A New Predictive Model for Strip Crown in Hot Rolling by Using the Hybrid AMPSO‐SVR‐Based Approach

Abstract

Strip crown prediction model based on support vector machines (SVMs) is proposed, and the improved adaptive mutation particle swarm optimization (AMPSO) algorithm is used to optimize the model parameters in this paper. A set of online inspection data from the steel plant is adopted to train and test model. The overall performance of the model is evaluated by the decision coefficient ( ), mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Comparing the results calculated by other relevant models and the present model with same experimental data, the accuracy of the model presented in this paper is verified. The AMPSO‐SVR model can be successfully applied to the prediction of strip crown in hot rolling. Both prediction accuracy and generalization ability of the new model have achieved good results. The proposed model provides a new method and idea for shape control and optimization research in hot strip rolling process.