Pressure setting, prediction and self learning method for temper rolling

Abstract

The invention discloses a method for setting, predicting and self-learning skin-pass rolling pressure, which comprises the following steps: a. collecting n groups of actual skin-pass rolling process parameters and n groups of actual rolling pressure data corresponding to the parameters; b. introducing steel grade of a planishing mill a0 and influence coefficients of working conditions a1, structuring a basic rolling pressure model which is suitable for the skin-pass rolling and reflects the basic functional relationship between each rolling process parameter and rolling pressure; c. calculating initial optimal values of the steel grade a0 and the influence coefficients of the working conditions a1; d. completing the correction of the initial optimal values of the steel grade a0 and the influence coefficients of the working conditions a1; e. setting and predicting the skin-pass rolling pressure; f. and self-learning the skin-pass rolling pressure model. Through continuously self-learning the steel grade and the influence coefficients of the working conditions, the invention effectively guarantees the calculation accuracy of the model on the premise of not damaging the physical significance of key rolling parameters such as friction coefficient and deformation resistance. At the same time, the method of the invention is clear in theory and rapid in calculation, thus the method issuitable for online application.

Description

technical field [0001] The invention relates to a tempering production technology, in particular to a rolling pressure setting, forecasting and self-learning method for tempering rolling which is practical in engineering. Background technique [0002] According to the basic rolling theory, there are many models used to set and predict the rolling pressure in the cold rolling production process, the famous one is the Stone model [1] (Stone M D. Iron and Steel Engineer Year Book. Pittsburgh: Association of iron and engineer publisher, 1953, 115~128), Hill model [2] (Cao Hongde. Basis of Plastic Deformation Mechanics and Principle of Rolling. Beijing: Machinery Industry Press, 1981), Bland-Ford Model [3] (Ford H, Alexander J M J.Inst.Metals, 1959, 34(88): 47~55), Lian Jiachuang Model [4] (Calculation of rolling pressure and limit minimum thickness of cold-rolled sheet. Heavy Machinery. 1979, (2,3): 20~37; 21~34) and so on. These models all have a common feature, that is, the...

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Problems solved by technology

However, due to the small reduction rate in the temper rolling process (generally about 0.3 to 1%), the effective friction coefficient in the roll gap is about an order of magnitude larger than that of the usual cold rolling. The contact arc of each interface is more of a plane property than a cylindrical surface property, so the traditional rolling pressure model based on the Hirschcock formula is not suitable for temper rolling

Although, W.L. Roberts [5] According to the characteristics of the tempering process, a set of temper rolling pressure model was developed, but this model is only an empirical formula, which is mainly aimed at general low-carbon steel, which is not universal and cannot be directly extended to different units

Other related literature [6，7] ([6] Bai Zhenhua. Research on rolling pressure model during thin strip temper rolling, Chinese Journal of Mechanical Engineering, 2004, 40(8): 63-66; [7] Bai Zhenhua, Lian Jiachuang. Baosteel 2050 hot rolling temper mill Research on the rolling pressure model. Heavy machinery, 2002, (6): 11-13), although the relevant temper rolling pressure model can achieve high calculation accuracy, but because the model involves an iterative process, the calculation is not very Stable, and the calculation time is uncontrollable, so the relevant model is more suitable for offline analysis and forecasting, but not suitable for online analysis and forecasting; at the same time, in order to improve the forecasting accuracy of the rolling pressure model in the actual production process, often The self-learning scheme of friction coefficient or deformation resistance is adopted, and the changes of all working conditions on site and the influence of incoming material fluctuations are attributed to the friction coefficient or deformation resistance, and the accuracy of the model is guaranteed by continuously correcting the friction coefficient or deformation resistance.

The biggest disadvantage of using this method is that after multiple self-study, the friction coefficient or deformation resistance loses its original physical meaning after multiple corrections, and becomes "pseudo-deformation resistance" or "pseudo-friction coefficient", which not only cannot guarantee the accuracy of the model Prediction accuracy is not conducive to on-site failure analysis and optimization of rolling process parameters

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