Analysis and Prediction of Sticker Breakout Based on XGBoost Forward Iterative Model

Abstract

All 61 sticker breakouts and 183 false sticker breakouts were obtained based on the on-line mould monitoring system during the conventional slab continuous casting. The 16-dimensional temperature characteristics and temperature velocity characteristics of the sticker breakout were extracted. The sticker breakout recognition based on the XGBoost forward iterative model was developed and optimized by the mean square error algorithm. The results show that the prediction probability of the sticker breakout after optimization is in the range of 0.72∼1.00. The smallest output value 0.5 higher than that before optimization. When the threshold is set to 0.65, the optimized XGBoost model can correctly predict all sticker breakouts and has a 99.5% accuracy rate. The XGBoost model has a stronger generalization ability and higher prediction accuracy, which promotes the intelligent production of continuous casting.