Three-dimensional graded metamaterials with customizable thermal responses under space-variant temperature stimuli DOI
Kaiyu Wang,

Zhengtong Han,

Fan Lin

et al.

Composite Structures, Journal Year: 2024, Volume and Issue: 353, P. 118717 - 118717

Published: Nov. 16, 2024

Language: Английский

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

Jiahan Huang,

Jianquan Chen, Huanzhuo Mai

et al.

Applied Acoustics, Journal Year: 2025, Volume and Issue: 234, P. 110633 - 110633

Published: March 3, 2025

Language: Английский

Citations

2
A novel framework to predict transversal and shear parameters of unidirectional composites by combining experimental, numerical and machine learning methods DOI Creative Commons

Siqi Cheng,

Xiaoyu Wang, Yuxuan Gao

et al.

Polymer Composites, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 25, 2025

Abstract This work presents a new method to predict the transversal and shear properties of unidirectional composites (UD) through combining experimental, numerical machine learning methods. The experimental results proved complexity difficulty explaining primary factors affecting mechanical UD. representative unit cell model was then created generate 500 virtual samples for learning. show that back propagation neural network (BP) is most suitable predicting UD, with an accuracy 98% within 2% error. minimum mean square absolute errors are 1.09E‐3 1.15E‐5, respectively. It interface has significant influences on all UD modulus composite in 12 directions (G c ) affected by input parameters optimized BP model. Due wide coverage data, proposed universal can be adopted made from different kinds fibers. Highlights Interface composites. Shear along intricated. Machine Specific beneficial improve predicted accuracy.

Language: Английский

Citations

1
Design and optimization of a mechanical metamaterial featuring dual tunability in auxeticity and bandgap modulation DOI
Jiayi Hu, Zhi Gong, Yuanlong Li

et al.

Composite Structures, Journal Year: 2025, Volume and Issue: unknown, P. 119050 - 119050

Published: March 1, 2025

Language: Английский

Citations

1
Inverse design framework of hybrid honeycomb structure with high impact resistance based on active learning DOI Creative Commons

Xingyu Shen,

Ke Yan, Difeng Zhu

et al.

Defence Technology, Journal Year: 2025, Volume and Issue: unknown

Published: April 1, 2025

Language: Английский

Citations

1
Intelligent Design of Low-Frequency Bandgaps in Cementitious Metamaterials for Enhanced Tunability DOI
Zhi Gong, Jiayi Hu, Peng Dong

et al.

Thin-Walled Structures, Journal Year: 2024, Volume and Issue: unknown, P. 112860 - 112860

Published: Dec. 1, 2024

Language: Английский

Citations

3
Performance Prediction and Inverse Design of Cylindrical Plate-Type Acoustic Metamaterials Based on Deep Learning DOI

Jiahan Huang,

Jianquan Chen, Huanzhuo Mai

et al.

Published: Jan. 1, 2025

Acoustic metamaterials are artificial structures that possess distinctive acoustic characteristics, allowing for modulation effects challenging to achieve in the natural world. Nevertheless, design of is a process due intricate relationship between their structural parameters and nonlinear performance. In view limitations conventional methodologies, which rely on priori knowledge experts often hindered by prolonged computation times necessity iterative trials objectives, this paper introduces deep learning-based method performance prediction inverse Cylindrical Plate-type Metamaterials (CPAMs). The creation dataset initiated generating large number samples using parametric model, with bandgap characteristics calculated through finite element method. A forward-design learning model then developed, predicting upper lower limits based input parameters. Additionally, an constructed, enabling rapid generation desired results validated simulation experimentation, confirming accuracy reliability model. This study demonstrates potential efficiently designing complex metamaterials, offering promising solution CPAMs development.

Language: Английский

Citations

0
Inverse design of a petal-shaped honeycomb with zero Poisson’s ratio and bi-directional tunable mechanical properties DOI

Ze-Yu Chang,

Hai‐Tao Liu,

Guangbin Cai

et al.

Composite Structures, Journal Year: 2025, Volume and Issue: unknown, P. 118967 - 118967

Published: Feb. 1, 2025

Language: Английский

Citations

0
Physically Constrained 3D Diffusion for Inverse Design of Fiber-reinforced Polymer Composite Materials DOI Creative Commons
Pei Xu, Yunpeng Wu, Alireza Zarei

et al.

Composites Part B Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 112515 - 112515

Published: April 1, 2025

Language: Английский

Citations

0
Neural networks based inverse design of inhomogeneous tetrachiral honeycombs for desired deformation DOI
Linzhe Du, Jian Sun, Yanju Liu

et al.

Composite Structures, Journal Year: 2025, Volume and Issue: unknown, P. 119236 - 119236

Published: May 1, 2025

Language: Английский

Citations

0
Gear-Shifting Tunable Meta-Shaft for Low-Frequency Torsional Vibration Suppression DOI
Dongxian Wang,

Hao Zhou,

Jianlei Zhao

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0