Novel online prediction model for thermal convexity of work rolls during hot steel rolling based on machine learning algorithms

In this study, an ML-based online prediction strategy is developed for the thermal convexity of work rolls in hot rolling. A finite volume method simulation was developed to establish the temperature field of the work rolls and validated by measurements. A thermal convexity simulation model was established on the basis of the temperature field model to supplement the thermal convexity data. The accelerated vector that departs from the global worst solution is introduced for the PSO algorithm, and the accelerated particle swarm optimization (APSO) algorithm is proposed, which has higher optimization efficiency and better optimization effect than the pso algorithm. It is tested that APSO-RF outperforms APSO-SVM and RBF in predicting the thermal convexity of rolls, showing the best prediction accuracy and stability. In addition, the reduction of rolling has the greatest effect on the work roll convexity. The ML prediction results are very close to the industrial measurements, with small average and maximum differences. The proposed APSO-RF in-line ML method has high accuracy and efficiency and is expected to improve the quality of hot rolling in a practical way.