A multi-pass cold-rolled strip section appearance intelligent control method

Abstract

The application provides a multi-pass cold-rolled strip section appearance intelligent control method, which comprises the following steps: step 1: under the condition that the AGC thickness control of the cold continuous rolling site is stable, the rolling mill is suddenly stopped, and the rolling data of each rack is collected; step 2: each rack of the cold continuous rolling site is regarded as a finite element model, and the finite element models of the racks are established in sequence; step 3: the control variable experiment of three kinds of plate shape actuators of each rack is carried out by using the established finite element model, the strip width data and the section shape data of each rack in the stable rolling stage are extracted, and the regulation and control efficiency coefficients of the three kinds of plate shape actuators of each rack are calculated; and step 4: a multi-rack outlet strip section appearance prediction mathematical model is established, the precision is verified, and the multi-rack outlet strip section appearance prediction mathematical model is packaged to obtain two-dimensional and three-dimensional visualization software for the outlet strip section appearance prediction of each rack.

Description

Technical Field

[0001] This invention belongs to the field of strip rolling technology and relates to an intelligent control method for the cross-sectional morphology of multi-pass cold-rolled strip. Background Technology

[0002] In actual cold rolling production, the transverse thickness deviation of strip steel usually needs to be minimized through multi-pass, multi-strip shape control methods during the cold rolling process. However, due to limitations in the original model settings, the appropriate adjustment amount of the actuator often requires repeated adjustments by skilled workers based on experience. Therefore, to quickly reduce the transverse thickness deviation of cold-rolled strip steel at the exit of actual production, effective and accurate prediction of the strip cross-sectional morphology at each stand exit is crucial.

[0003] Domestic researchers have conducted some studies on obtaining the shape control effect of cold rolling mills. The Chinese journal article "Research on the transverse thickness of cold-rolled strip steel based on ANSYS" takes a four-roll strip mill as the research object, uses ANSYS software to establish a finite element model of four-roll cold-rolled strip, and completely simulates the cold rolling process of strip. Based on the simulation and experimental results, it compares and analyzes the distribution law of transverse thickness during the cold rolling process of strip, providing guidance for actual production and theoretical research. The Chinese journal article "Research on the efficiency coefficient of six-roll cold rolling mill" (Rolling Steel, 2013, 30(5): 1-4.) proposes to establish a calculation model of the elastic deformation of the roll system of a six-roll cold rolling mill by using the modified influence function method, and determines the efficiency coefficient of each shape control mechanism by solving the transverse straightness distribution of the strip after rolling. Chinese invention patent application CN202110125458.X, entitled "A Method for Obtaining the Control Efficiency Coefficient of a UCM Rolling Mill Strip Shape Actuator," discloses a method for obtaining the control efficiency coefficient of a UCM rolling mill strip shape actuator. This invention utilizes the three-dimensional elastoplastic finite element method to construct a three-dimensional elastoplastic finite element model of the UCM rolling mill and strip, and obtains the control efficiency coefficient of the UCM rolling mill strip shape actuator, thus improving the accuracy and stability of the three-dimensional elastoplastic finite element model of the UCM rolling mill and strip. Chinese invention patent application CN202110727729.9, entitled "A Finite Element Simulation Method for Multi-Station Cold Rolling Strip Shape," obtains a simulation method for the cold rolling process based on a data transfer method. Chinese invention patent application CN202310652406.7, entitled "A Method for Predicting Transverse Thickness Distribution Based on Uneven Transverse Strength of Strip," discloses a method for predicting the transverse thickness of a single cold rolling stand. Chinese invention patent application CN202310609005.3, entitled "A Method for Predicting Strip Shape Based on Differences in Transverse Mechanical Properties of Rolled Steel," provides a method for predicting the strip shape at the exit point, taking into account differences in the transverse mechanical properties of rolled steel.

