An FE–DMN method for the multiscale analysis of short fiber reinforced plastic components

Gajek, Sebastian; Schneider, Matti; Böhlke, Thomas

doi:10.1016/j.cma.2021.113952

Cited by 44 publications

(30 citation statements)

References 74 publications

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“…As an additional benefit, for generating orientations, the TOP method requires only nine (or 22 variables, depending on the highest texture coefficient rank considered) to be stored, contrasting with methods that rely on the entire experimental database. Because of this low num-ber of parameters, it is possible to fuel data driven methods [96,97]. Additionally, as experimental data is always afflicted with some degree of measurement uncertainty, investigating the influence of the texture on the overall macroscopic response might be an interesting topic, i.e., via uncertainty quantification [98,99].…”

Section: Discussionmentioning

confidence: 99%

Generating polycrystalline microstructures with prescribed tensorial texture coefficients

Kuhn

Schneider²,

Sonnweber-Ribic³

et al. 2022

Comput Mech

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This work is concerned with synthetic microstructure models of polycrystalline materials. Once a representation of the microstructure is generated, the individual grains need to be furnished with suitable crystal orientations, matching a specific crystal orientation distribution. We introduce a novel method for this task, which permits to prescribe the orientations based on tensorial Fourier coefficients. This compact representation gives rise to the texture coefficient optimization for prescribing orientations method, enabling the determination of representative orientations for digital polycrystalline microstructures. We compare the proposed method to established and dedicated algorithms in terms of the linear elastic as well as the non-linear plastic behavior of a polycrystalline material.

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Section: Discussionmentioning

confidence: 99%

Generating polycrystalline microstructures with prescribed tensorial texture coefficients

Kuhn

Schneider²,

Sonnweber-Ribic³

et al. 2022

Comput Mech

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“…For a material with K phases, Liu et al [44,45] use a K -ary tree of laminates with fixed direction of lamination and intermittent rotations. Gajek et al [46,48] showed that using K -ary trees of laminates with variable direction of lamination, but no intermittent rotations, led to similar accuracy with fewer parameters to be trained. Recently, Nguyen and Noels [47,49] have studied more general building blocks inspired by polyhedral finite element methods.…”

Section: State Of the Artmentioning

confidence: 99%

“…DMNs were shown to work for modeling interface damage [50], strain localization [51], thermomechanically coupled materials [52] and porous materials [49]. Moreover, fully coupled FE-DMN methods were realized [47,48,52,53].…”

Section: State Of the Artmentioning

confidence: 99%

Training deep material networks to reproduce creep loading of short fiber-reinforced thermoplastics with an inelastically-informed strategy

et al. 2022

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Deep material networks (DMNs) are a recent multiscale technology which enable running concurrent multiscale simulations on industrial scale with the help of powerful surrogate models for the micromechanical problem. Classically, the parameters of the DMNs are identified based on linear elastic precomputations. Once the parameters are identified, DMNs may process inelastic material models and were shown to reproduce micromechanical full-field simulations with the original microstructure to high accuracy. The work at hand was motivated by creep loading of thermoplastic components with fiber reinforcement. In this context, multiple scales appear, both in space (due to the reinforcements) and in time (short- and long-term effects). We demonstrate by computational examples that the classical training strategy based on linear elastic precomputations is not guaranteed to produce DMNs whose long-term creep response accurately matches high-fidelity computations. As a remedy, we propose an inelastically informed early stopping strategy for the offline training of the DMNs. Moreover, we introduce a novel strategy based on a surrogate material model, which shares the principal nonlinear effects with the true model but is significantly less expensive to evaluate. For the problem at hand, this strategy enables saving significant time during the parameter identification process. We demonstrate that the novel strategy provides DMNs which reliably generalize to creep loading.

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“…In decades, chopped carbon fiber (CCF) [1][2][3][4][5][6][7][8] reinforced epoxy production sound lightweight, excellent mechanical properties which have enjoyed the significant applications of automotive and national defense industry. Generally, the CCF reinforced polymer structures usually suffer a load that far away from failure intensity, and one of fiber's main functions is to transfer the load from one fiber to another.…”

Section: Introductionmentioning

confidence: 99%

A dual‐scale homogenization method to study the properties of chopped carbon fiber reinforced epoxy resin matrix composites

Zheng

Qiu

et al. 2022

Polymer Composites

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In this work, a dual‐scale homogenization (DSH) procedures including the representative volume element (RVE) and Unit cell (UC) with Halpin–Tsai models in the UC scale and space direction‐sensitive Hotelling expansion (SDSHE) models in RVE scale separately. These two methods are introduced and implemented to predict the response of effective elastic properties in chopped fiber reinforced epoxy matrix structures. A post‐processing scheme of subdomain in UC scale based on Gauss theorem is proposed in SDSHE method, which is used to extract the average strain and stress in UC three‐dimensional structure. In addition, the X‐ray computed tomography was used to determine the fourth order tensor orientation angle and the probability distribution information of chopped fiber length in testing samples. Homogenization properties in RVE scale, and inclusion volume fraction (VF), thickness of interface and orientation tensors are designed in UC scale and compared. The excellent correlations with experiments prove the effectiveness of the proposed models. Furthermore, the algorithm proposed in this paper realizes the homogenization of the two scales in the process of numerical analysis, and it will effectively take into account many problems that cannot be achieved by other algorithms, such as the path strain of the model, the thickness of the interface, and so on.

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An FE–DMN method for the multiscale analysis of short fiber reinforced plastic components

Cited by 44 publications

References 74 publications

Generating polycrystalline microstructures with prescribed tensorial texture coefficients

Generating polycrystalline microstructures with prescribed tensorial texture coefficients

Training deep material networks to reproduce creep loading of short fiber-reinforced thermoplastics with an inelastically-informed strategy

A dual‐scale homogenization method to study the properties of chopped carbon fiber reinforced epoxy resin matrix composites

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