Controllable inverse design of auxetic metamaterials using deep learning

Zheng, Xiaoyang; Chen, Ta‐Te; Guo, Xiaofeng; Samitsu, Sadaki; Watanabe, Ikumu

doi:10.1016/j.matdes.2021.110178

Cited by 60 publications

(56 citation statements)

References 63 publications

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“…It should be noted that when evaluating the mechanical properties of bionic bone scaffolds, it is crucial to use the elasticity matrix of the structure, because the porous scaffolds are not isotropic. Nevertheless, in most previous studies ( Dang et al, 2018 ; Zheng et al, 2021 ), the mechanical properties of scaffolds under just one or two loading scenarios are investigated and consequently their conclusions are limited to certain conditions.…”

Section: Discussionmentioning

confidence: 99%

“…In the design of porous materials, the machine learning based technique has also been widely explored in the recent years. For example, Zheng et al (2021) has managed the inverse design of auxetic metamaterials using deep learning; Gu et al (2018) has managed the design of bioinspired hierarchical composite using machine learning; a deep-learning based model was proposed by Tan et al (2020) for the efficient design of microstructural materials. The advantage of the machine learning technique is that once the machine learning model is well trained and validated, it can serve as an efficient surrogate model for generating the real-time outputs from new inputs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model

Lü

Gong²,

Yang³

et al. 2022

Front. Bioeng. Biotechnol.

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The design of bionic bone scaffolds to mimic the behaviors of native bone tissue is crucial in clinical application, but such design is very challenging due to the complex behaviors of native bone tissues. In the present study, bionic bone scaffolds with the anisotropic mechanical properties similar to those of native bone tissues were successfully designed using a novel self-learning convolutional neural network (CNN) framework. The anisotropic mechanical property of bone was first calculated from the CT images of bone tissues. The CNN model constructed was trained and validated using the predictions from the heterogonous finite element (FE) models. The CNN model was then used to design the scaffold with the elasticity matrix matched to that of the replaced bone tissues. For the comparison, the bone scaffold was also designed using the conventional method. The results showed that the mechanical properties of scaffolds designed using the CNN model are closer to those of native bone tissues. In conclusion, the self-learning CNN framework can be used to design the anisotropic bone scaffolds and has a great potential in the clinical application.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model

Lü

Gong²,

Yang³

et al. 2022

Front. Bioeng. Biotechnol.

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“…In this work, we employ an easily-to-train DNN (compared to generative neural networks 34,36,39,52 ) to predict the effective buckling strength of uniaxially compressed lattices. The trained DNN will then be used as a decider for the inverse design of nonuniformly assembled architectures.…”

Section: Nonlinear Buckling Resistance Predictionmentioning

confidence: 99%

Inverse design of truss lattice materials with superior buckling resistance

2022

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Manipulating the architecture of materials to achieve optimal combinations of properties (inverse design) has always been the dream of materials scientists and engineers. Lattices represent an efficient way to obtain lightweight yet strong materials, providing a high degree of tailorability. Despite massive research has been done on lattice architectures, the inverse design problem of complex phenomena (such as structural instability) has remained elusive. Via deep neural network and genetic algorithm, we provide a machine-learning-based approach to inverse-design non-uniformly assembled lattices. Combining basic building blocks, our approach allows us to independently control the geometry and topology of periodic and aperiodic structures. As an example, we inverse-design lattice architectures with superior buckling performance, outperforming traditional reinforced grid-like and bio-inspired lattices by ~30–90% and 10–30%, respectively. Our results provide insights into the buckling behavior of beam-based lattices, opening an avenue for possible applications in modern structures and infrastructures.

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Section: Introductionmentioning

confidence: 99%

“…In recent years, deep learning algorithms have been exploited to handle these inverse design challenges [ 11 , 41–50 ]. However, the direct generation of pixel-based representative volume elements – which take advantage of variational autoencoders and generative adversarial networks (GANs) — focuses primarily on two-dimensional geometries [ 11 , 44–47 ]. Although the inverse design of three-dimensional (3D) geometries has been successfully accomplished in some studies, the associated neural networks are always combined with additional modeling processes [ 41–43 ].…”

Section: Introductionmentioning

confidence: 99%

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

Zheng

Chen

Jiang

et al. 2023

Science and Technology of Advanced Materials

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Architected cellular materials are a class of artificial materials with cellular architecture-dependent properties. Typically, designing cellular architectures paves the way to generate architected cellular materials with specific properties. However, most previous studies have primarily focused on a forward design strategy, wherein a geometry is generated using computer-aided design modeling, and its properties are investigated experimentally or via simulations. In this study, we developed an inverse design framework for a disordered architected cellular material (Voronoi lattices) using deep learning. This inverse design framework is a three-dimensional conditional generative adversarial network (3D-CGAN) trained based on supervised learning using a dataset consisting of voxelized Voronoi lattices and their corresponding relative densities and Young’s moduli. A well-trained 3D-CGAN adopts variational sampling to generate multiple distinct Voronoi lattices with the target relative density and Young’s modulus. Consequently, the mechanical properties of the 3D-CGAN generated Voronoi lattices are validated through uniaxial compression tests and finite element simulations. The inverse design framework demonstrates potential for use in bone implants, where scaffold implants can be automatically generated with the target relative density and Young’s modulus.

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Controllable inverse design of auxetic metamaterials using deep learning

Cited by 60 publications

References 63 publications

Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model

Designing anisotropic porous bone scaffolds using a self-learning convolutional neural network model

Inverse design of truss lattice materials with superior buckling resistance

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

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