Transactions of Materials and Heat Treatment

Title：Prediction of rolling force in hot rolling based on machine learning

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ClassificationCode：TF16

year,vol(issue):pagenumber：2021,46(10):233-241

Abstract：

In view of the poor prediction accuracy for the traditional rolling force mathematical model, the rolling force was predicted based on the strip rolling data and the support vector regression (SVR) model. Principal component analysis (PCA) technology was employed to reduce the dimension of input variables, and an improved particle swarm optimization (IPSO) algorithm was proposed to regulate the inertia weight and acceleration factors. The penalty factor c , kernel function parameter g and insensitive loss parameter ε of the SVR model were optimized by the IPSO algorithm. Finally, PCA-IPSO-SVR rolling force prediction model was established. Compared with the PCA-PSO-SVR, PSO-SVR and Grid-SVR models, the three error indexes of the PCA-IPSO-SVR model were at the lowest level, and the average absolute percentage error (MAPE) value was 4.8153%. The simulation results show that compared with the conventional PSO algorithm, the IPSO algorithm can avoid falling into the local minimums, thereby obtaining the optimal parameters of the model and improving the prediction accuracy of the model. Compared with the other three models, the PCA-IPSO-SVR model has higher prediction accuracy and better generalization performance.

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作者简介：刘明华(1976-），男，博士，副教授 E-mail：lmhxauat@163.com 通信作者：王文礼(1977-），男，博士，教授 E-mail：wangwl@nwpu.edu.cn

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