@Article{icces.2025.012395,
AUTHOR = {Xiangru He, Ying Deng, Zefu Li, Jie Zhi, Yonglin Chen, Weidong Yang, Yan Li},
TITLE = {Spatio-Temporal Prediction of Curing-Induced Deformation for Composite Structures Using a Hybrid CNN-LSTM and Finite Element Approach},
JOURNAL = {The International Conference on Computational \& Experimental Engineering and Sciences},
VOLUME = {34},
YEAR = {2025},
NUMBER = {1},
PAGES = {1--1},
URL = {http://www.techscience.com/icces/v34n1/65775},
ISSN = {1933-2815},
ABSTRACT = {Coordinated control of structural accuracy and mechanical properties is the key to composites manufacturing and the prerequisite for aerospace applications. In particular, accurate and efficient prediction of curing-induced deformation (CID) is of vital importance for fiber reinforced polymer composites quality control. In this study, we explored a novel spatio-temporal prediction model, which incorporates the finite element method with a deep learning framework to efficiently forecast the curing-induced deformation evolution of composite structures. Herein, we developed an integrated convolutional neural network (CNN) and long short-term memory (LSTM) network approach to capture both the space-distributed and time-resolved deformation from multi-parameter time series with spatial distribution. The numerical method combined with the bridging model was established to simulate deformation evolution and generate a comprehensive database. In contrast to conventional rapid prediction models that can only calculate the deformation after curing, the primary focus in developing this strategy lies in characterizing the spatio-temporal variations of warpage. The validations of composite laminates and sandwich structures with different stacking sequences demonstrate the model’s accuracy in predicting curing-induced deformation of composites. The proposed framework provides a promising approach to predict curing-induced warpage evolution for optimizing the process and precisely controlling part quality.},
DOI = {10.32604/icces.2025.012395}
}