Predicting hot-strip finish rolling thickness using stochastic configuration networks

Abstract

In the hot-rolling metal forming process, the consistency and accuracy of the thickness of the metal strip are the most important factors for the product quality control. The current method of utilizing a mechanism prediction model with pre-defined parameters does not perform well due to some limits on the model assumptions and environmental interference. Manually tuning these parameters of the mechanism model may even result in worse performance. To resolve this problem, an advanced randomized learner model, termed stochastic configuration network (SCN), is employed to build a data-driven prediction model which can be trained by using a dataset collected from a real-world hot-rolling production site. Based on the rolling theory and gray relational analysis (GRA), 36 features are selected as the inputs of the prediction model. Experimental results with comparisons show that our proposed method is feasible and outperforms other machine learning methods, such as deep learning models and the random vector functional link (RVFL) model.