Enhanced predictive modeling of hot rolling work roll wear using TCN-LSTM-Attention

During the hot rolling process, the work rolls suffer severe wear, resulting in a relatively short lifespan. Severe roll wear can adversely affect the strip shape while introducing roll wear into the crown calculation model can enhance the model accuracy. Therefore, it is crucial to quantify roll wear during the rolling process. Roll wear is a nonlinear time series and the accuracy of the existing work roll wear mechanistic models is not high. In this paper, a novel prediction model for work roll wear based on TCN-LSTM-Attention is developed. TCN utilizes convolutional structures of local and global information to extract data features, while LSTM focuses on capturing long-term and more complex sequence patterns, effectively handling nonlinear characteristics in data. With the incorporation of attention mechanisms, the model becomes more adept at effectively capturing relationships among different segments within the input sequence, which significantly improves predictive performance and reduces the risk of overfitting. Firstly, outlier cleaning and feature selection are performed using Boruta to construct the data set. Then, the predicted results of the proposed model are compared with the existing time series prediction models. The results indicate that the TCN-LSTM-Attention has the highest prediction accuracy, with an R² of 0.989 and an RMSE of 0.0082 μm. Finally, the predicted results of work roll wear are combined with the mechanism to correct the strip crown pre-calculation model, which significantly improves the calculation accuracy.