High-fidelity computational micromechanics of first-fibre failure in unidirectional composites: Deformation mechanisms and stress concentration factors

Barzegar, Mostafa; Costa, J.; Lopes, C.S.

doi:10.1016/j.ijsolstr.2020.08.005

Cited by 7 publications

(4 citation statements)

References 27 publications

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“…This trend can be observed in figure 7(b). Like the numerical calculation result of Barzegar et al [54]; note that the SCF tends to decrease as D/d f or V f increases.…”

Section: Accumulated Data For the Scfsupporting

confidence: 77%

“…Note that the size of an RFA is 4 √ 2d f , where d f is the diameter of a fiber. This is because, according to Barzegar et al's calculation of the SCF in an RFA [54], the SCF converges to 0 if the distance between the centers of the fibers is greater than 4. The number of fibers N and fiber coordinate data (x, y) of each RFA were saved and utilized later for determination of the fiber fracture sequence in section 2.2.…”

Section: Calculation Of Scfsmentioning

confidence: 98%

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Machine learning-assisted modelling of stress concentration factor of unidirectional fiber composites for predicting their tensile strength

Choi¹,

Na²,

Yu³

2022

Modelling Simul. Mater. Sci. Eng.

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Significant variations in the tensile strength of unidirectional (UD) fiber-reinforced composites are frequently observed due to randomness in the fiber arrays. Herein, we propose a novel method for predicting tensile strength capable of quantifying uncertainty based on a new recurrence relation for fiber fracture propagation and a determination algorithm for the fracture sequence for random fiber arrays. We performed finite element simulations, calculating the stress concentration factor (SCF) for UD composites with various random fiber arrays. Then, we trained an artificial neural network with the obtained SCF data and used it to predict the SCF for composites with an arbitrary random fiber array. The tensile strength of UD composites was predicted over a range of values, demonstrating that accuracy was superior to conventional prediction methods.

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“…This trend can be observed in figure 7(b). Like the numerical calculation result of Barzegar et al [54]; note that the SCF tends to decrease as D/d f or V f increases.…”

Section: Accumulated Data For the Scfsupporting

confidence: 77%

Section: Calculation Of Scfsmentioning

confidence: 98%

Machine learning-assisted modelling of stress concentration factor of unidirectional fiber composites for predicting their tensile strength

Choi¹,

Na²,

Yu³

2022

Modelling Simul. Mater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…In addition, its current implementation likely had both a too rough mesh and insufficient time resolution to investigate stress concentrations accurately. In a recent study, Barzegar et al 28 modeled the progressive failure and recoil of a fiber using the finite element method. The SCFs were analyzed in the break plane during the fiber failure process.…”

Section: Introductionmentioning

confidence: 99%

Dynamic stress concentrations around a single fiber break in unidirectional composites: a 3D finite element analysis

Mutlu

Sabuncuoğlu

Kadıoğlu

et al. 2023

Journal of Reinforced Plastics and Composites

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When a fiber break occurs in longitudinal tension of a unidirectional composite, dynamic stress concentrations arise, which can be different from the ones found considering only static loading. The current paper analyzes the dynamic stress concentration factors (SCF) around a fiber break in unidirectional carbon fiber/epoxy composites. 3D finite element models with random and hexagonal fiber distributions were analyzed to investigate the evolution of stress concentrations as a function of time and position. The results indicate that dynamic effects result in much higher SCFs with a larger effective area around the broken fiber. The increase of SCFs in the closest fibers was determined to be larger for lower fiber volume fractions due to the presence of dynamic effects. Similar to the static case, a lower volume fraction causes higher maximum dynamic SCF in random packings. Results also support the high prevalence of coplanar cluster breaks observed in the experiments.

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“…The predictive capability of computational mechanics for heterogeneous materials largely depends on the scale at which damage is explicitly modelled [30,31]. In particular, micromechanics can be used as a reliable tool for analysis and derivation of upscaled material properties in composite materials [32,33,34,35,36,37]. Thus, with the appropriate constitutive material models and making use of unstable crack propagation virtual specimens, it is possible to better understand the mechanisms that underlie mode I through-thickness crack propagation in unidirectional fibre-reinforced composite materials.…”

Section: Introductionmentioning

confidence: 99%

Estimating the mode I through-thickness intralaminar R-curve of unidirectional carbon fibre-reinforced polymers using a micromechanics framework combined with the size effect method

Varandas

Dalli

Catalanotti

et al. 2022

Composites Part A: Applied Science and Manufacturing

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High-fidelity computational micromechanics of first-fibre failure in unidirectional composites: Deformation mechanisms and stress concentration factors

Cited by 7 publications

References 27 publications

Machine learning-assisted modelling of stress concentration factor of unidirectional fiber composites for predicting their tensile strength

Machine learning-assisted modelling of stress concentration factor of unidirectional fiber composites for predicting their tensile strength

Dynamic stress concentrations around a single fiber break in unidirectional composites: a 3D finite element analysis

Estimating the mode I through-thickness intralaminar R-curve of unidirectional carbon fibre-reinforced polymers using a micromechanics framework combined with the size effect method

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