Point and interval prediction of the effective length of hot-rolled plates based on IBES-XGBoost

Abstract

The prediction of the effective length of steel plates is essential for improving the yield. Due to the complexity of variables and the lack of measuring means, there are few efficient and accurate models for the prediction of effective length. In this paper, a novel data-driven model is proposed to predict the effective length of hot rolled plates. The dataset used for the problem is established using machine vision techniques. An improved bald eagle search algorithm is used to optimize extreme gradient boosting (IBES-XGBoost) for prediction. Based on the point prediction results, the effective length interval of plates is estimated by combining the kernel density estimation method (KDE), which effectively reduces the risk caused by errors. The main work was completed in Python programming language and the validation shows that the proposed model can obtain highly accurate point and interval prediction results, which can provide accurate data reference for billet design and plane shape control of plates.