Inverse-designed growth-based cellular metamaterials

Mechanics of Materials, Journal Year: 2023, Volume and Issue: 182, P. 104668 - 104668

Published: May 2, 2023

Advancements in machine learning have sparked significant interest designing mechanical metamaterials, i.e., materials that derive their properties from inherent microstructure rather than just constituent material. We propose a data-driven exploration of the design space growth-based cellular metamaterials based on star-shaped distances. These two-dimensional are periodically-repeating unit cells consisting material and void patterns with non-trivial geometries. Machine models exploiting large datasets then employed to inverse for tailored anisotropic stiffness. Firstly, forward model is created bypass growth homogenization process accurately predict given finite set parameters. Secondly, an used invert structure–property maps enable accurate prediction designs stiffness query. successfully demonstrate frameworks' generalization capabilities by chosen outside domain space.

Language: Английский

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Topology optimization of irregular multiscale structures with tunable responses using a virtual growth rule

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2024, Volume and Issue: 425, P. 116864 - 116864

Published: March 25, 2024

Many applications demand tunable structural responses through tailored organic microstructural distributions and spatially varied material properties. Notable progress has been made in discovering optimized designs using periodic patterns fixed phases to achieve unusual responses. To enable the capability of exploring non-periodic architectures with continuous phase design space, we propose a topology optimization methodology that leverages virtual growth rule for designing unique multiscale structures irregular architectures, while naturally ensuring manufacturability. Our approach exploits algorithm create database, delineating constitutive relations between homogeneous frequency hints building blocks responsible generating microstructures resultant homogenized elasticity tensors. We then employ neural network yield differentiable relation. Subsequently, framework is introduced optimize both macroscale layout local block distribution. Finally, generalize account heterogeneous grow yet at microscale. present four examples showcase our proposed programming several types responses, including displacement cloaking, strain energy density, global stiffness, two three dimensions. The structures, characterized by their stochastic demonstrate programmed closely match desired targets. These also ensure connectivity offer flexibility select guaranteed minimal features. Consequently, leverage such features manifest manufacturability 3D printing. computational strategy, which precisely realizes facilitates manufacturing feasibility, can be beneficial prioritize exemplifying disorderedness, non-uniformity, heterogeneity.

Language: Английский

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Modulate stress distribution with bio-inspired irregular architected materials towards optimal tissue support

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 21, 2024

Abstract Natural materials typically exhibit irregular and non-periodic architectures, endowing them with compelling functionalities such as body protection, camouflage, mechanical stress modulation. Among these functionalities, modulation is crucial for homeostasis regulation tissue remodeling. Here, we uncover the relationship between functionality irregularity of bio-inspired architected by a generative computational framework. This framework optimizes spatial distribution limited set basic building blocks uses to assemble heterogeneous, disordered microstructures. Despite being non-periodic, assembled display spatially varying properties that precisely modulate towards target values in various control regions load cases, echoing robust capability natural materials. The performance generated experimentally validated 3D printed physical samples — good agreement observed. Owing its redirect loads while keeping proper amount stimulate bone repair, demonstrate potential application stress-programmable support orthopedic femur restoration.

Language: Английский

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Wireless electronic-free mechanical metamaterial implants

Materials Today, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

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Additively manufactured biodegradable Zn metamaterials with tunable Poisson’s ratio and enhanced mechanical properties

Virtual and Physical Prototyping, Journal Year: 2025, Volume and Issue: 20(1)

Published: Feb. 6, 2025

Language: Английский

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Data-driven topology optimization of mechanical metamaterials via deep neural network and material-field function

Mechanics of Advanced Materials and Structures, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 17

Published: Feb. 12, 2025

Data-driven methods offer an innovative way to explore high-performance mechanical metamaterials, accelerating their engineering applications. However, most existing approaches use image pixel values (e.g. element densities) as input, leading the curse of dimensionality, resulting in high storage, memory demands, computational costs, and long training times. This article presents a novel lightweight data-driven approach using material field series expansion (MFSE) function deep neural network (DNN) non-iteratively design optimal metamaterials. By describing distribution with material-field instead elemental densities, number variables is significantly reduced. A multi-layer perceptron was trained map coefficients boundary conditions, principal component analysis (PCA) applied reduce output dimensions. Once trained, instantly generates topology optimization designs for optimizing bulk modulus, shear or minimizing Poisson's ratio (PR), demonstrated through numerical examples. The proposed method achieves accuracy minimal amount data. Compared density-based models, MFSE-DNN reduces time, allowing on personal PCs lower resources. not limited studied metamaterial can be further extended various metamaterials extreme specific functionalities.

