Mechanical metamaterials and beyond

Nature Communications, Год журнала: 2023, Номер 14(1)

Опубликована: Сен. 26, 2023

Mechanical metamaterials enable the creation of structural materials with unprecedented mechanical properties. However, thus far, research on has focused passive and tunability their Deep integration multifunctionality, sensing, electrical actuation, information processing, advancing data-driven designs are grand challenges in community that could lead to truly intelligent metamaterials. In this perspective, we provide an overview within beyond classical functionalities. We discuss various aspects approaches for inverse design optimization multifunctional Our aim is new roadmaps discovery next-generation active responsive can interact surrounding environment adapt conditions while inheriting all outstanding features Next, deliberate emerging specific functionalities informative scientific devices. highlight open ahead metamaterial systems at component levels transition into domain application capabilities.

Язык: Английский

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Recent progress in gradient-structured metals and alloys

Progress in Materials Science, Год журнала: 2023, Номер 140, С. 101194 - 101194

Опубликована: Сен. 29, 2023

Язык: Английский

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Hierarchical re-entrant honeycomb metamaterial for energy absorption and vibration insulation

International Journal of Mechanical Sciences, Год журнала: 2023, Номер 250, С. 108307 - 108307

Опубликована: Март 21, 2023

Язык: Английский

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Inverse Design of Energy‐Absorbing Metamaterials by Topology Optimization

Advanced Science, Год журнала: 2022, Номер 10(4)

Опубликована: Дек. 11, 2022

Compared with the forward design method through control of geometric parameters and material types, inverse based on target stress-strain curve is helpful for discovery new structures. This study proposes an optimization strategy mechanical metamaterials a genetic algorithm establishes topology energy-absorbing structures desired curves. A series structural mutation algorithms design-domain-independent mesh generation are developed to improve efficiency finite element analysis iteration. The realizes ideal structures, which verified by additive manufacturing experimental characterization. error between designed structure less than 5%, densification strain reaches 0.6. Furthermore, special attention paid passive pedestrian protection occupant protection, reasonable solution given multiplatform structure. proposed framework provides path elastic-plastic large deformation problem that unable be resolved using classical gradient or algorithms, simplifies process metamaterials.

Язык: Английский

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Rational designs of mechanical metamaterials: Formulations, architectures, tessellations and prospects

Materials Science and Engineering R Reports, Год журнала: 2023, Номер 156, С. 100755 - 100755

Опубликована: Окт. 7, 2023

Язык: Английский

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Machine Learning Aided Design and Optimization of Thermal Metamaterials

Chemical Reviews, Год журнала: 2024, Номер 124(7), С. 4258 - 4331

Опубликована: Март 28, 2024

Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) been able to predict some unprecedented thermal properties. In this review, we first elucidate methodologies underpinning discriminative and generative models, as well paradigm of optimization approaches. Then, present a series case studies showcasing application in metamaterial design. Finally, give brief discussion on challenges opportunities fast developing field. particular, review provides: (1) Optimization metamaterials using algorithms achieve specific target (2) Integration models with enhance computational efficiency. (3) Generative structural design metamaterials.

Язык: Английский

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GNNs for mechanical properties prediction of strut-based lattice structures

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 269, С. 109082 - 109082

Опубликована: Фев. 2, 2024

Язык: Английский

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Artificial neural networks for inverse design of a semi-auxetic metamaterial

Thin-Walled Structures, Год журнала: 2024, Номер 200, С. 111927 - 111927

Опубликована: Апрель 20, 2024

Язык: Английский

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AI-Based Metamaterial Design

ACS Applied Materials & Interfaces, Год журнала: 2024, Номер 16(23), С. 29547 - 29569

Опубликована: Май 29, 2024

The use of metamaterials in various devices has revolutionized applications optics, healthcare, acoustics, and power systems. Advancements these fields demand novel or superior that can demonstrate targeted control electromagnetic, mechanical, thermal properties matter. Traditional design systems methods often require manual manipulations which is time-consuming resource intensive. integration artificial intelligence (AI) optimizing metamaterial be employed to explore variant disciplines address bottlenecks design. AI-based also enable the development by parameters cannot achieved using traditional methods. application AI leveraged accelerate analysis vast data sets as well better utilize limited via generative models. This review covers transformative impact for current challenges, emerging fields, future directions, within each domain are discussed.

Язык: Английский

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Inverse design of 3D cellular materials with physics-guided machine learning

Materials & Design, Год журнала: 2023, Номер 232, С. 112103 - 112103

Опубликована: Июль 4, 2023

This paper investigates the feasibility of data-driven methods in automating engineering design process, specifically studying inverse cellular mechanical metamaterials. Traditional designing materials typically rely on trial and error or iterative optimization, which often leads to limited productivity high computational costs. While approaches have been explored for materials, many these lack robustness fail consider manufacturability generated structures. study aims develop an efficient methodology that accurately generates metamaterial while ensuring predicted To achieve this, we created a comprehensive dataset spans broad range properties by applying rotations cubic structures synthesized from nine symmetries materials. We then employ physics-guided neural network (PGNN) consisting dual networks: generator network, serves as tool, forward acts simulator. The goal is match desired anisotropic stiffness components with unit-cell parameters. results our model are analyzed using three distinct datasets demonstrate efficiency prediction accuracy compared conventional methods.

Язык: Английский

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