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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Design and compression-induced bandgap evolution of novel polygonal negative stiffness metamaterials

International Journal of Mechanical Sciences, Год журнала: 2023, Номер 261, С. 108658 - 108658

Опубликована: Авг. 5, 2023

Язык: Английский

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Perspective: Machine Learning in Design for 3D/4D Printing

Journal of Applied Mechanics, Год журнала: 2023, Номер 91(3)

Опубликована: Окт. 5, 2023

Abstract 3D/4D printing offers significant flexibility in manufacturing complex structures with a diverse range of mechanical responses, while also posing critical needs tackling challenging inverse design problems. The rapidly developing machine learning (ML) approach new opportunities and has attracted interest the field. In this perspective paper, we highlight recent advancements utilizing ML for designing printed desired responses. First, provide an overview common forward problems, relevant types structures, space responses printing. Second, review works that have employed variety approaches different ranging from structural properties to active shape changes. Finally, briefly discuss main challenges, summarize existing potential approaches, extend discussion broader problems field This paper is expected foundational guides insights into application design.

Язык: Английский

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Analytical relationships for 2D Re-entrant auxetic metamaterials: An application to 3D printing flexible implants

Journal of the mechanical behavior of biomedical materials/Journal of mechanical behavior of biomedical materials, Год журнала: 2023, Номер 143, С. 105938 - 105938

Опубликована: Май 25, 2023

Both 2D and 3D re-entrant designs are among the well-known prevalent auxetic structures exhibiting negative Poisson's ratio. The present study introduces novel analytical relationships for hexagonal honeycombs both positive ranges of cell interior angle θ (θ<0 showing a ratio). derived solutions validated against finite element method (FEM) experimental results. results show that, compared to available in literature, presented this provide most accurate elastic modulus, ratio, yield stress. analytical/computational tools then implemented designing Kinesio taping (KT) applicable treatment Achilles tendon injuries. One main features is natural behavior. ratio distribution an obtained using longitudinal transverse strains used design print thermoplastic polyurethane (TPU) KT with non-uniform unit cells. shows that it capable replicating deformation global local distributions, similar those tendon. Due absence formulations procedures expected be instrumental printing flexible implants unusual auxeticity.

Язык: Английский

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Inverse machine learning framework for optimizing gradient honeycomb structure under impact loading

Engineering Structures, Год журнала: 2024, Номер 309, С. 118079 - 118079

Опубликована: Апрель 27, 2024

Язык: Английский

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Inverse design of Bézier curve-based mechanical metamaterials with programmable negative thermal expansion and negative Poisson's ratio via a data augmented deep autoencoder

Materials Horizons, Год журнала: 2024, Номер 11(11), С. 2615 - 2627

Опубликована: Янв. 1, 2024

We introduce a novel deep learning-based inverse design framework with data augmentation for chiral mechanical metamaterials Bézier curve-shaped bi-material rib realizing wide range of negative thermal expansion and Poisson's ratio.

Язык: Английский

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Mechanical properties of 3D continuous CFRP composite graded auxetic structures

Construction and Building Materials, Год журнала: 2024, Номер 440, С. 137379 - 137379

Опубликована: Июль 13, 2024

Язык: Английский

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High Energy Absorption Design of Porous Metals Using Deep Learning

International Journal of Mechanical Sciences, Год журнала: 2024, Номер 282, С. 109593 - 109593

Опубликована: Июль 27, 2024

Язык: Английский

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Machine learning in additive manufacturing: enhancing design, manufacturing and performance prediction intelligence

Journal of Intelligent Manufacturing, Год журнала: 2025, Номер unknown

Опубликована: Янв. 27, 2025

Язык: Английский

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Performance prediction and inverse design of cylindrical plate-type acoustic metamaterials based on deep learning

Applied Acoustics, Год журнала: 2025, Номер 234, С. 110633 - 110633

Опубликована: Март 3, 2025

Язык: Английский

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