KR100325335B1 - Method for predicting roll force in cold rolling - Google Patents

Abstract

PURPOSE: A method for predicting roll force in cold rolling is provided to obtain products of high quality by not using values measured at an ordinary temperature, but using values measured at about 200 deg.C that is an actual rolling temperature as strain resistance values of a metallic material applied. CONSTITUTION: In controlling a rolling mill using an ordinary setup model applied AGC (automatic gauge controller) system so that thickness error of a strip rolled in cold rolling process is within the allowable range, the method for predicting roll force in cold rolling comprises the steps of obtaining a strain resistance prediction equation represented as in the following expression 3 through regression analysis method using strain resistance measured values of a concerned material according to the test results after performing rolling-tensile test in the state that the measured temperature values are maintained by measuring temperature of the strip to be rolled coming out of the rolling mill during actual continuous cold rolling: £Expression 3| k=lx(m+ε)¬n, where k is strain resistance, l, m and n are peculiar constants of the materials differed depending on the materials to be rolled, and ε is strain rate; and applying the obtained strain resistance prediction equation in the AGC (automatic gauge controller) system to a roll force prediction equation represented as in the following expression 1: £Expression 1| P=BxkxKtxDpx{R(H-h)}¬0.5, where B is width of strip to be rolled, k is strain resistance of a material to be rolled, Kt is an influence term by tensions at front and rear of the rolling mill, Dp is an influence term of friction coefficient, R is radius of work rolls of the rolling mill, H is strip thickness at the inlet side of the rolling mill, and h is strip thickness at the outlet side of the rolling mill.

Description

Prediction Method of Rolling Load in Cold Rolling

The present invention relates to a rolling load prediction method in cold rolling, and in particular, to reduce the thickness deviation generated in the rolling plate through more accurate rolling load prediction, thereby improving the error rate to obtain economic benefits.

In general, in an automated cold rolling mill, all processes are controlled by a computer, and software called a setup model is required to actually drive these process control computers.

The setup model determines the operating conditions of the rolling mill to produce the desired product and changes the operating conditions of the equipment according to the change of the situation during the rolling operation.

The setup model is based on the rolling theory obtained through many studies for a long time, and the most important of the various rolling theories is the rolling load prediction equation, which is also widely known in the art as the Hill equation.

The rolling load prediction equation is used to calculate the roll gap and roll speed setpoints, which are the ultimate goals of the setup model, and to obtain the coefficients needed to drive the Automatic Gauge Controller (AGC).

Therefore, if the calculated rolling load is different from the actual rolling load, the plate thickness variation is large, resulting in economic loss due to the reduction of the real number. Therefore, accurate prediction of rolling load must be made in order to produce rolled plates of precise thickness.

The configuration of a general AGC system is briefly described as follows.

In a typical cold rolling process, a plurality of rolling mill stands are sequentially installed to sequentially reduce the thickness of the rolling plate, and in particular, an AGC system is intensively installed so that a roll of a suitable size can be formed in the first stand.

In other words, the roll cap of the No. 1 stand rolling mill, ie the upper and lower work rolls, calculated by the top-level computer in a given rolling condition is controlled by a screw down installed on the upper part of the upper work roll. When rolling proceeds after the initial value is set in this way, the thickness of the rolled plate is measured by a thickness measuring instrument installed at the rear end of No. 1 stand, and the rolling is continued if the error is within the allowable range compared to the target thickness. However, if the error is large, the process control computer performs real time control to reduce the error.

The process control computer calculates the control amount of the hydraulic system to be operated in order to reduce the plate thickness error, and commands the lower work to the hydraulic system installed at the lower part. This series of operations is performed until the plate thickness error is within the allowable range. Will repeat. Real-time control of such plate thickness control is commonly referred to as AGC.

In order to obtain an appropriate roll cap control amount (ΔS) in this AGC, the plasticity coefficient (M), which is a parameter used to obtain the roll cap control amount (ΔS), should be obtained, and the rolling load prediction is used to obtain the plasticity coefficient (M). The formula will be used. Therefore, if accurate prediction of the rolling load is assumed, an accurate roll cap control amount D can be obtained, so that a rolled plate of precise thickness can be produced.

The rolling load prediction formula used in the ordinary cold rolling set-up motor is as follows.

Where B is the width of the rolled plate

k: strain resistance other than rolled material

Kt: Influence term due to tension in front and rear of rolling mill

Dp: coefficient of friction influence

R: working roll radius of rolling mill

H, h: mouth and exit plate thickness of rolling mill

As can be seen in Equation (1), the size of the rolling load is determined by the deformation resistance k, which is the physical property of the material to be rolled, as well as the rolling conditions such as the width of the rolling plate, the thickness of the rolling mill, and the working roll radius of the rolling mill.

