摘要 In the traditional rolling force model of tandem cold rolling mills, the calculation of the deformation resistance of the strip head does not consider the actual size and mechanical properties of the incoming material, which results in a mismatch between the deformation resistance setting and the actual state of the incoming material and thus affects the accuracy of the rolling force during the low-speed rolling process of the strip head. The inverse calculation of deformation resistance was derived to obtain the actual deformation resistance of the strip head in the tandem cold rolling process, and the actual process parameters of the strip in the hot and cold rolling processes were integrated to create the cross-process dataset as the basis to establish the support vector regression (SVR) model. The grey wolf optimization (GWO) algorithm was used to optimize the hyperparameters in the SVR model, and a deformation resistance prediction model based on GWO–SVR was established. Compared with the traditional model, the GWO–SVR model shows different degrees of improvement in each stand, with significant improvement in stands S3–S5. The prediction results of the GWO–SVR model were applied to calculate the head rolling setting of a 1420 mm tandem rolling mill. The head rolling force had a similar degree of improvement in accuracy to the deformation resistance, and the phenomenon of low head rolling force setting from stands S3 to S5 was obviously improved. Meanwhile, the thickness quality and shape quality of the strip head were improved accordingly, and the application results were consistent with expectations.
Abstract：In the traditional rolling force model of tandem cold rolling mills, the calculation of the deformation resistance of the strip head does not consider the actual size and mechanical properties of the incoming material, which results in a mismatch between the deformation resistance setting and the actual state of the incoming material and thus affects the accuracy of the rolling force during the low-speed rolling process of the strip head. The inverse calculation of deformation resistance was derived to obtain the actual deformation resistance of the strip head in the tandem cold rolling process, and the actual process parameters of the strip in the hot and cold rolling processes were integrated to create the cross-process dataset as the basis to establish the support vector regression (SVR) model. The grey wolf optimization (GWO) algorithm was used to optimize the hyperparameters in the SVR model, and a deformation resistance prediction model based on GWO–SVR was established. Compared with the traditional model, the GWO–SVR model shows different degrees of improvement in each stand, with significant improvement in stands S3–S5. The prediction results of the GWO–SVR model were applied to calculate the head rolling setting of a 1420 mm tandem rolling mill. The head rolling force had a similar degree of improvement in accuracy to the deformation resistance, and the phenomenon of low head rolling force setting from stands S3 to S5 was obviously improved. Meanwhile, the thickness quality and shape quality of the strip head were improved accordingly, and the application results were consistent with expectations.
Ze-dong Wu,Xiao-chen Wang,Quan Yang, et al. Deformation resistance prediction of tandem cold rolling based on grey wolf optimization and support vector regression[J]. Journal of Iron and Steel Research International, 2023, 30(09): 1803-1820.
2023, Vol. 30 
