A virtual framework for prediction of full-field elastic response of unidirectional composites

Okereke, Michael; Akpoyomare, Ambrose I.

doi:10.1016/j.commatsci.2012.12.036

Cited by 67 publications

(36 citation statements)

References 48 publications

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“…Until recently, most 2D and 3D microstructural FE models have been created based on idealized or statistically-representative fiber arrangements (e.g. [23][24][25][26][27][28][29][30][31][32]), or based on 2D scanning-electron or optical microscopy image data (e.g. [27]).…”

Section: Introductionmentioning

confidence: 99%

Numerical reconstruction of graphite/epoxy composite microstructure based on sub-micron resolution X-ray computed tomography

Czabaj

Riccio

Whitacre

2014

Composites Science and Technology

107

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Section: Introductionmentioning

confidence: 99%

Numerical reconstruction of graphite/epoxy composite microstructure based on sub-micron resolution X-ray computed tomography

Czabaj

Riccio

Whitacre

2014

Composites Science and Technology

107

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“…The schematic representation of the VCCTL is given in Figure 35. [11]. The VL incorporated 3D RVEs of a typical UD composite with realistic spatial realization of the fibre inclusions; robust boundary conditions representative of physical tests and adequate homogenization techniques were used to bridge the scale between the macroscale validation data and microscale predictions of the 3D RVEs.…”

Section: One Of the Earliest Implementations Of A Virtual Laboratory mentioning

confidence: 99%

“…Okereke and Akpoyomare [11] developed another variant of the virtual testing scheme for prediction of a holistic set of effective elastic properties for UD composites. The authors used a computational micromechanics approach in their analysis.…”

Section: Unidirectional (Ud) Continuous Fibre Compositesmentioning

confidence: 99%

“…of many test materials arise from the stochastic distributions of the microscale. Okereke and Akpoyomare [11] used a statistical descriptor to determine the suitability, within a virtual testbed, of a numerically-generated 3D RVE of a UD composite. This and perturbation-based homogenization approaches [194] give localized stochastic effects.…”

Section: The Stochastic Of Microstructure Argumentmentioning

confidence: 99%

“…Therefore such developments have resulted in various forms of virtual laboratories that could eventually become substitutes to physical experiments under certain conditions [7]. Typical examples of virtual experiments [2,5,6] are described by the originating authors using various keywords as: computational experiments [8], virtual laboratories [9], numerical testing machines [7], virtual frameworks [10,11], immersive virtual environments [12], in-silico experiments [13], etc. In the context of this work, the authors will make interchangeable use of any of the following terms to refer to a virtual testing framework: virtual laboratory, virtual testbed, and virtual framework.…”

Section: Introductionmentioning

confidence: 99%

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Virtual testing of advanced composites, cellular materials and biomaterials: A review

Okereke

Akpoyomare

Bingley

2014

Composites Part B: Engineering

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Please cite this article as: Okereke, M.I., Akpoyomare, A.I., Bingley, M.S., Virtual testing of advanced composites, cellular materials and biomaterials: a review, Composites: Part B (2014), doi: http://dx.doi.org/10. 1016/ j.compositesb.2014.01.007 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. AbstractThis paper documents the emergence of virtual testing frameworks for prediction of the constitutive responses of engineering materials. A detailed study is presented, of the philosophy underpinning virtual testing schemes: highlighting the structure, challenges and opportunities posed by a virtual testing strategy compared with traditional laboratory experiments. The virtual testing process has been discussed from atomistic to macrostructural lengthscales of analyses. Several implementations of virtual testing frameworks for diverse categories of materials are also presented, with particular emphasis on composites, cellular materials and biomaterials (collectively described as heterogeneous systems, in this context). The robustness of virtual frameworks for prediction of the constitutive behaviour of these materials is discussed. The paper also considers the current thinking on developing virtual laboratories in relation to availability of computational resources as well as the development of multi-scale material model algorithms. In conclusion, the paper highlights the challenges facing developments of future virtual testing frameworks. This review represents a comprehensive documentation of the state of knowledge on virtual testing from microscale to macroscale length scales for heterogeneous materials across constitutive responses from elastic to damage regimes.

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Multiscale Experiments and Predictive Modeling for Failure Mitigation in Additive Manufacturing of Lattices

Utzeri,

Sasso,

Deshpande

et al. 2024

Adv Materials Technologies

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Additive Manufacturing (AM) empowers the creation of high‐performance cellular materials, underscoring the increasing need for programmable and predictable energy absorption capabilities. This study evaluates the impact of a precisely tuned fused filament fabrication (FFF) process on the energy absorption and failure characteristics of 2D‐thermoplastic lattice materials through multiscale experiments and predictive modeling. Macroscale in‐plane compression testing of both thick‐ and thin‐walled lattices, along with their µ‐CT imaging, reveal relative density‐dependent damage mechanisms and failure modes, prompting the development of a robust predictive modeling framework to capture process‐induced performance variation and damage. For lower relative density lattices, an FE model based on the extended Drucker–Prager material model, incorporating Bridgman's correction with crazing failure criteria, accurately captures the crushing response. As lattice density increases, interfacial damage along bead‐bead interfaces becomes predominant, necessitating the enrichment of the model with a microscale cohesive zone model to capture interfacial debonding. The predictive modeling introduces an enhancement factor, offering a straightforward method to assess the impact of the AM process on energy absorption performance, thereby facilitating the inverse design of FFF‐printed lattices. This approach provides a critical evaluation of how FFF processes can be optimized to achieve the highest attainable performance and mitigate failures in architected materials.

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A virtual framework for prediction of full-field elastic response of unidirectional composites

Cited by 67 publications

References 48 publications

Numerical reconstruction of graphite/epoxy composite microstructure based on sub-micron resolution X-ray computed tomography

Numerical reconstruction of graphite/epoxy composite microstructure based on sub-micron resolution X-ray computed tomography

Virtual testing of advanced composites, cellular materials and biomaterials: A review

Multiscale Experiments and Predictive Modeling for Failure Mitigation in Additive Manufacturing of Lattices

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