KR100711402B1 - Method for predicting of deformation resistance in heavy plate rolling process - Google Patents

Abstract

The present invention provides a simple hot deformation resistance for rolling load prediction by using process factors such as material thickness, temperature, and component, strain and strain rate in a reversible breadboard / rolling equipment during thick steel plate rolling process. The present invention relates to a method of predicting hot deformation resistance in a thick steel sheet rolling process that can be more accurately predicted through a process.

      The hot deformation resistance prediction method in the thick steel plate rolling process of the present invention uses the measured rolling load measured throughout the rolling pass schedule, and calculates the cumulative reduction rate according to the phenomenon that the strain of the previous pass accumulates in the current pass during rolling. In order to take into account, the constant index term of strain, strain rate is represented by functions expressed as composition function, temperature, etc. It is possible to achieve the improvement of the rolling load predictive power on-line by improving the accuracy of the setting of the rolling load by using the actual measurement value which is more reliable than the experiment, and reducing the necessity of calculating the cumulative pressure reduction rate.

 Thick steel plate, rolling process, hot deformation resistance, prediction

Description

Prediction method of hot deformation resistance in thick steel plate rolling process {METHOD FOR PREDICTING OF DEFORMATION RESISTANCE IN HEAVY PLATE ROLLING PROCESS}

1 is a block diagram of a rolling control system of a thick steel plate rolling process for carrying out the present invention.

2 is a flowchart showing a method for predicting hot deformation resistance in a thick steel sheet rolling process according to the present invention.

Figure 3 is a rolling load hit ratio chart of the present invention and conventional steel.

Figure 4 is a graph of measurement load-predictive load correlation between the present invention and conventional steel.

Explanation of symbols on the main parts of the drawings

100: control unit

200: rolling load regulator

300: thick steel plate rolling mill

The present invention relates to a method for predicting hot deformation resistance applied to a thick steel sheet rolling process of a steel mill, and in particular, in a reversible type rolling / rolling length rolling facility during a thick steel sheet rolling process, a simple process for the hot deformation resistance required for rolling load prediction is required. By implementing to more accurately predict through the present invention, it is possible to reduce the time and cost of measuring the deformation resistance of the material, and to predict the method of hot deformation resistance in the thick steel rolling process to reduce the learning dependence of the rolling load during actual operation will be.

In general, the surface, shape and mechanical properties of the material being hot rolled are greatly influenced by hot processing parameters such as deformation temperature, amount of deformation and speed of deformation, deformation resistance and the like. Among these hot processing variables, deformation resistance means flow stress due to deformation when plastic deformation of the material, which is important for determining the reduction ratio for each pass by determining how much high temperature deformation is possible in the mechanical and electrical capacity of the facility. It becomes data.

On the other hand, in the hot rolling process, the thickness of the material becomes an important management goal, and making the desired thickness is one of the main functions of the length rolling process. At this time, predicting the rolling load is used to set the roll gap in order to produce a rolled steel sheet of the target thickness, the rolling load is in proportion to the deformation resistance, it is possible to predict the rolling load by predicting the deformation resistance. Accordingly, in order to predict the accurate rolling load, it is necessary to predict the accurate deformation resistance.

Conventionally, in thick steel plate rolling, the thickness correction term is based on the reference strength value (Kref), the temperature function (C _T ), the rolling speed function (C _V ), the reduction ratio (r), and the roll flat radius function (Lrf) based on the general steel. Rolling load (F) was set using the following equation (1) multiplied by (C _H ).

On the other hand, the accuracy of the mathematical model for determining the rolling load is poor due to the change in steel grade and various rolling conditions of high grade steels (API materials, TMCP materials), and the situation of using excessive learning coefficients to compensate for this.

However, when the length of the rolled material is relatively short and the thickness is thick, since the number of rolling load measurement data collected according to the same length interval is small, it is difficult to secure the reliability of the measured average value, and when recalibrating the rolling load learning coefficient based on these measurements The deviation between the set rolling load and the measured rolling load does not decrease, which causes the increase in the number of passes due to the manual intervention by the operator.

Accordingly, in order to predict the setting accuracy of the rolling load close to the actual load, a technique capable of considering the change in deformation resistance and the change in rolling conditions of various materials is required.