[0004] The above research has three main shortcomings: (1) There are many assumptions about the mechanical properties of rolled materials. The mechanical properties of strip steel are not uniformly distributed along the width direction in the actual production process. Moreover, due to factors such as the original residual stress of hot-rolled material, work hardening during cold rolling, and roll deflection, this non-uniform mechanical properties will be transmitted and inherited. (2) Previous simulation studies on the control effect of the plate shape actuator were mainly conducted on single stands. Even the simulation of multi-stand cold continuous rolling has many assumptions. Furthermore, the control effect of the plate shape actuator of each stand in the cold continuous rolling process is inferred from the single stand. (3) There is no method for predicting the cross-sectional morphology of strip steel with multi-stand and multi-plate shape control methods. Summary of the Invention

[0005] To address the aforementioned technical problems, the purpose of this invention is to provide an intelligent control method for the cross-sectional morphology of multi-pass cold-rolled strip steel.

[0006] The present invention provides an intelligent control method for the cross-sectional morphology of multi-pass cold-rolled strip steel, comprising the following steps:

[0007] Step 1: Under the condition that the thickness control of AGC is stable at the cold continuous rolling mill, perform an emergency stop of the rolling mill and collect rolling data for each stand;

[0008] Step 2: Treat each stand in the cold rolling mill as a finite element model, and establish the finite element model of each stand in sequence;

[0009] Step 3: Using the established finite element model, conduct controlled variable experiments on the three types of plate shape actuators for each stand, extract strip width data and cross-sectional shape data for the stable rolling stage of each stand, and calculate the control efficiency coefficient of the three types of plate shape actuators for each stand.

[0010] Step 4: Establish a mathematical model for predicting the cross-sectional morphology of the exit strip of a multi-stand machine and verify its accuracy. Then, encapsulate the mathematical model for predicting the cross-sectional morphology of the exit strip of a multi-stand machine to obtain two-dimensional and three-dimensional visualization software for predicting the cross-sectional morphology of the exit strip of each stand.

[0011] The rolling data for each stand in step 1 includes: geometric data and roll material performance data for all rolls in each stand, rolling process parameters for each stand, geometric data of the strip at the entrance of the first stand and at the exit of each stand, and tensile data of the strip at different positions in the width direction at the entrance of each stand.

[0012] The intelligent control method for cross-sectional morphology of multi-pass cold-rolled strip steel of the present invention has at least the following beneficial effects:

[0013] (1) The multi-stand cold rolling finite element model provided by the present invention takes into account the study of the transverse cumulative deformation of strip steel during the cold rolling process, and can realize the simulation of the transfer of strip steel geometry and transverse mechanical properties during the cold rolling process, making the simulation model closer to reality.

[0014] (2) The method provided by this invention can solve the cross-sectional morphology of the strip at the exit of each stand during the cold rolling process. It can provide direct decision support for researchers and automation implementers, provide daily automation data collection, problem discovery during automation construction, and model and process analysis and design basis for enterprise production managers and researchers. It can also provide theoretical and simulation basis for quickly adjusting the adjustment amount of the mill shape actuator in each rolling pass to achieve a good strip shape in actual production.

[0015] (3) This invention is based on the simulation of rolling theory and finite element dynamics, which can effectively avoid equipment and time losses caused by experiments and reduce enterprise costs;

[0016] (4) The method of the present invention establishes a finite element model of each stand by performing coupled analysis on the actual cold continuous rolling strip genetic rolling process. Then, based on the model, a more accurate thickness control efficiency curve of each stand is obtained, and a method for predicting the cross-sectional morphology of the strip at the exit of each stand is given by combining multiple regression equations, so as to fill the gap in the prediction of the cross-sectional morphology of strip with multiple exits and multiple plate shape control methods. Attached Figure Description

[0017] Figure 1 This is a flowchart of an intelligent control method for the cross-sectional morphology of multi-pass cold-rolled strip steel according to the present invention;

[0018] Figure 2 These are the strip width data and lateral thickness distribution data at the entrance of each rack;

[0019] Figure 3a This is a verification diagram of the accuracy of the mathematical model for predicting the cross-sectional morphology of the export strip of the first frame;

[0020] Figure 3b This is a verification diagram of the accuracy of the mathematical model for predicting the cross-sectional morphology of the export strip of the second frame;

[0021] Figure 3c This is a verification diagram of the accuracy of the mathematical model for predicting the cross-sectional morphology of the export strip of the third frame;

