Transfer learning-based layout inverse design of composite plates for anticipated thermo-mechanical field DOI

Sen Yang,

Lin-Feng Zhu,

Richard K.K. Yuen

et al.

Applied Thermal Engineering, Journal Year: 2024, Volume and Issue: 263, P. 125362 - 125362

Published: Dec. 26, 2024

Language: Английский

Rapid heat transfer simulation of composites curing process based on cGANs and MPGNNs DOI
Bo Yang, Hang Shen, Fengyang Bi

et al.

International Journal of Heat and Mass Transfer, Journal Year: 2025, Volume and Issue: 241, P. 126752 - 126752

Published: Jan. 25, 2025

Language: Английский

Citations

1
Multi-scale optimisation of variable-stiffness composites for thermal cloak DOI
Alexandre Mas, Anita Catapano, Marco Montemurro

et al.

International Journal of Mechanical Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 110035 - 110035

Published: Feb. 1, 2025

Language: Английский

Citations

1
Continuous High-Throughput Characterization of Mechanical Properties via Deep Learning DOI
Guohua Zhu,

Xueyan Hu,

Rui‐Ying Bao

et al.

International Journal of Mechanical Sciences, Journal Year: 2025, Volume and Issue: unknown, P. 110137 - 110137

Published: March 1, 2025

Language: Английский

Citations

1
Analysis of cure kinetics of CFRP composites molding process using incremental thermochemical information aggregation networks DOI
Bo Yang,

Haoping Huang,

Fengyang Bi

et al.

Composite Structures, Journal Year: 2024, Volume and Issue: 331, P. 117904 - 117904

Published: Jan. 13, 2024

Language: Английский

Citations

6
A focused review on techniques for achieving cloaking effects with metamaterials DOI

Muhammad Shaheryar Khan,

Abdul Shakoor,

Osama Fayyaz

et al.

Optik, Journal Year: 2023, Volume and Issue: 297, P. 171575 - 171575

Published: Dec. 20, 2023

Language: Английский

Citations

10
Fast prediction of curing residual stress in polymer‐matrix composites by a deep learning method DOI Creative Commons
Fan Zhang,

Haili Guo,

Weiwei Wang

et al.

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

Published: Jan. 16, 2025

Abstract Curing residual stress (CRS) is common in polymer‐matrix composites due to the anisotropic properties of materials. Finite element method (FEM), most extensively used approach for curing behavior prediction, usually complicated and time‐consuming. To achieve a fast prediction process‐induced stresses, convolutional neural network (CNN) established based on FEM. Firstly, fully coupled methodology built validated through distortions experimentally manufactured laminates. Then, it applied generate models with different stacking layers, computed serves as dataset deep learning model. Finally, construction hyperparameters are determined, good generalization performance proves high accuracy current Besides, CNN (<1 s) greatly reduces computational time compared FEM (>14 min). The supervised machine shows great potential promoting efficiency sequence designing optimization composites. Highlights A model was predict numerical by deformation unsymmetrical FEM‐CNN proposed stress. Compared FEM, this accurate efficient.

Language: Английский

Citations

0
Non-conformal thermal cloak metamaterial by continuous metal fiber embedded 3D printing DOI

Muhammad Jawad Ahmad,

Xiaoyong Tian, Xin Dai

et al.

International Journal of Heat and Mass Transfer, Journal Year: 2025, Volume and Issue: 242, P. 126796 - 126796

Published: Feb. 14, 2025

Language: Английский

Citations

0
Temperature Field Prediction of Energetic Material Solidification Process Based on Enhanced Temporal Convolutional Networks with Proportional Orthogonal Decomposition DOI

Xiaocheng Tian,

Yan He, Yufeng Li

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Mask neural network for temperature field prediction for three-dimensional thermal design of electronic devices DOI
Lanzhi Liang, Longsheng Lu, Li Huang

et al.

International Communications in Heat and Mass Transfer, Journal Year: 2025, Volume and Issue: 164, P. 108757 - 108757

Published: March 4, 2025

Language: Английский

Citations

0
An enhanced temperature field inversion model by POD-BPNN-GA method for a 3D wing with limited sensors DOI
Jiaxin Hu, Jian‐Jun Gou, Chunlin Gong

et al.

International Communications in Heat and Mass Transfer, Journal Year: 2025, Volume and Issue: 164, P. 108778 - 108778

Published: March 5, 2025

Language: Английский

Citations

0