A method for obtaining the plate shape control efficiency based on digital twin of cold rolling process

Abstract

The present invention provides a method for obtaining the plate shape control efficiency based on the digital twin of the cold rolling process, with the goal of obtaining a plate shape control efficiency coefficient that is more in line with actual rolling conditions by considering the influencing factors of the thermal convexity of the working rolls. The thermal boundary conditions of the circumference of the rolling rolls are set, and the thermal convexity of the working rolls is predicted by establishing a finite element model of the temperature field of the cold rolling working rolls. The thermal convexity of the working rolls is coupled with the finite element model to construct a digital twin of the cold rolling process, and the strip outlet thickness and the relative length difference of the longitudinal fiber strips of the strip are simulated when different plate shape adjustment mechanisms such as working roll bending, intermediate roll bending, and intermediate roll transverse movement participate in the regulation. The control efficiency coefficient curve of each plate shape control actuator is calculated and obtained. The control efficiency coefficient curve obtained by the method of the present invention taking into account the influence of the thermal convexity of the working rolls is closer to the actual production conditions, which has practical significance for improving the plate shape control efficiency and improving the quality of cold-rolled strip products at the cold rolling site.

Description

Technical Field

[0001] The present invention belongs to the technical field of cold rolling and relates to a method for obtaining plate shape control efficiency based on a digital twin of a cold rolling process. Background Art

[0002] Cold-rolled sheets and strips are widely used in industries such as automobile manufacturing, aviation, home appliances, and food packaging. Deeply processed, high-quality, and high-value-added strip products are increasingly favored by producers and consumers. However, these downstream industries have increasingly higher requirements for the geometric shape and dimensional accuracy of strip steel. Therefore, it is very necessary to improve the calculation accuracy of cold-rolled plate shape.

[0003] Modern advanced cold rolling mills usually have a variety of plate shape adjustment methods, such as working roll bending, intermediate roll bending, and intermediate roll transverse movement. In actual production, it is necessary to comprehensively utilize various plate shape adjustment methods and achieve the goal of eliminating plate shape deviation through the mutual coordination of adjustment effects. The plate shape control model currently used in the field begins to quantitatively describe the performance of various plate shape adjustment methods with the control efficiency coefficient. As the basis of the multivariable optimal plate shape closed-loop feedback control algorithm, the control efficiency coefficient can comprehensively consider the control effect of a single actuator on the plate shape, describe the control characteristics of different actuators, and achieve high-precision control of the cold-rolled plate shape. The current methods for obtaining the control efficiency coefficient are mainly finite element method and data-driven algorithm. Since it is very difficult to obtain the changes in the thermal crown of the working roll online on site, the existing plate shape control efficiency coefficients obtained do not consider the influence of the thermal crown of the working roll, resulting in the obtained control efficiency coefficient not being consistent with the actual working conditions, affecting the control accuracy. Summary of the Invention

[0004] In order to solve the above technical problems, the purpose of the present invention is to provide a method for obtaining plate shape control efficiency based on the digital twin of the cold rolling process. The control efficiency coefficient curve obtained by considering the influence of the thermal convexity of the working roll is closer to the actual production conditions, which has practical significance for improving the plate shape control efficiency at the cold rolling site and improving the quality of cold-rolled strip products.

[0005] The present invention provides a method for obtaining the plate shape control efficiency based on a digital twin of a cold rolling process, comprising:

[0006] Step 1: Collect the cold rolling mill size parameters, production process parameters, emulsion property parameters, and strip and roll material parameters;

[0007] Step 2: Establish a finite element model of the temperature field of the cold rolling work roll based on the parameters collected in step 1, and calculate the transverse temperature distribution data of the work roll temperature field in the rolling stability stage;

[0008] Step 3: Verify the accuracy of the cold rolling work roll temperature field finite element model by comparing the temperature distribution data;

[0009] Step 4: Use the transverse temperature distribution data obtained in step 2 as a thermal load to achieve thermal-structural coupling between the work roll geometric model and the cold rolling work roll temperature field finite element model, and obtain the work roll thermal crown results in the rolling stability stage;