[0022] Figure 3d This is a verification diagram of the accuracy of the mathematical model for predicting the cross-sectional morphology of the export strip of the fourth frame;

[0023] Figure 3e This is a verification diagram of the accuracy of the mathematical model for predicting the cross-sectional morphology of the export strip of the fifth frame;

[0024] Figure 3f This is a verification diagram of the accuracy of the mathematical model for predicting the cross-sectional morphology of the export strip of the sixth frame. Detailed Implementation

[0025] In this embodiment, a 1800mm UCM six-roll cold rolling mill of a certain factory is used as an example to establish a proportional high-precision multi-stand cold rolling finite element model.

[0026] like Figure 1 As shown, a method for intelligent control of cross-sectional morphology of multi-pass cold-rolled strip steel includes:

[0027] Step 1: Under the condition that the thickness control of AGC is stable at the cold continuous rolling mill, perform an emergency stop of the rolling mill and collect rolling data for each stand;

[0028] In practice, the rolling data for each stand includes: the geometric data and material properties of all rolls in each stand, the rolling process parameters for each stand, the geometric data of the strip at the entrance of the first stand and the exit of each stand, and the tensile data of the strip at different positions in the width direction at the entrance of each stand.

[0029] Step 1.1: Obtain the roll geometry data and material property data for each stand, including the length of the work roll body, the length of the intermediate roll body, the length of the support roll body, the material density, the elastic modulus, and the Poisson's ratio for each stand; In this embodiment, the obtained roll geometry data and material property data for each stand are shown in Table 1.

[0030] Table 1. Geometric data and material properties of the rolls.

[0031]

[0032] Step 1.2: Obtain the rolling process parameters for each stand, including the friction coefficient, front / back tension, work roll bending force, intermediate roll bending force, and intermediate roll lateral displacement; in this embodiment, the obtained rolling process parameters for each stand are shown in Table 2.

[0033] Table 2 Rolling process parameters.

[0034]

[0035] Step 1.3: Obtain the strip geometry data at the first rack inlet and each rack outlet. The strip geometry data includes strip width data and transverse thickness distribution data. In this embodiment, the obtained strip width data and transverse thickness distribution data at the first rack inlet and each rack outlet are as follows: Figure 2 As shown.

[0036] Step 1.4: Cut a portion of the strip steel at the entrance of each frame, divide the strip steel into N strip steel samples at equal intervals along the width direction, perform a tensile test on each strip steel sample, obtain tensile data at different positions in the width direction of the strip steel at the entrance of each frame, and further process the tensile data to obtain the mechanical property data required for the finite element model of the strip steel.

[0037] Step 1.4.1: First, define the true stress-strain curve as consisting of an elastic deformation stage, a uniform plastic deformation stage, and a local plastic deformation stage; the boundary between the elastic deformation stage and the uniform plastic deformation stage is the yield point, and the stress at this point is called the yield stress; the boundary between the uniform plastic deformation stage and the local plastic deformation stage is the plastic instability point, and the stress at this point is called the tensile strength; the end of the stress-strain curve is the termination point of plastic deformation, and the specimen fractures.

[0038] Step 1.4.2: Obtain the nominal stress-strain curve: The nominal stress-strain curve, consisting of the elastic and plastic deformation stages, is obtained through a static tensile test at room temperature. The nominal stress... σ eng and relative linear strain ε eng Calculated using the following formula:

[0039] ;

[0040] ;

[0041] Where P is the tensile load; A 0 is the original cross-sectional area of ​​the sample; l 0 is the original length of the gauge length of the specimen; Δ l It is the elongation of the gauge length of the specimen.

[0042] Step 1.4.3: Correcting the elastic modulus: Since the measured tensile curve does not have a clear yield plateau, the true stress at the yield point is calculated using the following formula. σ s With strain ε true-c value:

[0043] ;

[0044] ;

[0045] ;

[0046] in, ε eng-c The nominal strain at the yield point, σ 0.2 Residual strain of the specimen ɛStress at 0.2% E To obtain the measured elastic modulus, the elastic modulus is corrected using the true strain and stress values ​​at the yield point. E m :

[0047] .