However, in the conventional method as described above, the deformation resistance curve of ordinary steel is described simply by adjusting only the reference strength value to the deformation resistance curve of general steel, which affects the deformation resistance curve of elements other than carbon (C). There is a problem in that it is ignored.

In addition, the existing technique for predicting the deformation resistance in the thick steel sheet rolling from the relationship between the high temperature flow stress and the strain rate predicts the deformation resistance by simply reducing the rolling strain in the rolling section, so that the strain accumulation phenomenon occurs between the rolling passes due to the low rolling temperature. In this case, there is a problem that it is difficult to accurately predict the rolling load.

In addition, it is almost impossible to verify the validity of the various equations used to calculate the cumulative strain rate online to calculate the cumulative strain due to the strain accumulation occurred during rolling, there is a problem that takes a long time to calculate this.

The present invention has been proposed to solve the above problems, the object of which is, in the reversible width rolling mill / length rolling equipment during the thick steel sheet rolling process, process factors, such as the thickness, temperature and components of the material, strain and strain By using the speed, it is possible to more accurately predict the hot deformation resistance required for rolling load prediction through a simple process, thereby reducing the time and cost of measuring the deformation resistance of the material, and reducing the learning dependence of the rolling load during actual operation. The present invention provides a method for predicting hot deformation resistance in a thick steel sheet rolling process.

In order to achieve the above object of the present invention, the hot deformation resistance prediction method in the thick steel plate rolling process of the present invention, the reduction factor (r), the coefficient of friction (μ) which is an effective factor related to the geometric change of the thick plate rolling conditions And a first step of obtaining a reduction force function Qp through Equation 2 using the aspect ratio s; Substituting the reduction force function (Qp), the predetermined contact projection length (L sub d), the average sheet width (Wm) and the measured actual rolling load (F (actual)) obtained in the first step into Equation 3 Obtaining a measured strain resistance (Km (actual)) by a second step; The measured strain resistance (Km (actual)), strain (ε), strain rate (DOT ε) and rolling temperature (T) obtained in the second step is substituted into Equation 4 and then statistically analyzed, Obtaining a coefficient at; And a fourth step of obtaining a hot deformation resistance Km by substituting the coefficient of Equation 4 and the equation of Misaka into Equation 5 below.

The method of predicting hot deformation resistance at the time of rolling the thick steel sheet further includes a fifth step of predicting the hot rolling load (F) by substituting the hot deformation resistance (Km) obtained in the fourth step into Equation 6 below. It is done.

Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. In the drawings referred to in the present invention, components having substantially the same configuration and function will use the same reference numerals.

1 is a block diagram of a rolling control system of a thick steel plate rolling process for carrying out the present invention.

Referring to Figure 1, the rolling control system of the thick steel plate rolling process for carrying out the present invention, the control unit 100 to predict the hot deformation resistance (Km) according to the present invention to obtain the rolling load (F) to control the rolling And, the rolling load regulator 200 for adjusting the rolling load according to the rolling control of the control unit 100, and the rolling load is adjusted by the rolling load regulator 200, the thick steel plate rolling mill 300 to perform a thick steel plate rolling )

2 is a flowchart showing a method for predicting hot deformation resistance in a thick steel sheet rolling process according to the present invention. S100 is a first step of obtaining a reduction force function Qp through Equation 2 below using a reduction factor r, a friction coefficient μ and a shape ratio s, which are effective factors related to the geometrical change of the thick plate rolling condition. to be. S200, the reduction force function (Qp) obtained in the first step, the predetermined contact projection length (L sub d), the average plate width (Wm) and the measured actual rolling load (F (actual)) Substituting 3, the second step is to find the actual deformation resistance Km (actual). S300, by substituting the measured strain resistance (Km (actual)), strain (ε), strain rate (DOT ε) and rolling temperature (T) obtained in the second step into the following equation 4 and statistical analysis, A third step of obtaining coefficients in Equation (4). S400 is a fourth step of obtaining a hot deformation resistance Km by substituting the coefficient of Equation 4 and the equation of Misaka into Equation 5 below.

Figure 3 is a rolling load hit ratio chart of the present invention and conventional steel, Figure 4 is a measurement load-predictive load correlation graph of the present invention and conventional steel.

3 and 4, it can be seen that according to the method of the present invention, the rolling load hit ratio is much higher than in the related art.

