Computationally efficient multi-field simulation of the curing process of thick composite parts

The use of composite materials in the industrial and aerospace sectors has led to a growing interest in studying the curing process and its effect on the final performance of composite parts. The inherent complexity of this process, combined with stringent manufacturing requirements, makes numerical simulation an essential tool for predicting composite materials' behavior and optimizing the manufacturing process while minimizing the risk of unacceptable defects. Multi-field simulations allow the simultaneous analysis of the variables involved during the curing process and the evaluation of the impact of varying process parameters on these variables. Specifically, this work solves the coupled thermochemical model using a refined 1D model based on Carrera Unified Formulation (CUF) and finite elements. This approach provides an accurate, detailed analysis of the process in terms of the composite part's temperature and degree of cure. A strength of this model is its ability to provide a three-dimensional representation of the part, which traditional 1D models cannot do while maintaining a low computational cost. This approach makes it possible to evaluate the effects of different boundary conditions on part surfaces and to consider complex cases such as thick sandwiches with the presence of tools. The layer-wise approach also allows capturing the effects of stacking sequences, material heterogeneities, and boundary conditions on the properties of the composite. The results of this study demonstrate the accuracy of the 1D FE/CUF approach in simulating the curing behavior of composite plates. Detailed analyses are conducted on plates subjected to different surface boundary conditions, focusing on convection effects. The present model successfully predicts temperature gradients and degree of cure distributions across the thickness and throughout the entire component. Additionally, the method's applicability to more complex cases, such as thick sandwich configurations, is demonstrated by including the effect of the tool to ensure more realistic simulations. Finally, comparative analyses of different laminate stacking sequences reveal the influence of these configuration changes on thermal and chemical behavior during curing. As a prospect, the coupled CUF-based approach can be combined with Artificial Intelligence to optimize the curing process. This integration would enable the rapid determination of optimal curing cycles, lamination configurations, and geometries to ensure the efficient production of high-quality composite components.