A design framework for gradually stiffer mechanical metamaterial induced by negative Poisson's ratio property

Ye, Mengli; Gao, Liang; Li, Hao

doi:10.1016/j.matdes.2020.108751

Cited by 74 publications

(37 citation statements)

References 48 publications

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“…Mechanical metamaterials are unique structures whose properties are determined not by their material composition as conventional structures but by the geometric configuration of the microstructure units [1][2][3]. Due to its unconventional mechanical properties, for instance, negative Poisson's ratio, negative effective mass, negative modulus, chirality, et al, it has broad application prospects [4][5][6][7][8]. The empirical method is the mainstream method of designing such mechanical metamaterials.…”

Section: Introductionmentioning

confidence: 99%

Stacked-origami mechanical metamaterial with tailored multistage stiffness

Wen

Chen

Long

et al. 2021

Preprint

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Origami-baed metamaterial has shown remarkable mechanical properties rarely found in natural materials, but achieving tailored multistage stiffness is still a challenge. This study proposes a novel zigzag-base stacked-origami (ZBSO) metamaterial with tailored multistage stiffness property based on crease customization and stacking strategies. A high precision finite element (FE) model to identify the stiffness characteristics of the ZBSO metamaterial has been established, and its accuracy is validated by quasi-static compression experiments. Using the verified FE model, we demonstrate that the multistage stiffness of the ZBSO metamaterial can be effectively tailored through two manners, i.e. varying the microstructures (through introducing new creases to the classical Miura origami unit cell) and altering the stacking way. Three strategies are utilized to vary the microstructure, i.e. adding new creases to the right, left, or both sides of the unit cell. We further reveal that the proposed ZBSO metamaterial has several outstanding advantages compared with traditional mechanical metamaterials, e.g. material independent, scale-invariant, lightweight, and excellent energy absorption capacity. The unravelled superior mechanical properties of the ZBSO metamaterials pave the way for the design of the next-generation cellular metamaterials with tailored stiffness properties.

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Section: Introductionmentioning

confidence: 99%

Stacked-origami mechanical metamaterial with tailored multistage stiffness

Wen

Chen

Long

et al. 2021

Preprint

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“…Typical large-scale objects include those in architecture and aerospace [19,22,23]. Bionic bones [24], convection radiators [25], and mechanical metamaterials [26] are examples of objects that require high-precision design. Topology optimization is an excellent design tool in engineering, but it is only suitable for the design of normal-sized structures.…”

Section: Introductionmentioning

confidence: 99%

Efficient, high-resolution topology optimization method based on convolutional neural networks

Liu

Wen

et al. 2021

Front. Mech. Eng.

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Topology optimization is a pioneer design method that can provide various candidates with high mechanical properties. However, high resolution is desired for optimum structures, but it normally leads to a computationally intractable puzzle, especially for the solid isotropic material with penalization (SIMP) method. In this study, an efficient, high-resolution topology optimization method is developed based on the superresolution convolutional neural network (SRCNN) technique in the framework of SIMP. SRCNN involves four processes, namely, refinement, path extraction and representation, nonlinear mapping, and image reconstruction. High computational efficiency is achieved with a pooling strategy that can balance the number of finite element analyses and the output mesh in the optimization process. A combined treatment method that uses 2D SRCNN is built as another speed-up strategy to reduce the high computational cost and memory requirements for 3D topology optimization problems. Typical examples show that the high-resolution topology optimization method using SRCNN demonstrates excellent applicability and high efficiency when used for 2D and 3D problems with arbitrary boundary conditions, any design domain shape, and varied load.

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“…Zheng et al 23 proposed two numerical schemes within the evolutionary optimization framework to obtain 2D auxetic metamaterials with favourable characteristics, and experimentally validated their special properties. Ye et al 24 proposed a method for the simultaneous adaptive design of gradually stiffer mechanical metamaterials along with auxetic property.…”

Section: Introductionmentioning

confidence: 99%

Robust topological designs for extreme metamaterial micro-structures

Chatterjee

Chakraborty

Goswami

et al. 2021

Sci Rep

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We demonstrate that the consideration of material uncertainty can dramatically impact the optimal topological micro-structural configuration of mechanical metamaterials. The robust optimization problem is formulated in such a way that it facilitates the emergence of extreme mechanical properties of metamaterials. The algorithm is based on the bi-directional evolutionary topology optimization and energy-based homogenization approach. To simulate additive manufacturing uncertainty, combinations of spatial variation of the elastic modulus and/or, parametric variation of the Poisson’s ratio at the unit cell level are considered. Computationally parallel Monte Carlo simulations are performed to quantify the effect of input material uncertainty to the mechanical properties of interest. Results are shown for four configurations of extreme mechanical properties: (1) maximum bulk modulus (2) maximum shear modulus (3) minimum negative Poisson’s ratio (auxetic metamaterial) and (4) maximum equivalent elastic modulus. The study illustrates the importance of considering uncertainty for topology optimization of metamaterials with extreme mechanical performance. The results reveal that robust design leads to improvement in terms of (1) optimal mean performance (2) least sensitive design, and (3) elastic properties of the metamaterials compared to the corresponding deterministic design. Many interesting topological patterns have been obtained for guiding the extreme material robust design.

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A design framework for gradually stiffer mechanical metamaterial induced by negative Poisson's ratio property

Cited by 74 publications

References 48 publications

Stacked-origami mechanical metamaterial with tailored multistage stiffness

Stacked-origami mechanical metamaterial with tailored multistage stiffness

Efficient, high-resolution topology optimization method based on convolutional neural networks

Robust topological designs for extreme metamaterial micro-structures

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