Predicting Hot-rolled Strip Crown Using a Hybrid Machine Learning Model

The stability of crown is a crucial factor in ensuring the quality of hot-rolled strips. In this study, a hybrid model based on ensemble learning is developed, incorporating four reliable ML models, namely support vector machine (SVM), gaussian process regression (GPR), artificial neural network (ANN), and random forest (RF). To enhance the accuracy and interpretability of the resulting crown model, pretreatment methods such as feature selection and cluster analysis are employed. The feature selection method based on mechanism analysis and maximum information coefficient (MIC) is used to obtain the optimized feature subset, while the K-means clustering algorithm is utilized to measure data similarity and cluster data points with high similarity. Analysis of experimental results indicates that the four single ML models exhibit good prediction performance for strip crown, with determination coefficients above 0.96. The hybrid model outperforms each of the single models in terms of prediction accuracy. Moreover, the incorporation of pretreatment methods leads to an increase in the determination coefficient and a decrease in the root mean square error for each model, culminating in the superior overall performance of the hybrid model established after pretreatment. These findings highlight the potential of the proposed approach for improving the accuracy and reliability of ML models in complex industrial environments.