[0010] Step 5: Based on the production process parameters, rolling mill dimensional parameters, and work roll thermal crown collected on-site in Step 1, a digital twin of the cold rolling process is established, and the cold rolling process is simulated and calculated;

[0011] Step 6: Compare the on-site measured rolling force data and strip exit thickness data with the simulated data in Step 5 to verify the accuracy of the cold rolling process digital twin;

[0012] Step 7: Within the maximum range of the production process parameter settings, the parameter settings of the work roll bending force, the intermediate roll bending force, and the intermediate roll lateral displacement are changed respectively to obtain the distribution of the strip outlet thickness and the distribution of the relative length difference of the longitudinal fiber strips of the strip under different strip shape control actuators;

[0013] Step 8: Based on the definition of the plate shape control efficiency coefficient and the length difference data of the rolled strip fiber strip when different plate shape adjustment mechanisms are put into use, the control efficiency coefficient curves of the working roll bending force, the intermediate roll bending force and the intermediate roll lateral displacement are calculated and obtained, which quantitatively describes the control ability of various plate shape adjustment mechanisms on the plate shape, thereby accurately controlling the plate shape.

[0014] In the method for obtaining the plate shape control effect based on the digital twin of the cold rolling process of the present invention:

[0015] (1) Cold rolling mill size parameters include:

[0016] Dimensional parameters of support roll, intermediate roll and working roll;

[0017] (2) Production process parameters include:

[0018] The friction coefficient between the work roll and the strip is μ, dimensionless;

[0019] Initial thickness of strip before rolling h in , mm;

[0020] Strip thickness after rolling h out , mm;

[0021] Strip width W0, mm;

[0022] Rolling reduction ratio ε, dimensionless;

[0023] The front tension and back tension σ of the rolling process b ,σ f , MPa;

[0024] Roller radius R, mm;

[0025] Rolling speed v R , m / min;

[0026] Strip deformation resistance σ s , MPa;

[0027] Working roll bending force, kN;

[0028] Intermediate roll bending force, kN;

[0029] Intermediate roller lateral displacement, mm;

[0030] Rolling force P, kN;

[0031] Working roll initial temperature T w0 , ℃;

[0032] (3) Emulsion property parameters include:

[0033] Emulsion concentration C, dimensionless;

[0034] Emulsion initial temperature T e0 , ℃;

[0035] Heat transfer coefficient h between emulsion and working roll in strong water cooling zone str , W·(m·K) -1 ;

[0036] Heat transfer coefficient h between emulsion and working roll in weak water cooling zone weak , W·(m·K) -1 ;

[0037] (4) Strip and roll material parameters include:

[0038] Strip and work roll density ρ s ,ρ w , kg·m -3 ;

[0039] Elastic modulus E of strip steel and working roll s ,E w , GPa;

[0040] Poisson's ratio ν between strip and work roll s ,ν w , dimensionless;

[0041] Working roll specific heat capacity C w , J·(kg·℃) -1

[0042] Thermal conductivity of working roll h w , W·(m·K) -1 .

[0043] In the method for obtaining the plate shape control efficiency based on the digital twin of the cold rolling process of the present invention, step 2 is specifically as follows:

[0044] Step 2.1: Establish a work roll geometric model based on the work roll size parameters;

[0045] Step 2.2: Determine the material properties of the work roll temperature field model based on the collected roll material parameters;

[0046] Step 2.3: Simplify the thermal boundary conditions of the work roll temperature field. Calculate the thermal boundary conditions based on the heat exchange during the rolling process and the emulsion properties, and apply them to the work roll geometric model.

[0047] Step 2.4: Use edge refinement to mesh the work roll geometry model;

[0048] Step 2.5: Set the solution conditions and establish the finite element model of the temperature field of the cold rolling work roll;

[0049] Step 2.6: Use the finite element model of the cold rolling work roll temperature field established in step 2.5 to calculate the temperature distribution change of the work roll during the rolling process;

[0050] Step 2.7: Use post-processing software to extract the transverse temperature distribution data of the working roll temperature field during the rolling stability stage in step 2.6.