Hereinafter, the operation and effects of the present invention will be described in detail with reference to the accompanying drawings.

1 to 4, first, in the first step (S100), using the reduction factor (r), the friction coefficient (μ) and the shape ratio (s) which are effective factors related to the geometric change of the thick plate rolling conditions The reduction force function Qp is obtained through Equation 2.

이고, 여기서, Hin은 압연패스의 입측 두께이고, R은 압연롤의 반지름이다. 상기 압하율(r)은

이고, 여기서, Hout는 압연패스의 출측 두께이다. 상기 마찰계수(μ)는 μ = 0.84-0.0005T이고, 여기서, T는 압연패스의 입측평균온도이다. 또한,

는 압하력 P 함수를 의미하고, a는 하기의 다른 수학식에 사용되는 a와 구분되어 사전에 설정되는 상수이고, b와 c는 상수가 아닌 r과 u로 이루어진 함수이다. 그리고, 압하력 함수내에 사용된 a,b,c는 s로 표현된 압하력함수의 2차계수항, 1차계수항, 상수항을 나타내기 위해 사용된 것입니다. In Equation 2, the shape ratio s is

Where Hin is the side thickness of the rolling pass and R is the radius of the rolling roll. The reduction ratio (r) is

Where Hout is the exit thickness of the rolling pass. The coefficient of friction (μ) is μ = 0.84-0.0005T, where T is the measured average temperature of the rolling pass. Also,

Denotes a reduction force P function, a is a constant which is set in advance by being separated from a used in other equations below, and b and c are functions of r and u, not constants. And, a, b, c used in the reduction function is used to represent the quadratic coefficient, the first coefficient term, and the constant term of the reduction function expressed as s.

Next, in the second step (S200), the reduction force function (Qp) obtained in the first step (S100), the predetermined contact projection length (L sub d), the average plate width (Wm) and the measured performance rolling load Substituting (F (actual)) into Equation 3 below, the actual strain resistance Km (actual) is obtained.

)는

이다. 상기 평균판폭(Wm)은

이고, 여기서, Win은 압연패스의 입력판폭이고, Wout는 압연패스의 출측판폭이다. 상기 실적 압연하중(F(actual))은 로드셀(Load Cell)등의 하중계로부터 측정된다.Equation 3 is derived from Equation 6 to obtain a hot rolling load (F), and in Equation 3, the contact projection length (

)

to be. The average plate width (Wm)

Where Win is the input plate width of the rolling pass and Wout is the exit plate width of the rolling pass. The actual rolling load F (actual) is measured from a load meter such as a load cell.

)의 각 계수를 구한다.Next, in the third step (S300), the measured deformation resistance (Km (actual)), the strain rate (ε), the strain rate (DOT ε) and the rolling temperature (T) obtained in the second step is expressed by Equation 4 below. After substituting the data, statistical analysis was performed to determine the constant (a, Q), strain index function (m), strain rate index (n), and composition function according to class of steel group.

Find each coefficient of

이고, 여기서, δ는

이다. 상기 변형율속도(DOT ε)는

이고, 여기서, V는 압연속도이고, θ는 판과 롤의 접촉각(contact angle)으로

로 정의된다. 또한, m은 변형율 지수 함수로서

으로 정의되고, n은 변형유속도 지수함수로서,

로 정의된다. 상기

는 강종그룹별 분류에 따른 조성함수이다.In Equation 4, the strain ε is

Where δ is

to be. The strain rate (DOT ε) is

Where V is the rolling speed and θ is the contact angle of the plate and the roll

Is defined as M is also a strain index function

N is the strain velocity index function,

Is defined as remind

Is the composition function according to the classification of steel group.

및 기설정된 미사카의 식(

)을 하기 수학식 5에 대입하여 열간변형저항(Km)을 구한다.
상기 수학식 4에서,

은 변형율 지수함수 m 함수를 의미하고,

은 변형율 속도 지수함수 n 함수를 의미하며,

과

식내 포함된

과

중의 계수 m과 n은 각각

및

식에 사용된다는 것을 표현하기 위해 나타낸 것이다.Next, in the fourth step S400, each of the coefficients a, Q, m, n and

And the preset formula of Misaka (

) Is substituted into the following Equation 5 to obtain the hot deformation resistance (Km).
In Equation 4,

Denotes the strain index function m,

Is the strain rate exponential function n function,

and

Included in the meal

and

Coefficients m and n are respectively

And

It is used to express that it is used in an expression.

