Stacking ensemble learning fused with whale optimisation algorithm based method for crown prediction of hot-rolled strip

Abstract

Precision of strip shape is one of the important indexes to measure the quality of strip steel in hot rolling. Accurate prediction of strip shape can realise the timely adjustment of the production system, which is the important foundation for ensuring the quality and stable production of hot-rolled strip steel products. Given the poor accuracy of the traditional mechanism model, this paper proposes a stacking ensemble learning for crown prediction that consist of the random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), categorical boosting (CatBoost) and linear regression, whose hyperparameters are optimised by whale optimisation algorithm (WOA) Firstly, the key features are selected by analysing the feature importance and the Pearson correlation coefficient between rolling parameters. Then, the hyperparameters of the stacking ensemble learning are optimised by four meta-heuristic algorithms during modeling process, among which WOA has the best optimisation effect. Compared to RF, XGBoost, LightGBM, CatBoost and the stacking, proposed method has the highest prediction accuracy that the hit rate reaches 98.58% in the range of 5.0 μm of crown deviation. In addition, the effect of strip width, bending force, rolling force and roll shifting on crown are analysed based on Shapley additive explanations (SHAP).