Material parameter identification of unidirectional fiber-reinforced composites

Hartmann, Stefan; Gilbert, Rose Rogin; Marghzar, Ali Kheiri; Leistner, Chris; Dileep, Pranav Kumar

doi:10.1007/s00419-021-01895-4

Cited by 10 publications

(8 citation statements)

References 31 publications

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“…However, this does not mean that the material parameters are addressed by the experiments. This issue is linked to the theory of the identifiability of material parameters [10] and discussed in connection with questions of solid mechanics by Hartmann and Gilbert [36,39], Hartmann et al [38], Sewerin [91], and Hartmann and Gilbert [37].…”

Section: General Remarks On Least-square Identificationmentioning

confidence: 99%

“…The unique identification of the whole set of nine material parameters is not a straight forward task using, for example, only DIC data, see the discussion in Hartmann et al [38]. In a first step, we are interested to find out which kind of tests would be necessary to obtain unique parameters.…”

Section: Parameter Identification For the Composite (Orthotropy)mentioning

confidence: 99%

“…As discussed in Hartmann et al [38], a compression experiment with lateral displacement constraints (this implies under the assumption of a homogeneous deformation ε 11 = ε 22 = 0) might be an alternative. However, the measured lateral stresses σ 11 and σ 22 are highly uncertain due to friction in the tool and the imperfections of the samples.…”

Section: Analytical Consideration In Orthotropic Elasticitymentioning

confidence: 99%

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Parameter estimation and its influence on layered metal–composite–metal plates simulation

et al. 2022

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Laminates made of metal—glass fiber-reinforced polymers—metal layers require material parameters for each constitutive model of the constituents. First, although parameter identification for metallic materials has been frequently discussed, the stepwise identification of material parameters in terms of uncertainties of previously determined parameters is investigated by the concept of error propagation. Second, the calibration of a model of orthotropy describing the large deformation behavior of the glass fiber-reinforced kernel layer is a challenging process, especially against the background of a reliable determination of the parameters. The main problem is related to the lack of available experiments. This issue is embedded in the concept of local identifiability. Thus, the article provides experiments to determine the parameters of both the steel as well as the glass fiber-reinforced polymers. Particularly for the latter issue, $$\mu $$ μ -CT data is chosen to provide a representative volume element, where all deformation modes can be investigated. In this sense, a concept to determine all material parameters in a locally unique and reliable manner is studied. A linear error propagation concept provides the uncertainty of the resulting material parameters of the whole parameter set for the homogenized material. The entire parameter identification process is discussed thoroughly, and validation examples such as bending and deep drawing in metal forming processes, are provided to estimate the prediction accuracy. In this respect, the uncertainties found by parameter identification are applied to the prediction of finite element simulations of layered metal–composite–metal forming processes and error propagation is used to estimate the uncertainties of the simulations. In this contribution, we restrict ourselves to experiments at room temperature.

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Section: General Remarks On Least-square Identificationmentioning

confidence: 99%

Section: Parameter Identification For the Composite (Orthotropy)mentioning

confidence: 99%

Section: Analytical Consideration In Orthotropic Elasticitymentioning

confidence: 99%

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Parameter estimation and its influence on layered metal–composite–metal plates simulation

et al. 2022

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“…CLT is seen as a candidate method for the characterization of FDM parts [58][59][60][61][62]. The elastic constants are required to characterize the mechanical behavior of the thin layers of FDM processed parts, as calculated by different methods in [63][64][65][66]. Nevertheless, the CLT uses assumptions with limited validity for FDM.…”

