Ensemble Learning Based Methods for Crown Prediction of Hot-Rolled Strip

Abstract

The strip crown or profile generated by the cooperation of finishing mills is affected by many factors, so obtaining an accurate crown has always been a challenge in hot strip rolling. As a kind of solution to ensure the crown accuracy of hot-rolled strips, this study develops three novel strip crown prediction models using the well-performing and efficient tree-based ensemble learning algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), respectively. The comparison results of measured and predicted strip crown show that all developed strip crown prediction models perform well based on the accurate extraction of key features as model inputs, the collection and pre-processing of a large amount of modeling data, and the use of the Bayesian optimization technique. By comparison, the LightGBM model with both high efficiency and high accuracy is considered the most recommended method for hot-rolled strip crown prediction. Besides, according to the feature importance scores of the input variables calculated based on the LightGBM model, the impact levels of each input variable on the strip crown are measured, and the calculation results fit well with the classical hot rolling theory indicating the modeling route as a reliable one.