Spatiotemporal prediction of surface roughness evolution of C/C composites based on recurrent neural network

Carbon/carbon (C/C) composites are widely used in aerospace applications due to their excellent properties. Their surface roughness is an important factor affecting the material properties and ablation resistance, which is essential for the accurate prediction of the material. Images are often used to summarize and characterize microstructural features, which include information such as fiber microstructure and fiber orientations. An ablation shape evolution model based on optimization of attention mechanism, which combines multi-scale convolutional neural network (MSCNN) and long short-term memory (LSTM) methods for the C/C composites, is proposed. MSCNN encodes the microstructural information of the ablation image, which is fed into the LSTM model to extract the spatiotemporal features and long-term dependencies latent in the temporal law. The attention mechanism is used to assign different weights to the extracted features which makes the model focus more on the important time steps in the sequence. The results show that the proposed model achieves excellent effects in predicting the evolution of ablative morphology, with high agreement with the simulated values. The method saves time and cost, while providing an efficient and reliable method for the design and optimization of thermal protection materials.