Transactions of Materials and Heat Treatment

Title：Prediction on finish rolling temperature for hot-rolled strip based on random forest

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ClassificationCode：TG335.5

year,vol(issue):pagenumber：2025,50(1):134-139

Abstract：

The complex boundary conditions and difficulty in parameter prediction in the precision rolling zone limit the accuracy of traditional online hot rolling temperature prediction for strip steel. Therefore, in order to improve the accuracy of final rolling temperature prediction, a data-driven modeling approach using random forest was adopted, and taking forty-three characteristic factors that affect the final rolling temperature as the input values for the data-driven final rolling temperature prediction model. Then, the imbalanced datasets such as changing specifications were processed by a hybrid algorithm of NCL and SMOTE, and the random feature selection of the decision tree included features that were highly or lowly correlated with the target variable. The results show that the constructed random forest prediction model for the final rolling temperature of hot-rolled strip has a maximum prediction value error of within 15 ℃ on the test set and good regression effect and generalization ability, meeting the accuracy requirements for the final rolling temperature prediction of hot-rolled strip on site.

Funds：

国家自然科学基金资助项目（52305405）;海安太原理工大学先进制造与智能装备产业研究院开放研发项目（2023HA-TYUTKFYF011）；常州工业职业技术学院高层次人才项目（GCC202413101006）

作者简介：马更生（1984-），男，博士，讲师 E-mail：gengshengma@163.com

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