Real-time rubber quality model based on CNN-LSTM deep learning theory

Abstract

Predicting the quality of a rubber compound is a necessity for intelligent mixing. However, the conventional quality prediction based on the power curve has low precision and significant dispersion, making it challenging to satisfy the need for real-time rubber mixing quality inspection. In this research, the vibration signals were collected for the first time as an input feature of the mixing quality prediction model, with carbon black dispersion is utilized as a quality index. On the basis of the theory of deep learning, the online quality prediction model of mixing was constructed using a variety of featured extraction methods and neural network structures, and the models were compared and tested. After 5 experiments, the average root mean square error (RMSE) of convolutional neural network-long short-term memory network (CNN-LSTM) model is 0.1160, which is 21.46% higher than that of LSTM model after time-domain featured extraction, demonstrating the efficacy and superiority of CNN-LSTM end-to-end model. This study is essential for the progression and breakthrough of the real-time mixing quality optimization issue.