Analysis and Evaluation of Fiber Orientation Reconstruction Methods

Breuer, Kevin; Stommel, Markus; Korte, Wolfgang

doi:10.3390/jcs3030067

Cited by 33 publications

(28 citation statements)

References 37 publications

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“…In the second step, the stiffness of the composite is composed of the stiffnesses for the individual directions of an ODF using the Voigt approach [28]. For the ODF, the method of maximum entropy [29] is used like for the creation of the RVEs (see Section 2). The neural network method is abbreviated with NN and the two-step homogenization with the Mori-Tanaka Method as MT in the following.…”

Section: Resultsmentioning

confidence: 99%

Prediction of Short Fiber Composite Properties by an Artificial Neural Network Trained on an RVE Database

Breuer

Stommel

2021

Fibers

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In this study, an artificial neural network is designed and trained to predict the elastic properties of short fiber reinforced plastics. The results of finite element simulations of three-dimensional representative volume elements are used as a data basis for the neural network. The fiber volume fraction, fiber length, matrix-phase properties, and fiber orientation are varied so that the neural network can be used within a very wide range of parameters. A comparison of the predictions of the neural network with additional finite element simulations shows that the stiffnesses of short fiber reinforced plastics can be predicted very well by the neural network. The average prediction accuracy is equal or better than by a two-step homogenization using the classical method of Mori and Tanaka. Moreover, it is shown that the training of the neural network on an extended data set works well and that particularly calculation-intensive data points can be avoided without loss of prediction quality.

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Section: Resultsmentioning

confidence: 99%

Prediction of Short Fiber Composite Properties by an Artificial Neural Network Trained on an RVE Database

Breuer

Stommel

2021

Fibers

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“…Therefore, the existing RDI model has been extended to the Arbitrary-Reconsidered-Double-Inclusion (ARDI) model, by using the complex modulus to consider the damping behavior. Afterwards, the orientation distribution function (ODF) has been reconstructed according to the maximum entropy method (MEM) [15] for a number of orientation tensors. This method allows the creation of a discrete ARDI material database.…”

Section: Methodology and Approachmentioning

confidence: 99%

“…For this, the spatial construction of an icosphere has been used. A detailed description can be found in [15]. In the next step, the effective tensors are rotated into a discrete number of directions of the icosphere, according to Equation (6):…”

Section: First Homogenization Stepmentioning

confidence: 99%

“…In case of ODF reconstruction, the entropy represents the deviation of the calculated probability. The MEM has been used due to the following advantage: The method is based on a physical approach without the necessary of a closure formulation and can reconstruct a large area of fiber orientation distribution, with respectively small error deviations compared to other reconstruction methods [15].…”

Section: Second Homogenization Stepmentioning

confidence: 99%

“…w i is the probability which the fibers in a volume element point in discrete direction p i . A detailed derivation of the corresponding equations of the MEM is given in [15].…”

Section: Second Homogenization Stepmentioning

confidence: 99%

See 2 more Smart Citations

Arbitrary-Reconsidered-Double-Inclusion (ARDI) Model to Describe the Anisotropic, Viscoelastic Stiffness and Damping of Short Fiber-Reinforced Thermoplastics

Kriwet

Stommel

2020

J. Compos. Sci.

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Current state of the art, simulation methods to determine the frequency-, temperature-and humidity-depending stiffness and damping do not show an accurate prediction of the structural dynamics of short-fiber-reinforced thermoplastics. Thus, in the current work the new developed Arbitrary-Reconsidered-Double-Inclusion (ARDI) model has been used to describe the stiffness and damping. Thereby, a homogenization equation has been used to derive the transversal-isotropic stiffness and damping tensors. By rotating and weighting these tensors using orientation distribution functions (ODF), it is possible to create a material database. A validation of the developed ARDI model was performed on bending vibration specimens under variation of the fiber direction, temperature and humidity, to investigate the structural dynamics. In general, the comparison of the results of the simulation and experiments shows a good correlation of the eigenfrequencies and the amplitudes. The main differences in the simulation can be traced back to the used modelling of the damping behavior.

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An algorithm for generating microstructures of fiber‐reinforced composites with long fibers

Schneider

2022

Numerical Meth Engineering

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We describe a sequential addition and migration (SAM) algorithm for generating microstructures of fiber‐reinforced composites with a direct control of the magnitude of curvature of the fibers. The algorithm permits to generate microstructures with fibers that are significantly longer than the edge lengths of the underlying cell. Industrially processed short and long‐fiber composites naturally feature a high volume fraction, which needs to be reflected by state‐of‐the‐art microstructure generation tools. Nowadays, it is well understood that digital twins of the microstructure of composites are essential for reliable computational multiscale methods. The original SAM algorithm was shown to reliably generate microstructures for short and straight cylindrical fibers. Digital volume images reveal, however, that the fibers in such fiber‐reinforced composites may show significant curvature, in particular for long fibers. The work at hand introduces an extension of the original SAM approach to curved fibers. More precisely, curved fibers are considered as sequences of straight fibers which are joined at their respective ends and whose level of bending is controlled by the angle between adjacent fiber segments. We discuss how to efficiently implement the novel method and how to select the crucial numerical parameters. We compare the introduced methodology to the original SAM algorithm for short fibers and demonstrate the superiority of the novel strategy for long fibers.

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Analysis and Evaluation of Fiber Orientation Reconstruction Methods

Cited by 33 publications

References 37 publications

Prediction of Short Fiber Composite Properties by an Artificial Neural Network Trained on an RVE Database

Prediction of Short Fiber Composite Properties by an Artificial Neural Network Trained on an RVE Database

Arbitrary-Reconsidered-Double-Inclusion (ARDI) Model to Describe the Anisotropic, Viscoelastic Stiffness and Damping of Short Fiber-Reinforced Thermoplastics

An algorithm for generating microstructures of fiber‐reinforced composites with long fibers

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