JPH03153820A - Method for predicting rolling load - Google Patents

Abstract

PURPOSE:To produce a steel sheet high in thickness precision by using respective rolling friction coefficients in the region above the transformation point of a material to be rolled and in the region below the transformation point to predict the rolling load in warm rolling after hot rolling. CONSTITUTION:The material is hot rolled and then warm-rolled. In this case, the Ar3 transformation point of the material is calculated. A hot rolling friction coefficient is used in the temp. region above the transformation point to predict the rolling load. A warm-rolling friction coefficient is used in the temp. region up to the transformation point to predict the rolling load. Alternately, the rolling in the temp. region above the transformation point and that in the temp. region up to the transformation point are separated, and the rolling loads are respectively learned to predict the rolling loads. Consequently, the precision in predicting the rolling load in warm rolling is improved.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a method for predicting rolling load during hot rolling and subsequent warm rolling.

C. Prior Art] It is a known technique to perform rolling at a temperature below the Ar3 transformation point (hereinafter referred to as warm rolling) at the final stage of the hot rolling process.

For example, in JP-A-62-284016, (a
) Rolling of at least 30% or more, or (b
) A method for manufacturing a steel sheet with excellent electromagnetic properties is disclosed by applying lubrication and performing warm rolling with an average friction coefficient of 30% or more with the average friction coefficient between the roll and the steel sheet being 0.2 or less.

Also, in JP-A-61-159528, TAr3+50°
C to T Ar3 + 2 (processing of 50% or more in total in the temperature range up to 10°C, 20% or more in the final pass, then TAr3+ at a cooling rate of lθ°C/sec or more within 5 seconds)
Cool down to 50°C or below, and then cool to 500°C or above TAr3+
A method for manufacturing a steel plate with excellent workability by performing warm rolling of 50% or more in a temperature range of 50° C. is disclosed.

[Problems to be Solved by the Invention] In addition to the above-mentioned conventional techniques, there are many methods for producing steel sheets with excellent mechanical properties by utilizing rolling in a hot region followed by rolling in a warm region. However, all of them only disclose conditions for improving the characteristics, and do not disclose techniques for manufacturing products to predetermined dimensions and shapes, such as thickness and width.

Generally, when hot rolling is followed by warm rolling, the accuracy of predicting the rolling load is poor, and as a result, it is difficult to manufacture a steel plate with good thickness accuracy.

An object of the present invention is to provide a method for predicting rolling load for manufacturing a steel plate with excellent plate thickness accuracy during warm rolling subsequent to hot rolling.

[Means for Solving the Problems] The present invention as set forth in claim 1 provides a method for reducing Ar3 of the rolled material when performing hot rolling and subsequent warm rolling on the rolled material.
The transformation point is calculated, and the rolling load is predicted using the hot rolling friction coefficient for rolling in a temperature range above the transformation point, and the warm rolling friction coefficient for rolling in a temperature range below the transformation point. This is what I did.

The present invention according to claim 2 provides for hot rolling and subsequent warm rolling of a material to be rolled, rolling in a temperature range above the transformation point of the material to be rolled and in a temperature range below the transformation point. The rolling load is learned separately and the rolling load is predicted separately.

[Effect] As is well known, the rolling load P is given by the following formula.

P = m-f2d-Qp-W ... (I) I2d
=, r positive 7ΣT...(2) Here, k
Recommendation: deformation resistance, J2d: projected contact arc length, Qp: number of rolling forces, W two plate width, R': flat roll radius, Δh: plate thickness difference before and after rolling. The deformation resistance Km in equation (1) is a function of strain ε, strain rate ε, rolled material temperature T, chemical composition C of the rolled material, etc., and is expressed by equation (3).

ki=f(ε, e, T%C...) ...(3) Also, the rolling blade incline number QP in equation (1) is the flat roll radius R', the plate thickness h, the rolling reduction ratio r, and the friction coefficient. It is a function of μ and is expressed by equation (4).

Qp=f (R', h, r, u) ''・(4) In the setup of normal hot strip finishing rolling, these formulas are used to estimate the rolling load, and the roll gap changes depending on the rolling load. The roll opening degree was adjusted so that rolling could be carried out according to a predetermined reduction schedule in anticipation of the amount of rolling. Calculation of resistance (
I) Calculation of rolling force function (IV) Calculating rolling load.

2 (1) to (4) are monotonous functions as long as hot rolling is performed, so the accuracy of predicting the rolling load is very good by learning, but if the rolling force is below the material's transformation point during the hot strip finishing mill, If warm rolling is included, the above (n), (
III) is different, it is necessary to take the following method.

It is a well-known fact that the transformation point Ar3 of a rolled material depends on the chemical components contained, and this transformation point is determined by the following factors: the linear expansion coefficient changes at the transformation point, the deformation resistance changes discontinuously, etc. It is possible to measure by FIG. 2 is an example of the latter. Therefore, the transformation points of various steels must be known in the form of a table or by a regression equation using the chemical composition as an independent variable.

