of closed-cell foams under impact loading. Micro-structural FE models based on CT-scan images of foam specimens (elastic-plastic material model with material constants of bulk aluminum) and macro-mechanical FE models (with crushable foam material model with material constants of foams) were constructed. Several experimental tests were also conducted to see which of the two noted (micro-or macro-) mechanical FE models can better predict the deformation and force-displacement curves of foams. Compared to the macro-structural models, the results of the micro-structural models were much closer to the corresponding experimental results. This can be explained by the fact that the micro-structural models are able to take into account the interaction of stress waves with cell walls and the complex pathways the stress waves have to go through, while the macro-structural models do not have such capabilities. Despite their high demand for computational resources, using micro-scale FE models is very beneficial when one needs to understand the failure mechanisms acting in the micro-structure of a foam in order to modify or diminish them.

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“…Liu et al [6] investigated the failure modes of the three-dimensional porous structure with the simplified model. Furthermore, with the improvement of X-ray technology, the method of a computerized tomography scan was adopted to research the internal microstructure in metal foams [7][8][9]. Ramirez et al [10] looked at the elastoplastic deformation of open-cell foams with micro-CT, which showed better consistency agreement with experimental results.…”

Section: Introductionmentioning

confidence: 99%

Interval Identification of Thermal Parameters Using Trigonometric Series Surrogate Model and Unbiased Estimation Method

Wang

Zhao

2020

Applied Sciences

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Metal-foam materials have been applied in many engineering fields in virtue of its high specific strength and desirable of thermodynamic properties. However, due to the inherent uncertainty of its attribute parameters, reliable analysis results are often ambiguous to obtain accurately. To overcome this drawback, this paper proposes a novel interval parameter identification method. Firstly, a novel modelling methodology is proposed to simulate the geometry of engineering metal foams. Subsequently, the concept of intervals is introduced to represent the uncertainty relationship between variables and responses in heat transfer systems. To improve computational efficiency, a novel augmented trigonometric series surrogate model is constructed. Moreover, unbiased estimation methods based on different probability distributions are presented to describe system measurement intervals. Then, a multi-level optimization-based identification strategy is proposed to seek the parameter interval efficiently. Eventually, an engineering heat transfer system is given to verify the feasibility of the proposed parameter identification method. This method can rapidly identify the unknown parameters of the system. The identification results demonstrate that this interval parameter identification method can quantify the uncertainty of a metal-foam structure in engineering heat transfer systems efficiently, especially for the actual case without sufficient measurements.

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Numerical and experimental aluminum foam microstructure testing with the use of computed tomography

Cited by 27 publications

References 7 publications

Identifying Elastic and Viscoelastic Material Parameters by Means of a Tikhonov Regularization

Identifying Elastic and Viscoelastic Material Parameters by Means of a Tikhonov Regularization

CT-Based Micro-Mechanical Approach to Predict Response of Closed-Cell Porous Biomaterials to Low-Velocity Impact

Interval Identification of Thermal Parameters Using Trigonometric Series Surrogate Model and Unbiased Estimation Method

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