Research In Optimizing The Pressure Model Of Hot Strip Rolling Mill

The prediction for rolling pressure is the core of the finishing mill group computer model. Its forecast accuracy will directly affect the roll gap settings, the bite stability, thickness accuracy and finally the quality of the products and so on. In conditions of the increasingly competitive market, the higher users'requirements for steel, the more important position of the rolling forces prediction in the rolling process. Because we need to consider quite a number of linear and nonlinear factors in the rolling process, very complicated, as the result of the model using in the Stainless steel works 1780mm hot rolling production line of Bashan Iron Steel C o. is not very ideal, it is need to do a further improvement.In this paper, we use the offline simulation to obtain and then analysis the production data and study on the impact of the chemical element in the strip on the deformation resistance, and then return the deformation resistance parameters of the strip chemical composition in linear regression math analysis method. It optimizes the deformation resistance model, and through the analysis of the self-learning model of the rolling force. We have made optimized calculation of rolling force self-adaptive learning in exponential smoothing method, which improved the forecast accuracy of the 1780mm hot rolling mill finishing rolling pressure calculation model and the adaptability after its change. Not only improved the deformation resistance model calculation accuracy in the rolling force prediction model, but also the self-learning coefficient of the rolling force model changing in the steel can still maintain a steady trend.Anyway, in regard the 1780mm hot strip mill rolling force prediction model forecast accuracy and adaptability, this issue has achieved preliminary results. However, because of limited theoretical research, and the data volume is not adequate, the expansion of the data remains to be further research and improvement in-depth. And on this basis implement the good adaptability of the rolling pressure model for more steel rolling force prediction.