This paper established a model based on an ensemble method to predict strip crown using 230,000 coils of data obtained from a hot rolling line. Before modeling, a specific method was proposed to reduce noise and remove outliers, and a new dataset of 5657 samples was generated. Parameter tuning considering mean squared error (MSE) was carried out to establish three machine learning models including support vector machine (SVC), regression tree (RT), and random forest (RF). Determination coefficient (R²), mean absolute error (MAE) and root mean squared error (RMSE) were used as indicators to evaluate the prediction of models. Results showed that the RF had the best performance with the highest R² of 0.707, as well as the lowest RMSE of 5.66 μm. Moreover, an additional method that repeated the three models 100 times was developed, and box plots were used to visualize the distribution of R², MAE and RMSE. RF can correct for decision trees to reduce overfitting to their training set, improving the generalization, and in this paper, the trained RF which had stable performance is considered as the most recommended model. After that, for RF, rankings of rolling process variable were validated to make a comparison with the existing physical understanding.

本文建立了一个基于整体方法的模型，该模型使用从热轧线获得的23万卷数据来预测带材的凸厚。在建模之前，提出了一种减少噪声和消除异常值的特定方法，并生成了一个包含5657个样本的新数据集。进行了考虑均方误差（MSE）的参数调整，以建立三个机器学习模型，包括支持向量机（SVC），回归树（RT）和随机森林（RF）。测定系数（R ²），平均绝对误差（MAE）和均方根误差（RMSE）用作评估模型预测的指标。结果表明，RF具有最高的性能，具有最高的R ²0.707，以及最低RMSE 5.66μm。此外，开发了将三个模型重复执行100次的另一种方法，并使用箱形图可视化R ²，MAE和RMSE的分布。RF可以校正决策树以减少对其训练集的过度拟合，从而提高泛化能力，在本文中，具有稳定性能的训练RF被认为是最推荐的模型。之后，对于RF，验证轧制过程变量的等级，以与现有的物理理解进行比较。