Prediction and analysis of cold rolling mill vibration based on a data-driven method

Abstract

Mill chatter is one of the most common problems in cold rolling. Thus, it is important to investigate the mill chatter phenomenon to ensure a high-speed and stable rolling process. The traditional mill chatter mechanism model cannot meet the monitoring and rapid diagnosis needs of the rolling process in the field. In this paper, a data-driven mill vibration analysis method is proposed. The main objective of this study was to develop a mill vibration monitoring method and an intelligent algorithm for mill chatter early warning. Rolling experiments showed that the proposed monitoring method could be a promising and effective technique for assessing the chatter phenomenon. The mill vibration acceleration amplitude prediction performance of a support vector regression, neural-network-based method, and extreme gradient boosting method were evaluated. The results proved that the prediction performances of the proposed extreme gradient boosting method were highly reliable with the highest determination coefficient value of 0.779, lowest mean absolute percentage error of 9.7%, and better forecast robustness under all of the dataset ratios. Meanwhile, the contribution rates of the variables on the mill vibrations were investigated, and results showed that an effective way to eliminate the mill chatter was to control the rolling speed, cumulative rolling strip length, tension and roll radius.