Curing Deformation Prediction and Compensation Methods for Large-Sized CFRP Components
The residual stresses induced by the curing process for carbon fiber-reinforced polymers (CFRPs) lead to inevitable deformation, which seriously affects manufacturing accuracy, especially for large-sized CFRP components. Furthermore, deformation may cause damage or failure of components during subsequent assembly. More attention needs to be paid to improving the curing accuracy for large-sized CFRP components. In this study, a normal direction compensation algorithm based on node deformation is proposed based on the mold profile. Firstly, a finite element model was constructed to simulate the curing process of CFRPs and validated with a small-sized test piece. The results showed the effectiveness of the simulated and compensation methods. Secondly, to meet the precision requirements of large-sized CFRP components, a neural network was used to establish a mapping between curing process parameters and curing deformation, and the parameters were then optimized with a genetic algorithm for subsequent analysis. Finaly, a 15 m CFRP component was used to explore the effect of compensation methods in reducing curing deformation of large-sized CFRP components. The verified results showed that the maximum deformation after compensation was 0.99 mm in the normal direction, which was superior to the 1.258 mm compensation in the connecting direction.
Authors
- Fuguo Li (ORCID: https://orcid.org/0000-0001-6987-5903)
- Lu Wan (ORCID: https://orcid.org/0000-0003-2563-9575)
- Tingting Cao (ORCID: https://orcid.org/0000-0003-0051-2287)
- Zhongqi Wang (ORCID: https://orcid.org/0000-0003-2985-3333)
- Yang Zhao (ORCID: https://orcid.org/0000-0001-5614-5901)
Institutions
- Northwestern Polytechnical University (CN)
- Wuhan Ship Development & Design Institute (CN)
Publication Details
- Journal
- Machines
- Published
- 2025-09-29
- DOI
- https://doi.org/10.3390/machines13100890
- Primary Topic
- Epoxy Resin Curing Processes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Natural Science Foundation of Shaanxi Province