Twist, tilt and stretch: From isometric Kelvin cells to anisotropic cellular materials

Mao, Huina; Rumpler, Romain; Gaborit, Mathieu; Göransson, Peter; Kennedy, John; O'Connor, Daragh; Trimble, Daniel; Rice, Henry J.

doi:10.1016/j.matdes.2020.108855

Cited by 14 publications

(4 citation statements)

References 43 publications

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“…The physical properties of the resin are taken as a Poisson's ratio of 0.35 and volumetric mass density of 1180 kilograms per cubic meter [20]. Young's modulus is measured by printing a cantilevered beam of known dimensions and observing its frequency response.…”

Section: Methodsmentioning

confidence: 99%

Toward a Bio-Inspired Acoustic Sensor: Achroia grisella’s Ear

et al. 2022

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Bio-inspiration looks to nature to overcome challenges innovatively. Insects show many examples of efficient approaches to hearing considering the small sizes of their bodies. The nocturnal moth Achroia grisella is capable of directional hearing of wavelengths several times larger than the separation between its tympana. Directionality in this moth seems to be monoaural and dependent exclusively on morphology, so a model is developed to replicate the structure. We start from a simple circular model, progressively incorporating more complex elements to improve the resemblance to the natural system. The goal is to develop a model inspired by Achroia's ear, that behaves similarly to it, and to 3D print devices that agree with the model. Equations, simulations, and 3D printed devices measured through Laser Doppler Vibrometry are compared.

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Section: Methodsmentioning

confidence: 99%

Toward a Bio-Inspired Acoustic Sensor: Achroia grisella’s Ear

et al. 2022

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“…Tessellated computer-aided design (CAD) data files often contain flaws such as overlaps, gaps or degenerate facets and may require correction in an additional repair software [5]. This is a significant issue since many new acoustic materials require accurate manufacture of features at the sub-millimetre scale [6,7].…”

Section: Introductionmentioning

confidence: 99%

The Impact of Additive Manufacturing on the Acoustic Performance of Novel Porous Materials

Ciochon

Kennedy

Leiba

et al. 2022

28th AIAA/CEAS Aeroacoustics 2022 Conference

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Commonly used conventional acoustic porous materials suffer from a limited operational frequency range and great dependency of the sample thickness on their performance. For this reason, acoustic metamaterials have caught the eye of the scientific community with their ability to manipulate and absorb soundwaves despite their subwavelength dimensions. These novel acoustic materials are especially of interest for aerospace and automotive industries. Industrially relevant design tools are required to unlock the potential of these materials and the development of these tools requires benchmark problems. This work analyzes how the additive manufacturing process influences the acoustic performance of a periodic porous acoustic material. To answer this question, samples of a benchmark material were fabricated using selective laser melting with a 0.03 mm layer height. An optical microscope, a confocal microscope and computerized tomography scanner were used to examine manufactured specimens and provide insight into their surface topology. Numerical models of the structure were created. The computational results were compared with the experimental values. A combination of modelling strategies are investigated to incorporate features of the additive manufacture in the prediction of the acoustic behaviour. The observed mismatch between them can only partially be explained by the presence of the surface roughness. This study emphasizes the need to take into account more aspects of the additive manufacturing process when designing acoustic materials.

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“…Prior research has identified a myriad of architectures with tailored anisotropy-behaving stiff in some directions and compliant in others. Most popular for its simple fabrication and property extraction has been the class of truss lattices (13,(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28). In principle, arbitrary thermodynamically admissible stiffness combinations can be achieved by the complex arrangement of trusses (20), which may be guided by topology optimization schemes (also referred to as inverse homogenization) (13).…”

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confidence: 99%

“…Consequently, existing works ( 23 , 25 , 30 , 41 ) have typically considered only a small number of fixed lattice topologies, whose superposition with different strut thicknesses and/or base materials results in a limited design space for the effective properties—but with the added benefit of enabling spatial gradients. In addition, the common focus on cubic and hence orthotropic UCs ( 26 , 35 ) ignores shear–normal and shear–shear coupling components in the effective stiffness tensor—although it has been recognized that those may be beneficial for, among others, compliance minimization and wave guidance ( 22 , 42 ). By contrast, a completely random topology (based on a random placement and connection of struts in a truss) results in an overwhelmingly high-dimensional and nonlinear parameterization with low symmetry and a prohibitively small fraction of mechanically useful UCs (not even to think of smooth spatial transitions between different random topologies).…”

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confidence: 99%

Inverting the structure–property map of truss metamaterials by deep learning

Bastek

Kumar

Telgen

et al. 2021

Proc. Natl. Acad. Sci. U.S.A.

105

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Inspired by crystallography, the periodic assembly of trusses into architected materials has enjoyed popularity for more than a decade and produced countless cellular structures with beneficial mechanical properties. Despite the successful and steady enrichment of the truss design space, the inverse design has remained a challenge: While predicting effective truss properties is now commonplace, efficiently identifying architectures that have homogeneous or spatially varying target properties has remained a roadblock to applications from lightweight structures to biomimetic implants. To overcome this gap, we propose a deep-learning framework, which combines neural networks with enforced physical constraints, to predict truss architectures with fully tailored anisotropic stiffness. Trained on millions of unit cells, it covers an enormous design space of topologically distinct truss lattices and accurately identifies architectures matching previously unseen stiffness responses. We demonstrate the application to patient-specific bone implants matching clinical stiffness data, and we discuss the extension to spatially graded cellular structures with locally optimal properties.

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Twist, tilt and stretch: From isometric Kelvin cells to anisotropic cellular materials

Cited by 14 publications

References 43 publications

Toward a Bio-Inspired Acoustic Sensor: Achroia grisella’s Ear

Toward a Bio-Inspired Acoustic Sensor: Achroia grisella’s Ear

The Impact of Additive Manufacturing on the Acoustic Performance of Novel Porous Materials

Inverting the structure–property map of truss metamaterials by deep learning

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