[0051] In the method for obtaining the plate shape control efficiency based on the cold rolling process digital twin of the present invention, step 3 is specifically as follows:

[0052] The transverse temperature distribution data was compared with the temperature distribution data measured on site within five minutes after the work rolls were taken off the mill to verify the accuracy of the finite element model of the temperature field of the cold rolling work rolls.

[0053] In the method for obtaining the plate shape control efficacy based on the cold rolling process digital twin of the present invention, step 5 is specifically as follows:

[0054] Step 5.1: Set the material properties of the cold rolling process digital twin based on the collected strip and roll material parameters;

[0055] Step 5.2: Establish and solve the cold rolling process digital twin based on the measured production process parameters, rolling mill dimensional parameters, and the work roll thermal crown results obtained in the rolling stability stage in step 4;

[0056] Step 5.3: Use Ls-Prepost post-processing software to extract the calculation results of rolling force and strip thickness during the rolling process.

[0057] In the method for obtaining the plate shape control efficiency based on the cold rolling process digital twin of the present invention, step 6 is specifically as follows:

[0058] Step 6.1: Compare the measured rolling force, theoretically calculated rolling force, and measured strip exit center thickness with the rolling force and strip exit thickness calculated in step 5 to verify the accuracy of the cold rolling process digital twin.

[0059] Step 6.2: Compare the transverse thickness distribution data of the strip after rolling obtained by simulation calculation in step 5 with the actual measured strip thickness distribution data after rolling to further verify the accuracy of the digital twin of the cold rolling process.

[0060] In the method for obtaining the plate shape control efficiency based on the cold rolling process digital twin of the present invention, the expression of the plate shape control efficiency coefficient in step 8 is:

[0061]

[0062] Among them, eff i (y i ) is the plate shape control efficiency coefficient, IU(y i ) is the change in plate shape deviation, Δu i The adjustment amount of the plate shape control actuator;

[0063]

[0064] Among them, y i is the coordinate of a certain position along the width of the strip, L(y i ) is the coordinate y i The length of the longitudinal fiber strip at is the average length of the longitudinal fiber strips in the strip.

[0065] The present invention provides a method for obtaining the plate shape control efficiency based on the digital twin of the cold rolling process, which has at least the following beneficial effects:

[0066] Based on the basic principles of tribology and heat transfer, this paper establishes the circumferential temperature boundary conditions of the working rolls during the cold rolling process. Using ANSYS, a finite element model of the temperature field of the cold rolling working rolls is established, which in turn yields the axial distribution of the working roll thermal expansion. Finite element theory is used to establish a three-dimensional temperature field-roll system-strip coupled digital twin of the cold rolling process. The accuracy of this digital twin is improved through interactive verification using a large amount of field-measured data. Based on the definition of the plate shape control efficiency coefficient, the control efficiency coefficient curves for the working roll bending force, intermediate roll bending force, and intermediate roll lateral displacement are calculated and obtained, quantitatively describing the plate shape control capabilities of various plate shape adjustment mechanisms, thereby enabling precise control of the plate shape. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1This is a schematic diagram of the heat exchange area division along the circumference of the work roll in this embodiment;

[0068] Figure 2 This is the mesh division of the finite element model of the working roll temperature field in this implementation scheme;

[0069] Figure 3 This is a real picture of the temperature field distribution of the working roll in this embodiment taken by a thermal imager;

[0070] Figure 4 This is a comparison diagram of the measured value of the working roll temperature field and the simulation result in this embodiment;

[0071] Figure 5 The results of the thermal expansion of the working roll in this embodiment changing with time;

[0072] Figure 6 This is the mesh division of the digital twin of the cold rolling process in this implementation plan;

[0073] Figure 7 This is a comparison chart of the measured values ​​of the strip thickness distribution along the width direction and the simulation calculation results in this embodiment;

[0074] Figure 8a The variation of strip outlet thickness with different working roll bending force setting values;

[0075] Figure 8b The variation of strip outlet thickness with different set values ​​of the intermediate roller lateral displacement;