The deformation resistance k is a value obtained by experiment as a physical quantity representing the strength of the material to be rolled, that is, the load per unit area required for plastic deformation of the material.

Therefore, as can be seen in Equation (1), the rolling load and the deformation resistance are directly proportional, so in order to accurately predict the rolling load, the deformation resistance of the material to be rolled must be accurately obtained.

The cold deformation resistance prediction formula used in the conventional setup model is used by formulating the measured value of the deformation resistance for a certain amount of plastic working, and the equation is exponentially increased as the plastic working amount, i.e., the strain ε increases. It has the same form.

Where l, m and n are the intrinsic constants of the material depending on the material to be rolled.

Therefore, to obtain the cold strain resistance prediction equation of the material to be rolled is the same as to find the material constant l, m, n of the formula (3).

Here, a brief description of a method for obtaining the cold deformation resistance prediction constant used in the conventional method is as follows.

The cold deformation resistance of metal materials can be calculated by rolling-tensile test, circumferential pressure test, flat plate compression test, torsion test, and bending test method. Tensile test method.

In this method, ten pieces of material cut out from the same base metal are respectively rolled at regular intervals of rolling reduction, and then the tensile test piece is removed from the rolled plate. And each of them is measured the tensile strength through a tensile test.

The tensile strength of the material thus obtained is obtained by obtaining the deformation resistance corresponding to the rolling reduction rate at the time of rolling because the specimen has already been subjected to work hardening by rolling.

Therefore, when these strain resistances are plotted on the X-Y graph so as to correspond to the initial rolling amount of each test piece, the strain resistance measurement of the material can be obtained, and a representative function that can satisfy all of these measurements. In other words, a deformation resistance prediction equation of the form as shown in Equation (3) is obtained.

In general, a rare analysis method is used for deriving a function using experimental values, and the formula (3) uses a regression analysis method because the deformation resistance increases in the form of an exponential function according to the strain rate.

FIG. 1 is an example showing a process of plotting strain resistance for each reduction ratio obtained by a conventional rolling-tension method and making them into one prediction equation.

However, since the cold deformation resistance of the metal material obtained by such a conventional method is actually different from the experimental conditions in which the value is used, many errors in the calculation of the rolling load are obtained by directly using the value obtained by the experiment. Will be generated.

Because the existing cold strain resistance measurement experiment was carried out at room temperature, the actual rolling is due to the large load acting between the upper and lower work rolls (1) (1a) between the rolling plate 3 of the material as shown in Figure 2a This is because a large amount of heat is generated, which causes the temperature of the rolled plate to rise to about 200 ° C.

Figure 2b shows the actual temperature change of the rolling plate during continuous cold rolling, the tensile test at a relatively high temperature of about 200 ℃ as a result of measuring the temperature of the rolling plate exiting the rolling mill cold deformation based on the measured data here It is reasonable to find a resistance formula.

In the present invention, in calculating the rolling load in the setup model of cold rolling, the measured value of the deformation resistance of the metal material to be reflected is used without using the value measured at room temperature, and according to the actual rolling temperature. The purpose is to overcome the errors of rolling load calculation in the conventional method and to calculate a more accurate rolling load to produce a good quality product.

1 is a deformation resistance curve diagram of a metal material obtained by a conventional rolling-tension test method;

Figure 2a is a schematic diagram showing the temperature of the rolling plate that changes as the rolling proceeds during the normal continuous cold rolling, the continuous cold rolling mechanism consisting of five rolling mills,

Figure 2b is a schematic diagram showing the temperature of the rolling plate changes as the rolling progresses in the normal continuous cold rolling, a graph showing the temperature change of the rolling plate during continuous cold rolling,

3 is a configuration diagram of a test apparatus for measuring deformation resistance at high temperature;

4 is a graph comparing the experimental and actual values of strain resistance for BO4CZ steels;

5 is a comparative graph of the rolling load prediction value and the measured value showing the effect of the present invention.

<Explanation of symbols for the main parts of the drawings>

1: upper work roll 1a: lower work roll

2: upper reinforcement roll 2a: lower reinforcement roll

3: rolled sheet 4: tensile tester

5: tensile test piece 6: upper jaw of tensile tester

6a: lower jaw of tensile tester 7: heating furnace

8: thermocouple 9: temperature measuring device

10: temperature controller

In order to achieve the above object, the present invention, in the control of the rolling mill using an AGC system to which a conventional setup model is applied, so that the error of the plate thickness to be rolled in the cold rolling process is within the allowable range,

After measuring the temperature of the rolling plate exiting the rolling mill during the continuous continuous cold rolling, the rolling-tension test method was carried out while the measured temperature was maintained, and then the strain resistance measurement of the corresponding material resulted from the test result was used. Obtaining a deformation resistance prediction equation represented by Equation (3) through regression analysis;

(Where k is strain resistance

1, m, n: intrinsic constant of the material depends on the material to be rolled

ε: strain):

On the AGC system, applying the strain resistance prediction equation obtained as described above to the rolling load prediction equation represented by the following equation (1),

(B, width of the rolled sheet

k: strain resistance of rolled material

Kt: Influence term due to tension in front and rear of rolling mill

Dp: coefficient of friction

R: working roll radius of rolling mill

H, h: the mouth, exit plate thickness of the rolling mill); characterized in that it comprises a.

