Hybrid mechanical metamaterials: advances of multi-functional mechanical metamaterials with simultaneous static and dynamic properties

Heliyon, Journal Year: 2025, Volume and Issue: 11(3), P. e41985 - e41985

Published: Jan. 18, 2025

Mechanical metamaterials are architected structures with unique functionalities, such as negative Poisson's ratio and stiffness, which widely employed for absorbing energy of quasi-static impact loads, giving improved mechanical response. Acoustic/elastic metamaterials, their dynamic counterparts, rely on frequency-dependent properties microstructure elements, including mass density bulk modulus, to control the propagation waves. Although introduced significant contribution solving independently static problems, they were facing certain resistance use in real-world engineering mainly because a lack integrated systems possessing both vibration attenuation performance. Advances manufacturing processes material computational science now enable creation hybrid offering multifunctionality terms simultaneous properties, them ability controlling waves while withstanding applied loading conditions. Exploring towards this direction, review paper introduces design process multifunctional properties. We emphasize still remaining challenges how can be potentially implemented solutions.

Language: Английский

---

Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2025, Volume and Issue: 437, P. 117725 - 117725

Published: Jan. 21, 2025

Language: Английский

---

Inverse design of spinodoid structures using Bayesian optimization

Computational Mechanics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 19, 2025

Abstract Tailoring materials to achieve a desired behavior in specific applications is of significant scientific and industrial interest as design key driver innovation. Overcoming the rather slow expertise-bound traditional forward approaches trial error, inverse attracting substantial attention. Targeting property, model proposes candidate structure with property. This concept can be particularly well applied field architected their structures directly tuned. The bone-like spinodoid are class materials. They considerable thanks non-periodicity, smoothness, low-dimensional statistical description. Previous work successfully employed machine learning (ML) models for design. amount data necessary most ML poses severe obstacle broader application, especially context inelasticity. That why we propose an inverse-design approach based on Bayesian optimization operate small-data regime. Necessitating substantially less data, small initial set iteratively augmented by silico generated until targeted properties found. application elastic demonstrates framework’s potential paving way advance

Language: Английский

---

Optimizing Metamaterial Inverse Design with 3D Conditional Diffusion Model and Data Augmentation

Advanced Materials Technologies, Journal Year: 2025, Volume and Issue: unknown

Published: April 3, 2025

Abstract The inverse design of metamaterials is critical for advancing their practical applications. Although deep learning has transformed this process, challenges remain, particularly with insufficient data and less realistic, diverse generation 3D represented as voxels. To address these limitations, a augmentation technique developed based on topological perturbation introduced conditional diffusion model (3D‐CDM) to optimize metamaterial generation. This original dataset, comprising 200 voxel representations lattices triply periodic minimal surfaces, labeled effective physical properties computed using homogenization methods. dataset expanded 5000 entries the proposed technique. Training 3D‐CDM augmented significantly improved quality accuracy generated designs. successfully produces realistic targeted properties, including volume fraction, Young's modulus, thermal conductivity, outperforming existing voxel‐based generative models in terms fidelity diversity. can be further optimized extended broader range material microstructures.

Language: Английский

---

Symmetric positive definite convolutional network for surrogate modeling and optimization of modular structures

Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 154, P. 110906 - 110906

Published: April 29, 2025

Language: Английский

---