.png){kind=icon}
Return
A universal framework for finding the optimal laminate design rules using generative and explainable machine learning models
.png){kind=icon}
PRE
AI
DOI：10.1016/j.coco.2025.102548.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• The data-driven framework is very versatile and can be easily extended to complex multi-objective or non-mechanical design problems. • The laminate pattern of [θ/−θ/−θ/θ/−θ/θ/θ−θ] is a novel pattern for 8-layer homogeneous laminate design, which is found by our proposed framework. • The framework is applied to problems of the quasi-isotropic and homogeneous laminate design. The generated laminating rules are validated by the analysis of classical laminate theories.
Keywords:
data-driven framework
laminate design
multi-objective optimization
quasi-isotropic laminates
classical laminate theory
Journal
IF:
7.7
Papers:
2.7K
Citations:
1.2W
Organization
Cited Papers
Defining the Design Space for Double–Double Laminates by Considering Homogenization Criterion.png)
AIAA Journal
OAAI
AIAA JournalIF0
OAAIShareSaveOn manufacturing constraints for tow-steered composite design optimizationCOMPOSITE STRUCTURES
COMPOSITE STRUCTURESIF7.1
PREAI
ShareSaveShareSaveAccelerating the Layup Sequences Design of Composite Laminates via Theory-Guided Machine Learning ModelsPolymersOAAI
PolymersIF0
OAAIShareSaveDesigning complex architectured materials with generative adversarial networksSCIENCE ADVANCES
OAAI
SCIENCE ADVANCESIF12.5
OAAIShareSaveShareSaveShareSave
View more