[0076] Figure 8c The variation of strip outlet thickness with different setting values ​​of the intermediate roll bending force;

[0077] Figure 9a The law of the change of the relative length difference of the longitudinal fiber strips with different setting values ​​of the working roll bending force;

[0078] Figure 9b The law of the relative length difference of the longitudinal fiber strips changes with the different set values ​​of the intermediate roller lateral displacement;

[0079] Figure 9c The law of the change of the relative length difference of the longitudinal fiber strips with different setting values ​​of the intermediate roll bending force;

[0080] Figure 10a 、 Figure 10b 、 Figure 10c : is the control efficiency coefficient curve of different plate shape adjustment mechanisms in this embodiment. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments. The specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0082] The core idea of ​​the present invention is: first, establish a finite element model of the temperature field of the cold rolling working roll of the cold rolling last stand, couple the thermal crown calculation results with the finite element model of the temperature field of the cold rolling working roll to construct a digital twin of the cold rolling process that conforms to the actual production characteristics of cold rolling, and then obtain the control efficiency coefficient curves of different plate shape adjustment mechanisms according to actual production conditions.

[0083] The following method for obtaining the plate shape control efficiency based on the digital twin of the cold rolling process of the present invention calculates the thermal crown of the working roll of the last stand of a 1450mm five-stand cold rolling production line of a domestic factory and obtains the control efficiency coefficient curve. The method specifically includes the following steps:

[0084] Step 1: Collect the cold rolling mill size parameters, production process parameters, emulsion property parameters, and strip and roll material parameters;

[0085] Tables 1, 2, 3 and 4 respectively show the mill size parameters, production process parameters, strip and roll material parameters and emulsion property parameters.

[0086] Table 1 Rolling mill size parameters

[0087] Working roll Intermediate roller Support roller Roller diameter / mm 425 490 1420 Roller neck diameter / mm 240 280 780 <![CDATA[ Roller Body length / mm]]> 1450 1440 1450 Roll neck length / mm 248 288 780

[0088] Table 2 Production process parameters

[0089]

[0090] Table 3 Strip steel and roll material parameter settings

[0091] Model parameters value Work roll linear expansion coefficient <![CDATA[1.2e -6 / ℃]]> <![CDATA[h w / W·(m·K) -1 ]]> 46.47 <![CDATA[T w0 / ℃]]> 30 <![CDATA[C w / J·(kg·℃) -1 ]]> 490 <![CDATA[ρ s ,ρ w / kg·m -3 ]]> 7850 <![CDATA[E s ,AND w / GPa ]]> 2.07 <![CDATA[ν s ,n w ]]> 0.3

[0092] Table 4 Emulsion property parameters

[0093] parameter value <![CDATA[T e0 / ℃]]> 55 C 5％ <![CDATA[h str / W·(m 2 ·K) -1 ]]> 4747 <![CDATA[h weak / W·(m 2 ·K) -1 ]]> 1300

[0094] Step 2: Based on the parameters collected in step 1, a finite element model of the temperature field of the cold rolling work roll is established, and the transverse temperature distribution data of the work roll temperature field in the rolling stability stage is calculated. The specific modeling process is as follows;

[0095] Step 2.1: Establish a work roll geometric model based on the work roll size parameters collected in Table 1;

[0096] Step 2.2: Determine the material properties of the work roll temperature field model based on the collected roll material parameters;

[0097] According to the roll material parameters collected in Table 3, the material parameters in the finite element model of the work roll temperature field are set, and the material properties of the work roll are set to isotropic linear elastic material;

[0098] Step 2.3: Simplify the thermal boundary conditions of the work roll temperature field based on the parameters collected in step 1. Calculate the thermal boundary conditions based on the heat exchange during the rolling process and the emulsion property parameters, and apply the thermal boundary conditions to the work roll geometric model.

[0099] The working roll is divided into 38 zones along the axial direction according to the number of cooling nozzles and coverage width; Figure 1 The circumferential area division of the working roll is as follows: According to the coverage of the emulsion and the contact between the working roll, the strip and the intermediate roll, the working roll is divided into eight heat exchange areas along the circumference. Among them, area A is the direct contact area between the strip and the roll; areas B, D, F, and H are weak water cooling areas; areas C and G are strong water cooling areas; and area E is the direct contact area between the working roll and the intermediate roll.

