Prediction Of Rolling Force Of Hot Rolled Strip Based On Support Vector Machine

The rolling force mathematical model is one of the most important mathematical models in the rolling area,and its prediction accuracy directly affects the thickness accuracy and shape quality of the products.The modeling process of the traditional mathematical model assumes and simplifies many practical factors in the rolling process.The calculation error of the model is large,and it often can not meet the requirements of modern high-precision rolling technology.Establishing a high-precision mathematical model of rolling force has become one of the research hot spots in recent years.Support vector regression(SVR)is a machine learning method based on statistical learn theory.It can not only avoid the errors caused by traditional mathematical model assumptions that are out of reality and simplified too rough,but also has strong generalization ability in the case of limited experimental data.At present,the SVR rolling force model has some shortcomings,such as single kernel function in SVR rolling force model may be difficult to solve the high-dimensional and nonlinear strip rolling problem,and the dimension of input variables is too high.In this paper,Sims hot rolling formula is used to determine the main parameters of affecting the rolling force.According to the main parameters,the experimental data of plate rolling are collected from two large domestic steel plants,and the experimental data are preprocessed by T test criterion and normalization method.The preprocessed data of A plant is used as the training set to train the model and the preprocessed data of B plant is used as the test set to verify the accuracy of the model,and the comprehensive performance of the model is evaluated by using statistical indexes.The main research contents and results of this paper are as follows:A hybrid kernel function(HKF)is used instead of a single kernel function,and a hybrid optimization algorithm(PSO-BAS)combining the particle swarm algorithm algorithm(PSO)and the beetle antennae search algorithm(BAS)is proposed.The PSO-BAS algorithm optimizes the parameters of the HKSVR rolling force model(penalty factor(c),radial basis kernel function(g),insensitive loss parameter(?),polynomial kernel function(d)and control parameters(m)),and the PSO-BAS-HKSVR rolling force prediction model is established.The prediction accuracy of the PSO-BAS-HKSVR is verified by the test sets,and the calculation results of the model are compared with PSO-HKSVR,PSO-SVR,grid search algorithm(Grid)-SVR,BP neural network(BPNN),general regression neural network(GRNN),radial basis function network(RBF)and traditional rolling force mathematical models.The PSO-BAS-HKSVR model has better generalization ability,and the average absolute percentage error(MAPE)value of the model is only 4.9600%.On the other hand,the principal component analysis(PCA)technology is used to reduce the dimensionality of the input variables,and the method of adaptively adjusting the inertia weight and acceleration factors is proposed to improve the PSO algorithm(IPSO),and the IPSO algorithm is used to optimize the parameter of SVR rolling force model(c,g),and the PCA-IPSO-SVR rolling force prediction model is established.The test set is used to verify the prediction accuracy of PCA-IPSO-SVR,and the calculation results of the model are compared with PCA-PSO-SVR,PSO-SVR,Grid-SVR,BPNN,GRNN,RBF and traditional rolling force mathematical models.The PCA-IPSO-SVR model has the highest prediction accuracy,and the MAPE value of the model is 5.1716%.Compared with the PCA-IPSO-SVR model,the prediction accuracy of PSO-BAS-HKSVR is increased by 4.09%,but the calculation speed of PSO-BAS-HKSVR is reduced by 7.982 s.Compared with the calculation speed of the model,it is of more important significance to improve the prediction accuracy of the model.The PSO-BAS-HKSVR model has strong generalization performance,and a new method is proposed to achieve high-precision rolling force prediction.