Prediction of hot-rolled strip crown based on Boruta and extremely randomized trees algorithms

The quality of hot-rolled steel strip is directly affected by the strip crown. Traditional machine learning models have shown limitations in accurately predicting the strip crown, particularly when dealing with imbalanced data. This limitation results in poor production quality and efficiency, leading to increased production costs. Thus, a novel strip crown prediction model that uses the Boruta and extremely randomized trees (Boruta–ERT) algorithms to address this issue was proposed. To improve the accuracy of our model, we utilized the synthetic minority over-sampling technique to balance the imbalance data sets. The Boruta–ERT prediction model was then used to select features and predict the strip crown. With the 2160 mm hot rolling production lines of a steel plant serving as the research object, the experimental results showed that 97.01% of prediction data have an absolute error of less than 8 μm. This level of accuracy met the control requirements for strip crown and demonstrated significant benefits for the improvement in production quality of steel strip.