[0100] Step 2.4: Use edge refinement to mesh the working roll geometry model, such as Figure 2 As shown;

[0101] Step 2.5: Set the solution conditions and establish the finite element model of the temperature field of the cold rolling work roll;

[0102] Step 2.6: Use the finite element model of the cold rolling work roll temperature field established in step 2.5 to calculate the temperature distribution change of the work roll during the rolling process;

[0103] Step 2.7: Use post-processing software to extract the transverse temperature distribution data of the work roll temperature field during the rolling stability stage in step 2.6;

[0104] Step 3: Verify the accuracy of the cold rolling work roll temperature field finite element model by comparing the temperature distribution data;

[0105] During the specific implementation, the transverse temperature distribution data is compared with the temperature distribution data measured on site within five minutes after the work roll is taken off the machine to verify the accuracy of the finite element model of the temperature field of the cold rolling work roll.

[0106] Figure 3 This is the temperature distribution result of the working roll taken with a thermal imager. Figure 4 This is a comparison chart of the measured values ​​and simulation results of the work roll temperature field. The error is within 0.8℃, the temperature distribution trend is in good agreement, and the accuracy of the finite element model of the cold rolling work roll temperature field is high.

[0107] Step 4: Use the transverse temperature distribution data obtained in step 2 as thermal load to realize the thermal-structural coupling of the work roll geometric model and the cold rolling work roll temperature field finite element model, and obtain the work roll thermal expansion results at the rolling stability stage; the work roll thermal expansion distribution results at different rolling stages are as follows: Figure 5 shown.

[0108] Step 5: Based on the production process parameters, rolling mill dimensional parameters, and work roll thermal crown collected on-site in Step 1, a digital twin of the cold rolling process is established, and the cold rolling process is simulated and calculated;

[0109] The digital twin visualization interface is established as follows Figure 6 As shown in the figure, the rolling force and strip thickness calculation results of the rolling process are extracted using Ls-Prepost post-processing software.

[0110] Step 6: Compare the on-site measured rolling force data and strip exit thickness data with the simulated data calculated in Step 5 to verify the accuracy of the cold rolling process digital twin. Specifically, the following steps are performed:

[0111] Step 6.1: Compare the measured rolling force, theoretically calculated rolling force, and measured strip exit center thickness with the rolling force and strip exit thickness calculated in step 5 to verify the accuracy of the cold rolling process digital twin.

[0112] The rolling force and strip exit thickness calculated in step 5 are compared with the measured rolling force, theoretically calculated values, and measured strip thickness data, as shown in Table 5. The simulated rolling force and strip exit thickness calculation results are in good agreement with the measured data, with maximum errors of 4.29% and 1.3%, respectively.

[0113] Table 5 Comparison of measured and calculated values ​​of strip thickness and rolling force

[0114]

[0115] Among them, h ACT is the measured value of strip steel outlet thickness, h FEM is the simulation result of strip steel outlet thickness; ε ACT is the measured value of the reduction rate, ε FEM is the simulation result of reduction rate; P ACT is the measured value of rolling force, P HILL is the theoretical calculated value of rolling force, P FEM is the rolling force simulation value; δ(P ACT , P FEM ) represents the error between the measured value and the simulated value of rolling force, δ(h ACT , h FEM ) represents the error between the measured value of the strip outlet thickness and the simulation calculation result.

[0116] Step 6.2: Compare the transverse thickness distribution data of the strip after rolling obtained by simulation calculation in step 5 with the actual measured strip thickness distribution data after rolling to further verify the accuracy of the digital twin of the cold rolling process.

[0117] The distribution of strip thickness along the width direction extracted from step 5 is compared with the actual strip thickness distribution measured on site to further verify the accuracy. The comparison results are as follows: Figure 7 As shown in the figure, the simulated calculated values ​​of the thickness distribution results are consistent with the measured values.