Next, the deformation resistance has temperature dependence as shown in Figure 2.
However, since the rolling temperature changes discontinuously at the transformation point, it is important whether the rolling temperature is above the transformation point or below the transformation point. However, as mentioned above, this discontinuity is known, so
It goes without saying that the rolling load should be calculated taking into account the deformation resistance.

Next, regarding the rolling force function, the independent variables R' h and r are geometric variables, and are given as rolling conditions or determined in the process of convergence calculation. On the other hand, Geleji's equation is known in which the friction coefficient μ monotonically increases as the rolling temperature decreases.

μ= 0.112-0.0O05T-0,056V
... (5) Here, T: rolling temperature, V: rolling speed However, as a result of measuring μ over the warm region to hot region,
It has become clear that this μ changes discontinuously at the transformation point, as shown in FIG. Here, the friction coefficient was calculated by measuring the transfer mark distance of the mark made on the roll to the rolled material, calculating the advance rate, and using Orovan's formula regarding the advance rate and the friction coefficient. In other words, if the friction coefficient in the warm rolling zone is extrapolated from the friction coefficient in the conventional hot rolling zone and is taken to be larger as the temperature decreases, the estimation of the rolling force function will be incorrect and the predicted value of the rolling force will be the actual value. As a result, the plate thickness becomes thinner.

As mentioned above, when estimating the friction coefficient, the transformation temperature of the rolled material is calculated, and whether the rolling temperature is above or below the transformation point is calculated. Based on that, the friction coefficient table for each temperature range is calculated. Unless the friction coefficient is predicted using a formula or a mathematical formula, it is impossible to expect the same rolling load accuracy in the warm region.

Therefore, according to the present invention as set forth in claim 1, the friction coefficient used for predicting the rolling load is estimated depending on whether the rolling temperature is above the transformation point or below the transformation point.
It is possible to improve the prediction accuracy of rolling loads in the warm rolling region and manufacture steel plates with excellent plate thickness accuracy.

Furthermore, according to the present invention as set forth in claim 2, learning of the rolling load is performed separately depending on whether the rolling temperature is above the transformation point or below the transformation point. It is possible to improve load prediction accuracy and manufacture steel plates with excellent plate thickness accuracy.

[Example] (Example 1) First, specific implementation results of the present invention as set forth in claim 1 will be described.

800.850, 900' of low carbon steel with a thickness of 10cm
Ck: It was heated and rolled with a roll having a diameter of 310 mm at a reduction rate of 20%. The results are summarized in Table 1.

The transformation point of this material T Ar3 = 830°C,
Materials A and B, whose rolling temperatures are 900°C and 850°C, are hot-rolled, and their deformation resistance is calculated from the formula as 17.9.
．． 18.5 kgf/■Otori 2, and the estimated values of the friction coefficient were 0.3 and 0.35, respectively, from the formula 1.

For material C, which is rolled at a rolling temperature of 800℃ using the conventional method, the friction coefficient is estimated to be 0 using the conventional method.
．． 4, so calculate Orowan's rolling force function and a
When the rolling load per width was calculated using the deformation resistance value of 15.0 kgf/am'' at OO°C, it was 333 kgf/臘鳳, which was 5.7% larger than the actual measured load of 314 kgf/am.

On the other hand, for material D, to which the method of the present invention is applied for a material whose rolling temperature is 800°C, the coefficient of friction of this steel in the warm region below the transformation point is 0.25 at 800°C from the mathematical formula. Therefore, as a result of calculating the rolling load per width using this, the calculated load was 315 kgf/agi, which almost matched the actual value.

(Example 2) Next, specific implementation results of the present invention as set forth in claim 2 will be described.

Learning is being carried out to improve the prediction accuracy of rolling loads in hot strip finishing mills, but in the past, learning for hot-rolled materials and warm-rolled materials has not been done separately. The prediction error at that time was 1σ = 8.7% for a steel plate with a thickness of 1.2 to 2.3 mm, but when learning was performed separately for the warm region and the hot region, the method of the present invention Since the estimation of the friction coefficient in the warm region became more accurate, the prediction accuracy of the rolling load in the warm region improved to 10 = 5.1%.

[Effects of the Invention] As described above, according to the present invention, it is possible to improve the prediction accuracy of rolling load in warm rolling subsequent to hot rolling, and to manufacture a steel plate with excellent plate thickness accuracy.

[Brief explanation of the drawing]

FIG. 1 is a diagram showing the relationship between the friction coefficient and the temperature of the rolled material, and FIG. 2 is a diagram showing the relationship between the deformation resistance of the rolled material and the temperature.

Claims (2)

[Claims] (1) When subjecting the rolled material to hot rolling and subsequent warm rolling, calculate the Ar3 transformation point of the rolled material, and when rolling in a temperature range above the transformation point, calculate the hot rolling friction coefficient,
A method for predicting rolling load, which is characterized by predicting rolling load using a warm rolling friction coefficient during rolling in a temperature range below the transformation point. (2) When performing hot rolling and subsequent warm rolling on the rolled material, separate rolling in a temperature range above the transformation point of the rolled material and rolling in a temperature range below the transformation point, A rolling load prediction method characterized by separately learning rolling loads and predicting rolling loads.