Prediction of high‐temperature mechanical properties of filled rubber based on the deep learning algorithm

Abstract Filled rubber has wide applications in industries due to its high temperature and corrosion resistance. Therefore, it is crucial to accurately depict the high‐temperature mechanical behavior of the filled rubber. With the expansion of machine learning, the deep learning (DL) algorithm provides a new method to investigate the stress–strain relation of filled rubber. In this paper, the carbon nanotube‐filled fluororubber was used as an example to train various DL models, such as convolutional neural network (CNN), long short‐term memory (LSTM) network, and CNN‐LSTM hybrid models. These models were trained using test data at relatively lower temperatures to predict the relation between stress and strain at higher temperatures. Comparing the test results, it was found that all the predicted results closely matched the experimental data. However, the CNN‐LSTM hybrid model exhibited the lowest error and the most stable calculation process. The results indicated that the DL model not only reduces the time and resources needed to develop new constitutive relationships for filled rubber but also offers greater advantages in predicting the high‐temperature mechanical properties of filled rubber. Highlights DL models predict the high‐temperature mechanical behavior of filled rubber. Higher temperature results got by extrapolation from lower temperature data. More test data at various temperatures improve the prediction accuracy. The prediction accuracy at 200°C surpasses at 220°C. CNN‐LSTM model has the highest calculation efficiency and accuracy.