EMBODIMENT OF THE INVENTION Hereinafter, this invention is demonstrated with reference to drawings.

Figure 3 is a schematic diagram showing a high temperature strain resistance measuring apparatus used in the present invention.

After the tensile test piece (5) is attached to a conventional tensile tester (4), a heating furnace (7) covering the tensile test piece (5) and the entire jaws (6) (6a) of the tensile tester is installed.

The installed heating furnace (7) is a thermostat (8) so that the temperature measured by the thermocouple (8) and the temperature measuring device attached to the tensile test piece (5) can be maintained at a desired test temperature of 200 ℃ To adjust the temperature appropriately.

Using the experimental apparatus configured to enable high temperature tensile test as described above, when the tensile test is performed while maintaining the temperature close to the actual rolling temperature, the deformation resistance of the rolled sheet can be measured. The measured strain resistance, like the conventional method, is used to predict the deformation resistance that can be used in actual rolling through the multiple regression analysis.

Thus, the accuracy of rolling load calculation becomes high by using the deformation resistance prediction formula measured at high temperature in rolling load formula (1). As the accuracy of rolling load calculation increases, accurate calculation of the plasticity coefficient M used for the control in the AGC system becomes possible, thereby enabling accurate control of the upper and lower roll caps, thereby reducing the thickness variation of the rolled plate. It becomes possible.

Hereinafter, the present invention will be described in detail with reference to the following Examples.

(Example)

The cooling strain resistance prediction equation presented by the present invention was obtained at a comparatively high temperature of 200 ° C. and used in calculating the rolling load. Figure 4 shows the strain resistance measured at room temperature and 200 ℃ for the steel species called BO4CZ which is typically produced in cold rolling mills with the strain rate and the formula for predicting the strain resistance obtained by multiple regression analysis Marked on

In addition, in order to examine the accuracy of the strain resistance prediction equation obtained from the previous experiment in the same figure, the strain resistance value obtained from the actual rolling is marked as 'ε'.

The method of calculating the strain resistance from the actual rolling is obtained by inverting Equation (1) on the basis of the rolling load measured from the load cell during the rolling of the steel sheet.

From Figure 4 it can be seen that the strain resistance measured at a high temperature of 200 ℃ is about 10 to 15% lower than the strain resistance measured at room temperature. In addition, it can be seen that the deformation resistance in actual rolling is in good agreement with the deformation resistance measured at high temperature.

Therefore, in the case of cold rolling in which the processing is performed under high load conditions, it can be seen that accurate calculation is possible in predicting the actual rolling load by using the strain resistance measured under the same conditions as the rolling temperature.

On the other hand, 5 degrees is a comparison between the rolling load calculated from the setup model and the actual rolling load before rolling, and the rolling load calculated using the newly obtained strain resistance prediction equation matches the actual rolling load and the rolling load prediction method is conventional. It can be seen that the rolling load calculation can be more accurate than the method.

In the present invention as described above, in obtaining the rolling load in the setup model of cold rolling, the measured value at the actual rolling temperature of about 200 ° C. is used without using the value measured at room temperature in the deformation resistance value of the reflected metal material. As it is used, there is an effect of overcoming the error of the rolling load calculation in the conventional method and calculating a more accurate rolling load to produce a good quality product.

Claims (1)

In controlling the rolling mill using the AGC system to which the usual setup model is applied, the error of the plate thickness rolled in the cold rolling process is within the allowable range. After measuring the temperature of the rolling plate exiting the rolling mill during the continuous continuous cold rolling, the rolling-tension test method was carried out while the measured temperature was maintained, and then the strain resistance measurement of the corresponding material resulted from the test result was used. Obtaining the deformation resistance prediction equation represented by the following equation (3) through the regression analysis method.

(Where k is strain resistance l, m, n: Intrinsic constant of material that depends on the material to be rolled ε: strain): On the AGC system, applying the strain resistance prediction equation obtained as described above to the rolling load prediction equation represented by Equation (1) below.

(B, width of the rolled sheet k: strain resistance of rolled material Kt: Influence term due to tension in front and rear of rolling mill Dp: coefficient of friction influence R: working roll radius of rolling mill H, h: mouth and exit plate thickness of rolling mill): Rolling load prediction method in cold rolling, characterized in that configured to include.

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