[0118] Step 7: According to the maximum range of rolling process parameter settings, the parameters of work roll bending force (WRB), intermediate roll bending force (IRB) and intermediate roll lateral displacement (IRS) are changed respectively. The parameter settings are shown in Table 6. Then, the distribution of strip outlet thickness and the relative length difference of strip longitudinal fiber strips under different plate shape control actuators is obtained through cold rolling process digital twin simulation calculation. The change law of strip outlet thickness with different plate shape control actuator setting values ​​is shown in the figure below. Figures 8a-8c As shown in Figure 9a-9c, the change law of the relative length difference of the longitudinal fiber strips of the strip with the setting value of the actuator for different plate shape control is shown.

[0119] Table 6 Setting values ​​of plate shape adjustment mechanism

[0120]

[0121] Step 8: Based on the definition of the flatness control efficiency coefficient and the data on the difference in length of the post-rolling strip steel fibers when different flatness control mechanisms are in place, the control efficiency coefficient curves for the work roll bending force, intermediate roll bending force, and intermediate roll lateral displacement are calculated. These curves are then transplanted into the on-site flatness control model to minimize the residual deviation in the outlet strip shape, fully utilizing the mill's flatness control capability and improving flatness control accuracy. Figure 10 shows the control efficiency coefficient curves for different flatness control mechanisms.

[0122] The flatness control efficiency coefficient is defined as the change in the relative length difference of the longitudinal fiber strips of the strip under the unit adjustment of the control efficiency actuator. It is represented by IU in the actual flatness control system. The expression of IU is as follows:

[0123]

[0124] Among them, y i is the coordinate of a certain position along the width of the strip, L(y i ) is the coordinate y i The length of the longitudinal fiber strip at is the average length of the longitudinal fiber strips in the strip.

[0125] Therefore, the expression of the plate shape control efficiency coefficient is:

[0126]

[0127] Among them, eff i (y i ) is the plate shape control efficiency coefficient, IU(y i ) is the change in plate shape deviation, Δu i It is the adjustment amount of the plate shape control actuator.

[0128] The above description is only a preferred embodiment of the present invention and is not intended to limit the concept of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for obtaining the plate shape control efficiency based on the digital twin of the cold rolling process, characterized in that: include: Step 1: Collect the cold rolling mill size parameters, production process parameters, emulsion property parameters, and strip and roll material parameters; Step 2: Establish a finite element model of the temperature field of the cold rolling work roll based on the parameters collected in step 1, and calculate the transverse temperature distribution data of the work roll temperature field in the rolling stability stage; Step 3: Verify the accuracy of the cold rolling work roll temperature field finite element model by comparing the temperature distribution data; Step 4: Use the transverse temperature distribution data obtained in step 2 as a thermal load to achieve thermal-structural coupling between the work roll geometric model and the cold rolling work roll temperature field finite element model, and obtain the work roll thermal crown results in the rolling stability stage; Step 5: Based on the production process parameters, rolling mill dimensional parameters, and work roll thermal crown collected on-site in Step 1, a digital twin of the cold rolling process is established, and the cold rolling process is simulated and calculated; Step 6: Compare the on-site measured rolling force data and strip exit thickness data with the simulated data in Step 5 to verify the accuracy of the cold rolling process digital twin; Step 7: Within the maximum range of the production process parameter settings, the parameter settings of the work roll bending force, the intermediate roll bending force, and the intermediate roll lateral displacement are changed respectively to obtain the distribution of the strip outlet thickness and the distribution of the relative length difference of the longitudinal fiber strips of the strip under different strip shape control actuators; Step 8: Based on the definition of the flatness control efficiency coefficient and the data on the difference in length of the steel fiber strip after rolling when different flatness control mechanisms are put into use, the control efficiency coefficient curves of the work roll bending force, the intermediate roll bending force, and the intermediate roll lateral displacement are calculated and obtained. This quantitatively describes the control capabilities of various flatness control mechanisms on flatness, thereby accurately controlling the flatness; The step 5 is specifically as follows: Step 5.1: Set the material properties of the cold rolling process digital twin based on the collected strip and roll material parameters; Step 5.2: Establish and solve the cold rolling process digital twin based on the measured production process parameters, rolling mill dimensional parameters, and the work roll thermal crown results obtained in the rolling stability stage in step 4; Step 5.3: Use Ls-Prepost post-processing software to extract the rolling force and strip thickness calculation results during the rolling process; The step 6 is specifically as follows: Step 6.1: Compare the measured rolling force, theoretically calculated rolling force, and measured strip exit center thickness with the rolling force and strip exit thickness calculated in step 5 to verify the accuracy of the cold rolling process digital twin. Step 6.2: Compare the post-rolling strip transverse thickness distribution data obtained from the simulation in Step 5 with the on-site measured post-rolling strip thickness distribution data to further verify the accuracy of the cold rolling process digital twin; The expression of the plate shape control efficiency coefficient in step 8 is: Among them, eff i (y i ) is the plate shape control efficiency coefficient, IU(y i ) is the change in plate shape deviation, Δu i The adjustment amount of the plate shape control actuator; Among them, y i is the coordinate of a certain position along the width of the strip, L(y i ) is the coordinate y i The length of the longitudinal fiber strip at is the average length of the longitudinal fiber strips in the strip.

