Multi-dimension and multi-modal rolling mill vibration prediction model based on multi-level network fusion

Abstract

Mill vibration is a common problem in rolling production, which directly affects the thickness accuracy of the strip and may even lead to strip fracture accidents in serious cases. The existing vibration prediction models do not consider the features contained in the data, resulting in limited improvement of model accuracy. To address these challenges, this paper proposes a multi-dimensional multi-modal cold rolling vibration time series prediction model (MDMMVPM) based on the deep fusion of multi-level networks. In the model, the long-term and short-term modal features of multi-dimensional data are considered, and the appropriate prediction algorithms are selected for different data features. Based on the established prediction model, the effects of tension and rolling force on mill vibration are analyzed. Taking the 5th stand of a cold mill in a steel mill as the research object, the innovative model is applied to predict the mill vibration for the first time. The experimental results show that the correlation coefficient (R²) of the model proposed in this paper is 92.5%, and the root-mean-square error (RMSE) is 0.0011, which significantly improves the modeling accuracy compared with the existing models. The proposed model is also suitable for the hot rolling process, which provides a new method for the prediction of strip rolling vibration.