Steel rolling time prediction method based on two-level decision tree model

Rolling time is a key parameter in the hot rolling production of wide and thick plates. However, due to the complexity and uncertainty of production, it is difficult to accurately preset it in the production preparation stage, which affects the preparation and implementation effect of production Operation plan. To solve this problem, based on a large number of wide and heavy plate rolling historical data accumulated in production, the key factors affecting the rolling time and their relationship were analyzed. According to the characteristics of data type and data struc-ture, a two-level decision tree prediction model was proposed to improve the preset accuracy of rolling time. Firstly, the information gain rate of C4. 5 was improved based on the dependency between attributes, and the branch nodes were reduced by the level of information entropy. The improved C4. 5 Classification tree was used to model the nominal attributes in the data. Furthermore, based on Fayyad boundary point decision theorem and Support vector machine improved cart algorithm, a regression model for numerical attributes in the Classification subset was estab-lished. The samples from rolling history data was selected randomly for experiment. The two-level decision tree model was compared with a variety of prediction models to verify the accuracy and robustness of the proposed model. © 2025 CIMS. All rights reserved.