Language: Английский

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Anisotropic Nature of Lightweight Wooden Metamaterials with Mechanical/Thermomechanical Multistability

Advanced Functional Materials, Journal Year: 2023, Volume and Issue: 33(51)

Published: Aug. 8, 2023

Abstract Nanoengineered wood provides a renewable structural material with 3D micro and nanoarchitectures, exhibiting many beneficial characteristics such as being lightweight in nature, mechanically strong, eco‐friendly, thermally insulation, low carbon footprint. Most nanocellulose aerogels lack sufficient mechanical strength, while nanowood involves trade‐off between strength insulation performance. Here, nanowood‐derived product mechanical/thermomechanical multistability called wooden metamaterial, which is ultrastiff yet lightweight, designed synthesized. The self‐healing behaviors of cellulose nanofibrils originally present the cell walls their combination microscale constraints are utilized to form directional porous frameworks (porosity ≥98%) encapsulated empty fiber lumen predesigned macroscopic architectures. metamaterials showing ultrahigh specific (207.7 MPa cm 3 g −1 ), anisotropy an approximate factor 4. Wooden have overcome deficiencies existing building materials advanced aerospace thermal insulators, great potential for revolutionizing architecture manufacturing industries, particularly scalable, energy‐efficient, cost‐effective.

Language: Английский

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Mechanical characterisation of novel aperiodic lattice structures

Materials & Design, Journal Year: 2023, Volume and Issue: 229, P. 111922 - 111922

Published: April 12, 2023

This paper compares the mechanical properties of a class lattice metamaterials with aesthetically-pleasing patterns that are governed by mathematics aperiodic order. They built up ordered planar rod networks and exhibit higher non-crystallographic rotational symmetries. However, they lack translational symmetry associated periodic metamaterials. We present schematics illustrating their development based on pattern-unique mathematical substitution rules exploit numerical framework from previous work to demonstrate fascinating near-isotropic properties. The structures compared well-known hexagonal respect elastic anisotropy measure proximity bulk shear moduli Hashin–Shtrikman-Walpole limits. study lends insight into cost-constrained benefits introducing additional connectivities between aperiodically-ordered point sets. results show lattices have potential yield superior ones subject rigidity underlying shapes constitute pattern. inherent ‘near-isotropy’ these structures, even uniform strut thicknesses at low fractional densities, varied orientation struts them as promising mesoscale architecture for solving complex multi-axially loaded structural optimization problems, providing inspiration this study.

Language: Английский

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Dislocation Distribution, Crystallographic Texture Evolution, and Plastic Inhomogeneity of Inconel 718 Fabricated by Laser Powder Bed Fusion

Advanced Engineering Materials, Journal Year: 2024, Volume and Issue: unknown

Published: May 6, 2024

Plastic inhomogeneity, particularly localized strain, is one of the main mechanisms responsible for failures in engineering alloys. This work studies spatial arrangement and distribution microstructure (including dislocations grains) their influence plastic inhomogeneity Inconel 718 fabricated by additive manufacturing (AM). The bidirectional scanning strategy with no interlayer rotation results highly ordered alternating arrangements coarse Goss‐like {110}<001> textured grains separated fine Cube‐like {100}<001> grains. also an overall high density geometrically necessary (GNDs) that are dense Although texture desirable isotropy dominant, it gradually weakens during deformation undesirable component (second most dominant as‐built microstructure) increases. clustered bimodal grains, textures, GND densities causes fast roughening deformation, along line row However, chessboard a lower comparatively more random crystallographic GNDs, (and much texture) remains stable throughout deformation. uniform reducing inhomogeneity.

Language: Английский

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A novel bio-inspired design method for porous structures: Variable-periodic Voronoi tessellation

Materials & Design, Journal Year: 2024, Volume and Issue: 243, P. 113055 - 113055

Published: May 31, 2024

This paper introduces a novel approach, namely Variable-Periodic Voronoi Tessellation (VPVT), for the bio-inspired design of porous structures. The method utilizes distributed points defined by variable-periodic function to generate tessellation patterns, aligning with wide diversity artificial or natural cellular In this VPVT method, truss-based architecture can be fully characterized variables, such as frequency factors, thickness factors. approach enables optimal structures both mechanical performance and functionality. varied, anisotropic cell shapes sizes provide significantly greater flexibility compared typical isotropic addition, not only micro-macro multiscale materials, but is also applicable meso-macro scale structures, constructions, biomedical implants, aircraft frameworks. work employs Surrogate-assisted Differential Evolution (SaDE) perform optimization process. Numerical examples experiments validate that proposed achieves about 51.1% 47.8% improvement in compliance damage strength, respectively, than existing studies.

Language: Английский

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