Digital image and volume correlation for deformation and damage characterisation of fibre-reinforced composites: A review

Holmes, John W.; Sommacal, Silvano; Das, Raj; Stachurski, Zbigniew; Compston, Paul

doi:10.1016/j.compstruct.2023.116994

Cited by 31 publications

(6 citation statements)

References 235 publications

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“…(Table 6 and 7) are not exhaustive to cover the breadth and depth of MSM for hard composites, [ 131 ] but the intent is to provide a concise overview of techniques across multiple scales. This article complements recent reviews on composite modeling by including a greater breadth of numerical models rather than focusing solely on woven fabrics, [ 132,133 ] laminated composites, [ 41 ] or a specific approach [ 18,115,116,134 ] to composite modeling. As outlined previously, presenting soft [ 135 ] and hard composite models in the same study would also encourage model transferability and innovation in modeling new emerging composites and heterogenous metamaterials that could have engineered properties from different families of composites.…”

Section: Methodsmentioning

confidence: 95%

“…[ 114 ] This technique does not always guarantee an exact match and is validated with FEA results to check correct pairs between DVC volumes. [ 115 ] As Rashidi et al [ 116 ] explain in their in‐depth review, an optimized X‐ray CT (micro‐CT) workflow of composites can assist in generating surface or volume meshes for the MSM of underlying deformation mechanisms, providing qualitative and quantitative compositional information.…”

Section: Methodsmentioning

confidence: 99%

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Multiscale Computational and Artificial Intelligence Models of Linear and Nonlinear Composites: A Review

Agarwal,

Pasupathy,

et al. 2024

Small Science

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Herein, state‐of‐the‐art multiscale modeling methods have been described. This research includes notable molecular, micro‐, meso‐, and macroscale models for hard (polymer, metal, yarn, fiber, fiber‐reinforced polymer, and polymer matrix composites) and soft (biological tissues such as brain white matter [BWM]) composite materials. These numerical models vary from molecular dynamics simulations to finite‐element (FE) analyses and machine learning/deep learning surrogate models. Constitutive material models are summarized, such as viscoelastic hyperelastic, and emerging models like fractional viscoelastic. Key challenges such as meshing, data variability and material nonlinearity‐driven uncertainty, limitations in terms of computational resources availability, model fidelity, and repeatability are outlined with state‐of‐the‐art models. Latest advancements in FE modeling involving meshless methods, hybrid ML and FE models, and nonlinear constitutive material (linear and nonlinear) models aim to provide readers with a clear outlook on futuristic trends in composite multiscale modeling research and development. The data‐driven models presented here are of varying length and time scales, developed using advanced mathematical, numerical, and huge volumes of experimental results as data for digital models. An in‐depth discussion on data‐driven models would provide researchers with the necessary tools to build real‐time composite structure monitoring and lifecycle prediction models.

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Section: Methodsmentioning

confidence: 95%

Section: Methodsmentioning

confidence: 99%

Multiscale Computational and Artificial Intelligence Models of Linear and Nonlinear Composites: A Review

Agarwal,

Pasupathy,

et al. 2024

Small Science

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“…In the experimental study, both the universal testing machine and the Aramis 2D optical structural deformation measurement system were used [37,38]. During the experimental investigations, the load applied to the composite strut and the displacement of the crosshead was registered in real time -which allowed the determination of the previously described critical load values.…”

Section: Experimental Studiesmentioning

confidence: 99%

Buckling of Compressed Thin-Walled Composite Structures with Closed Sections

Różyło,

Rosłaniec,

Kuciej

2023

Adv. Sci. Technol. Res. J.

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The purpose of the study was to conduct an experimental-numerical analysis of the critical state (buckling) of thin-walled composite structures with a square cross-section. The subject of the study was thin-walled composite structures, which were made of CFRP -carbon epoxy composite. The structures were characterized by a closed square cross-sectional shape. In the research paper, stability tests of axially compressed thin-walled struts were performed in order to determine the critical state of the structures. Experimental tests were carried out on a universal testing machine, using a system for optical measurement of deformation of structures. Numerical simulations were carried out using the finite element method (FEM). The research made it possible to evaluate the work of the structure in the buckling state from a qualitative as well as quantitative point of view. During the study, the buckling form and the corresponding critical load were determined -which quantitatively and qualitatively showed high agreement for both experimental testing and numerical simulation. The novelty of the present paper was the use of several interdisciplinary research methods (testing machine, optical deformation measurement system and numerical simulations) in order to determine the critical state of thin-walled composite structures with closed sections.

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“…Fiber-reinforced composite (FRC) materials have emerged as crucial components in advanced engineering and are widely used in various industries [1]. These include aerospace, construction, transportation, and automotive sectors, as well as the production of sports equipment and medical devices, owing to their superior strength and durability [2].…”

Section: Introductionmentioning

confidence: 99%

Modelling and optimization of fabrication parameters for multi-walled carbon nanotubes-filled GFRP composites using RSM and Rao-1 algorithm

Ch,

Punna

2024

Eng. Res. Express

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This study presents an optimized approach for fabricating multi-walled carbon nanotubes (MWCNTs) filled glass fibre reinforced polymers (GFRP) composites using hybrid optimization approach. The experimental runs performed as per Box-Behnken design of response surface methodology (RSM) by considering three fabrication parameters: MWCNT loading, sonication time (ST), and oven curing temperature (OCT), and the output response, ultimate tensile strength (UTS) is noted. Analysis of variance (ANOVA) is employed to ascertain the significance of the effects that each factor has on UTS and found fabrication variables, OCT, and combined effects of ST and OCT are most significant. Other variables, direct effects of MWCNT loading, interaction effects of all three combinations have influence on UTS. Mathematical modeling is postulated using RSM from which contour plots are drawn to illustrate both direct and interactive effects and reveal fabrication parameters have detrimental effects on UTS. The mathematical equation of UTS is then solved by Rao-1 optimization algorithm and obtained condition is: 1.0% of MWCNT loading, 97.5 minutes of sonication time, and 760C of oven curing temperature and corresponding UTS of 624 MPa. SEM analysis has also been performed to verify the distribution of MWCNTs in the GFRP and observed uniform dispersion of MWCNTs in the developed composite. A confirmatory test validates the predicted optimal fabrication condition derived from the RSM combined with Rao-1 algorithm, ensuring that the methodology has ability to enhance the UTS of MWCNTs-embedded GFRP composites.

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Digital image and volume correlation for deformation and damage characterisation of fibre-reinforced composites: A review

Cited by 31 publications

References 235 publications

Multiscale Computational and Artificial Intelligence Models of Linear and Nonlinear Composites: A Review

Multiscale Computational and Artificial Intelligence Models of Linear and Nonlinear Composites: A Review

Buckling of Compressed Thin-Walled Composite Structures with Closed Sections

Modelling and optimization of fabrication parameters for multi-walled carbon nanotubes-filled GFRP composites using RSM and Rao-1 algorithm

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