Section: Introductionmentioning

confidence: 99%

Exploring the Role of Manufacturing Parameters on Microstructure and Mechanical Properties in Fused Deposition Modeling (FDM) Using PETG

et al. 2021

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Additive manufacturing develops rapidly, especially, fused deposition modeling (FDM) is one of the economical methods with moderate tolerances and high design flexibility. Ample studies are being undertaken for modeling the mechanical characteristics of FDM by using the Finite Element Method (FEM). Even in use of amorphous materials, FDM creates anisotropic structures effected by the chosen manufacturing parameters. In order to investigate these process-related characteristics and tailored properties of FDM structures, we prepare FDM-printed poly(ethylene terephthalate) glycol (PETG) samples with different process parameters. Mechanical and optical characterizations are carried out. We develop 2D-digital-image-correlation code with machine learning algorithm, namely K-means cluster, to analyze microstructures (contact surfaces, the changes in fiber shapes) and calculate porosity. By incorporating these characteristics, we draw CAD images. A digital twin of mechanical laboratory tests are realized by the FEM. We use computational homogenization approach for obtaining the effective properties of the FDM-related anisotropic structure. These simulations are validated by experimental characterizations. In this regard, a systematic methodology is presented for acquiring the anisotropy from the process related inner substructure (microscale) to the material response at the homogenized length scale (macroscale). We found out that the layer thickness and overlap ratio parameters significantly alter the microstructures and thereby, stiffness of the macroscale properties. Graphical Abstract

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“…Among others the Sobol sensitivity analysis can be used as a global approach, see [62] for a certain application. In contrast, local sensitivity analysis comprises the internal numerical differentiation (IND) and external numerical differentiation (END), [28,29]. Here, we follow the external numerical differentiation concept, that is, the computation of the sensitivities () by means of the central difference method,

\frac{d s (κ)}{d κ} = \sum_{i = 1}^{n_{D}} \sum_{j = 1}^{n_{κ}} \frac{Θ_{i}^{FE} (κ + Δ κ_{j} {\overset{true‾}{e}}_{j}) - Θ_{i}^{FE} (κ - Δ κ_{j} {\overset{true‾}{e}}_{j})}{2 Δ κ_{j}} e_{i} {\overset{true‾}{e}}_{j}^{T},

with the vectors

e_{i} \in ℝ^{n_{D}}

and

{\overset{true‾}{e}}_{j} \in ℝ^{n_{κ}}

, where all entries are zero except the one in row

i

j

alternatively.…”

Section: Parameter Identificationmentioning

confidence: 99%

Identification of the thermal conductivity tensor for transversely isotropic materials

Tröger

Hartmann

2022

GAMM-Mitteilungen

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The knowledge of the thermal conductivities is of particular interest for the thermo‐mechanical modeling of transversely isotropic composite materials. Hence, the identification of these material parameters by solving an inverse problem is significant, as they cannot be directly measured. In this study, a suitable experimental setup is presented, where infrared thermography is used to measure the surface temperatures of thin specimens. Further, a local identifiability concept is employed to study whether locally unique parameters can be obtained. This leads to a particular step‐wise identification concept. The parameter identification is performed applying a nonlinear least‐square approach and finite elements. In the step‐wise identification process the convection coefficient is required first, and, subsequently, the coefficients of the thermal conductivity tensor are determined. Due to the step‐wise identification, the uncertainties of previously identified parameters have to be considered in the subsequent identification steps. The resulting uncertainties are estimated using the Gaussian error propagation concept. It turns out that the thermal conductivities of transversely isotropic materials are generally identifiable from surface temperature data. Furthermore, since all uncertainties have an essential influence on the results of real numerical simulations, their error propagation should be considered in resulting boundary‐value problems. Thus, the uncertainty quantification is demonstrated by a validation experiment.

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Material parameter identification of unidirectional fiber-reinforced composites

Cited by 10 publications

References 31 publications

Parameter estimation and its influence on layered metal–composite–metal plates simulation

Parameter estimation and its influence on layered metal–composite–metal plates simulation

Exploring the Role of Manufacturing Parameters on Microstructure and Mechanical Properties in Fused Deposition Modeling (FDM) Using PETG

Identification of the thermal conductivity tensor for transversely isotropic materials

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