Efficient generator of random fiber distribution with diverse volume fractions by random fiber removal

Park, Shin-Mu; Lim, Jae Hyuk; Seong, Myeong Ryun; Sohn, Dongwoo

doi:10.1016/j.compositesb.2018.12.042

Cited by 52 publications

(26 citation statements)

References 27 publications

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“…RVE modelling has been widely used in the study of twodimensional (2D) and three-dimensional (3D) woven composites [31][32][33][34][35] and also in multi-scale modelling of unidirectional (UD) composites. [36][37][38][39][40][41] Totry et al 42 used a 2D RVE model to predict the failure of a composite laminate under transverse compression and out-of-plane shear loading, while Sun et al 43 used a 3D RVE model to predict the failure of a composite laminate under combined transverse compression and out-of-plane/in-plane shear bi-axial loading. Several studies have typically used RVE modelling to obtain effective material properties from a representative unit cell.…”

Section: Representative Volume Element Modellingmentioning

confidence: 99%

Microscale modelling of lightning damage in fibre-reinforced composites

Millen

Lee

2023

Journal of Composite Materials

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In this work, three-dimensional (3D) finite element simulations were undertaken to study the effects of lightning strikes on the microscale behaviour of continuous fibre-reinforced composite materials and to predict and understand complex lightning damage mechanisms. This approach is different from the conventional mesoscale or macroscale level of analysis, that predicts the overall lightning damage in composite laminates, thus providing better understanding of lightning-induced thermo-mechanical damage at a fundamental level. Micromechanical representative volume element (RVE) models of a UD composite laminate were created with circular carbon fibres randomly distributed in an epoxy matrix. The effects of various grounding conditions (one-, two-, and four-side grounding), fibre volume fractions ( V f = 55, 60%, and 65%), and peak current amplitudes (10, 20, and 40 kA) on microscopic damage in RVE models were characterised to understand fibre, epoxy matrix, and fibre-matrix interfacial damage associated with a lightning strike. Thermal damage, estimated based on epoxy matrix decomposition temperature, was largely constant up to 20 kA before increasing significantly at 40 kA. Severe thermal damage steadily decreased with increasing V f. Thermo-mechanical damage was predicted using a ductile plasticity model with Drucker–Prager yield criterion for epoxy matrix failure, and cohesive surfaces for fibre-matrix interface debonding. Thermal strain had the largest contribution to thermo-mechanical damage, while dynamic pressure loading was negligible in all RVE models. The RVE model proposed in this work, to the best of the authors’ knowledge, is the first model predicting lightning-induced fibre-matrix interfacial damage.

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Section: Representative Volume Element Modellingmentioning

confidence: 99%

Microscale modelling of lightning damage in fibre-reinforced composites

Millen

Lee

2023

Journal of Composite Materials

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“…If the gðrÞ of RDAFF_AFSA is closer to 1, the distribution of points exhibits a higher degree of randomness. 18,21 Figure 10(b) shows the pair distribution function curves for RDAFF_AFSA, CSR, and the experimental data.…”

Section: Pair Distribution Functionmentioning

confidence: 99%

Generation of unidirectional fiber random distribution structures based on an artificial fish swarm algorithm with random deletion after fiber filling

Liu

Huang

et al. 2023

Journal of Composite Materials

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This study proposes a random distribution method for generating high-content volume fractions of fibers in their cross-sectional area. This approach is referred to as the artificial fish swarm algorithm (AFSA) with random deletion after fiber filling (RDAFF_AFSA). Initial fibers were first generated using AFSA, followed by fiber filling the matrix-rich region with a hard-core model. The desired representative volume element (RVE) was ultimately obtained by random deletion. The proposed method can generate RVEs with high-content volume fractions (up to 67%) of fibers. The generated RVEs were statistically analyzed and compared with the completely spatial random (CSR) pattern and experiment data. The results showed that RDAFF_AFSA exhibited a high degree of consistency with the CSR and experimental data. The elastic constants of the carbon fiber-reinforced plastic composites were predicted by finite element analysis. The predicted results are very reasonable compared to the experimental results. The proposed method can provide a highly valuable alternative for micromechanical and multiscale analyses of unidirectional fiber-reinforced composites.

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“…According to the literature, different techniques were adopted to verify the accuracy of volumes that are equivalent to the whole non-periodic domain. Typically, investigations about the effect of the geometric characteristics of a representative volume on the resulting homogenized elastic properties were carried out [ 28 , 29 , 39 , 40 , 43 , 46 , 47 , 48 , 51 , 52 , 55 , 56 , 62 ]. From this point of view, it can be realized that the statistically equivalent volumes herein determined shall also be representative in terms of homogenized elastic properties.…”

Section: Homogenized Propertiesmentioning

confidence: 99%

“…To improve the prediction of the mechanical behavior of heterogeneous materials, recent studies have presented methodologies to create sub-volumes with random microstructures which attempt to capture the effects of fiber arrangement on the homogenized properties, and also on the distribution of the stresses at microscopic level [ 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 ]. These approaches are very interesting from the point of view of more realistic representations when compared to classical periodic fiber arrangement.…”

Section: Introductionmentioning

confidence: 99%

Identification of Representative Equivalent Volumes on the Microstructure of 3D-Printed Fiber-Reinforced Thermoplastics Based on Statistical Characterization

et al. 2022

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The present work describes a methodology to compute equivalent volumes representing the microstructure of 3D-printed continuous fiber-reinforced thermoplastics, based on a statistical characterization of the fiber distribution. In contrast to recent work, the methodology herein presented determines the statistically equivalent fiber distribution directly from cross-section micrographs, instead of generating random fiber arrangements. For this purpose, several regions, with different sizes and from different locations, are cropped from main cross-section micrographs and different spatial descriptor functions are adopted to characterize the microstructures in terms of agglomeration and periodicity of the fibers. Detailed information about the adopted spatial descriptors and the algorithm implemented to identify the fiber distribution, as well as to define the location of cropped regions, are given. From the obtained statistical characterization results, the minimum size of the equivalent volume required to be representative of the fiber distribution, which is found in the cross-section micrographs of 3D-printed composite materials, is presented. To support the findings, as well as to demonstrate the effectiveness of the proposed methodology, the homogenized properties are also computed using representative equivalent volumes obtained in the statistical characterization and the results are compared to those experimentally measured, which are available in the literature.

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Efficient generator of random fiber distribution with diverse volume fractions by random fiber removal

Cited by 52 publications

References 27 publications

Microscale modelling of lightning damage in fibre-reinforced composites

Microscale modelling of lightning damage in fibre-reinforced composites

Generation of unidirectional fiber random distribution structures based on an artificial fish swarm algorithm with random deletion after fiber filling

Identification of Representative Equivalent Volumes on the Microstructure of 3D-Printed Fiber-Reinforced Thermoplastics Based on Statistical Characterization

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