A Transformer-based method integrated with principal component analysis for predicting stress-strain curves of CFRP laminates with random gaps
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OpenAlex topics: Mechanical Behavior of Composites · Smart Materials for Construction · Composite Structure Analysis and Optimization
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References (40)
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1. Automating aerospace composites production with fibre placement, Reinf
2. Experimental and numerical investigation of the effect of gaps on fatigue behavior of unidirectional carbon/epoxy automated fiber placement laminates10.1177/0021998316655393 ↗
3. Experimental study of the effect of automated fiber placement induced defects on performance of composite laminates10.1016/j.compositesa.2011.01.007 ↗
4. Effects of defects in automated fiber placement laminates and its correlation to automated optical inspection results10.1177/07316844221093273 ↗
5. Multiscale analysis of low-velocity impact performance and damage mechanism of CFRP double-bolted joints10.1016/j.compositesa.2026.109792 ↗
6. Experimental and numerical study on the tensile failure behavior of toughened-interlayer composite laminates with automated fiber placement (AFP) induced gap and overlap defects10.1007/s12289-020-01581-w ↗
7. Induced defect layer method to characterize the effect of fiber tow gaps for the laminates manufactured by automated fiber placement technique10.1177/00219983211031649 ↗
8. Effect of gap on strengths of automated fiber placement manufactured laminates10.1016/j.compstruct.2021.113677 ↗
9. Modeling on the effect of automated fiber placement induced gaps in curved composite laminates10.1016/j.compstruct.2023.117721 ↗
10. Modeling the effect of automated fiber placement induced gaps based on serial layer scanned point clouds10.1016/j.compstruct.2023.116929 ↗
11. Perspective: materials informatics and big data: realization of the “fourth paradigm” of science in materials science10.1063/1.4946894 ↗
12. Machine learning in materials informatics: recent applications and prospects10.1038/s41524-017-0056-5 ↗
13. Machine learning and materials informatics approaches for predicting transverse mechanical properties of unidirectional CFRP composites with microvoids10.1016/j.matdes.2022.111340 ↗
14. A deep learning convolutional neural network and multi-layer perceptron hybrid fusion model for predicting the mechanical properties of carbon fiber10.1016/j.matdes.2023.111760 ↗
15. Microstructure dependent transverse strength criterion for UD-CFRP composites via computational micromechanics and machine learning10.1016/j.compscitech.2024.110551 ↗
16. A triplet attention-enhanced deep learning approach to predict full-field stress of unidirectional CFRP composites with microvoids10.1016/j.compscitech.2025.111361 ↗
17. Integrated convolutional and graph neural networks for predicting mechanical fields in composite microstructures10.1016/j.compositesa.2024.108618 ↗
18. Deep learning approach for predicting multi-component stress fields in fiber-reinforced composites under different load paths10.1016/j.compscitech.2025.111198 ↗
19. Non-destructive strength prediction of composite laminates utilizing deep learning and the stochastic finite element methods10.1016/j.compstruct.2023.116815 ↗
20. A deep transfer learning approach to construct the allowable load space of notched composite laminates10.1016/j.compscitech.2024.110432 ↗
21. Experimental investigation and artificial intelligence-based modeling of the residual impact damage effect on the crashworthiness of braided Carbon/Kevlar tubes10.1016/j.compstruct.2020.112247 ↗
22. A novel method to predict the stiffness evolution of in-service wind turbine blades based on deep learning models10.1016/j.compstruct.2020.112702 ↗
23. Machine learning-based modelling, feature importance and Shapley additive explanations analysis of variable-stiffness composite beam structures10.1016/j.istruc.2024.106206 ↗
24. Prediction and validation of the transverse mechanical behavior of unidirectional composites considering interfacial debonding through convolutional neural networks10.1016/j.compositesb.2021.109314 ↗
25. Prediction of composite microstructure stress-strain curves using convolutional neural networks10.1016/j.matdes.2020.108509 ↗
26. Peridynamics-fueled convolutional neural network for predicting mechanical constitutive behaviors of fiber reinforced composites10.1016/j.cma.2024.117309 ↗
27. Prediction of stress-strain behavior of carbon fabric woven composites by deep neural network10.1016/j.compstruct.2023.117073 ↗
28. Development of a memory-efficient and computationally cost-effective CNN for smart waste classification
29. Continuous stiffness optimization of mobile robot in automated fiber placement10.1016/j.rcim.2024.102833 ↗
30. Deformation and fracture mechanisms of automated fiber placement pre-preg laminates under out-of-plane tensile loading10.1016/j.compstruct.2020.112948 ↗
31. 2017
32. Applicability evaluation of damage evolution models in simulating repeated low-velocity impact behavior of composite laminates10.1177/07316844241239468 ↗
33. Bottom-up stochastic multiscale model for the mechanical behavior of multidirectional composite laminates with microvoids10.1016/j.compositesa.2024.108144 ↗
34. Vaswani · 2017
35. Transformer-based deep neural networks for short-term solar power prediction in the Middle East and North Africa regions10.1016/j.engappai.2025.111848 ↗
36. Ye
37. A neural network transformer model for composite microstructure homogenization10.1016/j.engappai.2024.108622 ↗
38. A novel lightweight model combined with convolutional neural network and transformer for gearbox fault diagnosis using infrared thermal images
39. A Transformer-based neural network for automatic delamination characterization of quartz fiber-reinforced polymer curved structure using improved THz-TDS10.1016/j.compstruct.2024.118272 ↗
40. Data-driven stress and strain curves of the unidirectional composites by deep neural networks with principal component analysis and selective-data augmentation10.1016/j.compstruct.2023.116902 ↗