In the hot strip rolling (HSR) process, accurate prediction of bending force can improve the control accuracy of the strip crown and flatness, and further improve the strip shape quality. In this paper, six machine learning models, including Artificial Neural Network (ANN), Support Vector Machine (SVR), Classification and Regression Tree (CART), Bagging Regression Tree (BRT), Least Absolute Shrinkage and Selection operator (LASSO), and Gaussian Process Regression (GPR), were applied to predict the bending force in the HSR process. A comparative experiment was carried out based on a real-life dataset, and the prediction performance of the six models was analyzed from prediction accuracy, stability, and computational cost. The prediction performance of the six models was assessed using three evaluation metrics of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R²). The results show that the GPR model is considered as the optimal model for bending force prediction with the best prediction accuracy, better stability, and acceptable computational cost. The prediction accuracy and stability of CART and ANN are slightly lower than that of GPR. Although BRT also shows a good combination of prediction accuracy and computational cost, the stability of BRT is the worst in the six models. SVM not only has poor prediction accuracy, but also has the highest computational cost while LASSO showed the worst prediction accuracy.

中文翻译：

---

预测热轧带钢弯曲力的六种机器学习模型的比较评估

在热轧带钢（HSR）过程中，弯曲力的准确预测可以提高带材凸厚和平直度的控制精度，并进一步提高带钢的形状质量。本文提出了六种机器学习模型，包括人工神经网络（ANN），支持向量机（SVR），分类和回归树（CART），装袋回归树（BRT），最小绝对收缩和选择算子（LASSO）和高斯过程回归（GPR）用于预测高铁过程中的弯曲力。基于真实数据集进行了比较实验，并从预测准确性，稳定性和计算成本方面分析了这六个模型的预测性能。使用均方根误差（RMSE）的三个评估指标评估了六个模型的预测性能，²）。结果表明，GPR模型被认为是预测弯曲力的最佳模型，具有最佳的预测精度，更好的稳定性和可接受的计算成本。CART和ANN的预测准确性和稳定性略低于GPR。尽管BRT还显示出预测准确性和计算成本的良好组合，但是BRT的稳定性在这六个模型中最差。SVM不仅具有较差的预测精度，而且具有最高的计算成本，而LASSO的预测精度最差。