Early Perception and Accurate Prediction of Hot Strip Flatness Based on Data Dimension Reduction and Multi-Output Regression

To achieve early perception and accurate prediction of flatness quality, a partial least squares–particle swarm optimization–multi-output support vector regression (PLS-PSO-MSVR) is proposed. Firstly, we parameterized the flatness and used it as an evaluation indicator for flatness. Then, the prediction model was constructed using multi-output support vector regression (MSVR). In the modeling process, particle swarm optimization is used to optimize the parameters. To overcome the problem of information redundancy, reduce data dimensions to reduce computational time, and improve the prediction performance of the algorithm, this paper combines partial least squares and PSO-MSVR to achieve accurate prediction of the flatness features. Finally, the actual industrial process data from the hot rolling 1580 production line was used for validation, and the predicted performance was evaluated using mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), and coefficient of determination (R2). MAE decreased to 0.15, MSE decreased to 0.038, and RMSE decreased to 0.195. The R2 approaches 1, indicating excellent model fit. This study achieves accurate prediction of the flatness characteristic coefficient, which not only enhances the diagnostic efficiency of steel flatness quality but also helps avoid unnecessary economic losses. Moreover, the prediction model provides a reliable basis for flatness control, offering operators a user-friendly reference tool. This approach compensates for the time lag inherent in the original system and contributes to improved accuracy in flatness control.