2. The method for obtaining the plate shape control efficiency based on the cold rolling process digital twin according to claim 1, characterized in that: (1) Cold rolling mill size parameters include: Dimensional parameters of support roll, intermediate roll and working roll; (2) Production process parameters include: The friction coefficient between the work roll and the strip is μ, dimensionless; Initial thickness of strip before rolling h in , mm; Strip thickness after rolling h out , mm; Strip width W0, mm; Rolling reduction ratio ε, dimensionless; The front tension and back tension σ of the rolling process b ,σ f , MPa; Roller radius R, mm; Rolling speed v R , m / min; Strip deformation resistance σ s , MPa; Working roll bending force, kN; Intermediate roll bending force, kN; Intermediate roller lateral displacement, mm; Rolling force P, kN; Working roll initial temperature T w0 , ℃; (3) Emulsion property parameters include: Emulsion concentration C, dimensionless; Emulsion initial temperature T e0 , ℃; Heat transfer coefficient h between emulsion and working roll in strong water cooling zone str , W·(m·K) -1 ; Heat transfer coefficient h between emulsion and working roll in weak water cooling zone weak , W·(m·K) -1 ; (4) Strip and roll material parameters include: Strip and work roll density ρ s ,ρ w , kg·m -3 ; Elastic modulus E of strip steel and working roll s ,E w , GPa; Poisson's ratio ν between strip and work roll s ,ν w , dimensionless; Working roll specific heat capacity C w , J·(kg·℃) -1 Thermal conductivity of working roll h w , W·(m·K) -1 .

3. The method for obtaining the plate shape control efficiency based on the cold rolling process digital twin according to claim 1, characterized in that: The step 2 is specifically as follows: Step 2.1: Establish a work roll geometric model based on the work roll size parameters; Step 2.2: Determine the material properties of the work roll temperature field model based on the collected roll material parameters; Step 2.3: Simplify the thermal boundary conditions of the work roll temperature field. Calculate the thermal boundary conditions based on the heat exchange during the rolling process and the emulsion properties, and apply them to the work roll geometric model. Step 2.4: Use edge refinement to mesh the work roll geometry model; Step 2.5: Set the solution conditions and establish the finite element model of the temperature field of the cold rolling work roll; Step 2.6: Use the finite element model of the cold rolling work roll temperature field established in step 2.5 to calculate the temperature distribution change of the work roll during the rolling process; Step 2.7: Use post-processing software to extract the transverse temperature distribution data of the working roll temperature field during the rolling stability stage in step 2.

6.

4. The method for obtaining the plate shape control efficiency based on the cold rolling process digital twin according to claim 1, characterized in that: The step 3 is specifically as follows: The transverse temperature distribution data was compared with the temperature distribution data measured on site within five minutes after the work rolls were taken off the mill to verify the accuracy of the finite element model of the temperature field of the cold rolling work rolls.

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