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Spatio-Temporal Prediction of Curing-Induced Deformation for Composite Structures Using a Hybrid CNN-LSTM and Finite Element Approach

Xiangru He¹, Ying Deng¹, Zefu Li¹, Jie Zhi^1,2, Yonglin Chen^1,2, Weidong Yang^1,2,3,*, Yan Li^1,2,3,*

1 School of Aerospace Engineering and Applied Mechanics, Tongji University, Shanghai, 200092, China
2 Shanghai Institute of Aircraft Mechanics and Control, Shanghai, 200092, China
3 Key Laboratory of AI-aided Airworthiness of Civil Aircraft Structures, Shanghai, 200092, China

* Corresponding Authors: Weidong Yang, Email: ; Yan Li. Email:

The International Conference on Computational & Experimental Engineering and Sciences 2025, 34(1), 1-1. https://doi.org/10.32604/icces.2025.012395

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.

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Keywords

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