는

이고, 여기서, f는 예를 들어, f=0.963+0.161Mn+4.388Nb+0.86V+4.03Ti+0.29Mo+0.022Ni+0.028Cr)이다. 그리고, C,Si,Mn,P,S,Cu,Nb,V,Ni,Mo,Cr,Al,Ti는 후강판에 사용되는 조정(weight percent)을 보이고 있다.here,

Is

Where f is, for example, f = 0.963 + 0.161Mn + 4.388Nb + 0.86V + 4.03Ti + 0.29Mo + 0.022Ni + 0.028Cr). In addition, C, Si, Mn, P, S, Cu, Nb, V, Ni, Mo, Cr, Al, Ti show a weight percent used for thick steel sheets.

On the other hand, according to the method of predicting the hot deformation resistance during the rolling of the thick steel sheet of the present invention, the hot rolling load (F) can be predicted by substituting the hot deformation resistance (Km) obtained in the fourth step into Equation 6 below.

On the other hand, the equation for predicting the average strain resistance depending on the strain (ε), strain rate (DOT ε) and rolling temperature (T) is as shown in Equation 7.

here,

σ = flow stress,

Km: average strain resistance,

T: rolling temperature (° C.),

At this time, if Equation 7 is summarized as a function of composition and temperature, Equation 5 is obtained.

Referring to Figure 3, it can be seen that the rolling load hit ratio according to the present invention is significantly higher than in the prior art. Here, the rolling load hit ratio for each steel type is obtained by classifying the compositional functions and constants described by the steel type group, and the difference between the rolling load prediction value and the rolling load actual value calculated using the predicted deformation resistance is calculated as the rolling load actual value. The percentage of division is defined as the error rate.

4A and 4B, FIG. 4A is a conventional predicted load-measured load correlation graph, and FIG. 4B is a predicted load-measured load correlation graph according to the present invention.

4A and 4B, it can be seen that according to the method of the present invention, the predicted load is significantly proportional to the measured load compared to the conventional method, which is also shown in FIG. 3 according to the present invention. That means the hit rate is high.

The present invention described above is not limited to the above-described embodiments and the accompanying drawings, but is defined by the claims, and the apparatus of the present invention may be substituted, modified, and modified in various ways without departing from the spirit of the present invention. It is apparent to those skilled in the art that modifications are possible.

According to the present invention as described above, in the method of predicting the hot deformation resistance applied to the thick steel plate rolling process of steel mill, in the reversible breadboard rolling / lengthening rolling equipment during the thick steel plate rolling process, the thickness, temperature and components of the material, etc. By using the process factor, strain rate and strain rate of, by implementing a simple process to more accurately predict the hot deformation resistance required for rolling load prediction, it is possible to reduce the time and cost of measuring the deformation resistance of the material, The learning dependence of the rolling load can be reduced, more precise thickness control is possible, and the effect of improving the error rate and securing the operation stability can be obtained.

Claims (2)

A first step of obtaining a reduction force function (Qp) through Equation (2) using a reduction factor (r), a friction coefficient (μ), and a shape ratio (s) which are effective factors related to the geometrical change of the thick plate rolling condition; [Equation 2]

Substituting the reduction force function (Qp), the predetermined contact projection length (L sub d), the average plate width (Wm) and the measured actual rolling load (F (actual)) obtained in the first step into the following equation (3): Obtaining a measured strain resistance (Km (actual)) by a second step; [Equation 3]

The measured deformation resistance (Km (actual)), strain (ε), strain rate (DOT ε) and rolling temperature (T) obtained in the second step were substituted into the following Equation 4, and statistical analysis was performed. Obtaining a coefficient; And [Equation 4]

The coefficient of Equation 4 and the formula of preset Misaka (

Step 4) to obtain the hot deformation resistance (Km) by substituting [Equation 5]

Hot deformation resistance prediction method during thick steel sheet rolling comprising a. The method of claim 1, wherein the method of predicting hot deformation resistance during rolling of the thick steel sheet A fifth step of predicting the hot rolling load F by substituting the hot strain resistance Km obtained in the fourth step into Equation 6 below; [Equation 6]

Hot deformation resistance prediction method when the thick steel sheet further comprises a.

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