Composite Structures, Год журнала: 2024, Номер 353, С. 118717 - 118717
Опубликована: Ноя. 16, 2024
Язык: Английский
Composite Structures, Год журнала: 2024, Номер 353, С. 118717 - 118717
Опубликована: Ноя. 16, 2024
Язык: Английский
Applied Acoustics, Год журнала: 2025, Номер 234, С. 110633 - 110633
Опубликована: Март 3, 2025
Язык: Английский
Процитировано
2Polymer Composites, Год журнала: 2025, Номер unknown
Опубликована: Янв. 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.
Язык: Английский
Процитировано
1Composite Structures, Год журнала: 2025, Номер unknown, С. 119050 - 119050
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
1Defence Technology, Год журнала: 2025, Номер unknown
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
1Thin-Walled Structures, Год журнала: 2024, Номер unknown, С. 112860 - 112860
Опубликована: Дек. 1, 2024
Язык: Английский
Процитировано
3Опубликована: Янв. 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.
Язык: Английский
Процитировано
0Composite Structures, Год журнала: 2025, Номер unknown, С. 118967 - 118967
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0Composites Part B Engineering, Год журнала: 2025, Номер unknown, С. 112515 - 112515
Опубликована: Апрель 1, 2025
Язык: Английский
Процитировано
0Composite Structures, Год журнала: 2025, Номер unknown, С. 119236 - 119236
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0