[0048] Step 1.4.4: Calculate the true stress-strain curve: It is generally assumed that the original cross-sectional area of ​​the specimen is... A Since the nominal stress and strain are approximately constant (0), they can be converted into true stress according to the law of conservation of volume. σ true With logarithmic strain ε true The actual stress-strain curve is then obtained, and its calculation method is as follows:

[0049] ;

[0050] ;

[0051] in, A It is the cross-sectional area of ​​the sample at each loading instant; l It is the instantaneous elongation of the gauge length of the specimen.

[0052] Step 1.4.5: Calculate the effective stress-strain curve: Calculate the effective stress and effective strain using the corrected elastic modulus, true stress, and true strain to obtain the effective stress-strain curve:

[0053] ;

[0054] ;

[0055] Step 1.4.6: Correcting the Necking Stage: The effective stress-strain curve obtained in the above steps is in the uniform plastic deformation stage before the necking (plastic instability point) occurs. At this time, the curve gradually rises. However, after plastic instability occurs, it enters the local plastic deformation stage. As the load decreases, the cross-sectional area also decreases sharply, causing the curve to decline. The declining segment of the curve will cause negative stiffness in the finite element simulation calculation, thus stopping the calculation. Therefore, it is necessary to use the data from the last stage of the rising segment of the curve to correct the tangent stiffness of the declining segment.

[0056] ;

[0057] ;

[0058] in, E T It is the tangent modulus; σ nIt is the maximum stress value in the final stage of the rising phase; ε n This represents the maximum strain value in the final stage of the rising segment. From this, the effective stress-strain curves of the strip at different locations along the strip width direction can be obtained, which are the mechanical property data required for modeling the strip finite element model.

[0059] Step 2: Treat each stand in the cold rolling mill as a finite element model and build the finite element model of each stand in sequence.

[0060] Step 2.1: Based on the obtained geometric data and material property data of the first stand rolls, rolling process parameters, strip geometry and mechanical property data at different positions in the strip width direction, perform the modeling work of the first stand finite element model.

[0061] Step 2.1.1: Based on the roll geometry, material property data and rolling process parameters of the first stand obtained in Step 1, use SOLID164 elements in ANSYS software to perform finite element geometric modeling of the roll and define the material properties of the roll model to complete the geometric and mechanical property modeling of the roll.

[0062] Step 2.1.2: Perform finite element geometric modeling of the strip based on the strip geometric data obtained in Step 1.3, and then assign the strip width mechanical property data obtained in Step 1.4 to different positions on the strip finite element model; thus, the geometric and transverse mechanical property modeling of the strip is completed.

[0063] Step 2.2: Conduct experiments on the established finite element model of the first stand and extract the nodal data of the strip cross section during the stable rolling stage of the finite element model.

[0064] Step 2.3: To consider the rolling inheritance of strip geometry and mechanical properties, a finite element model of the second stand is established based on the second stand roll geometry data and material property data, rolling process parameters, mechanical property data of strip at different positions in the width direction at the entrance of the second stand, and the strip cross-section node data of the first stand finite element model extracted in Step 2.2 during the stable rolling stage.

[0065] Step 2.4: The establishment of the finite element model for subsequent stands is the same as in Step 2.3. When modeling each subsequent stand, the nodal data of the strip cross section of the finite element model of the previous stand during the stable rolling stage are used to replace the geometric data of the inlet strip.

[0066] Step 3: Using the established finite element model, conduct controlled variable experiments on the three types of plate shape actuators for each stand, extract the strip width and cross-sectional shape data of the finite element model of each stand during the stable rolling stage, and calculate the control efficiency coefficient data of the three types of plate shape actuators for each stand.

[0067] Step 3.1: Design and conduct control variable experiments for the plate-shaped actuator of each frame finite element model;

[0068] In this embodiment, the adjustment range of the three plate-shaped actuators in each frame gradually increases from the minimum value to the maximum value. n=5, and a total of 6 frames are set up for 90 sets of experiments. The set values ​​of the bending force of the work roll, the bending force of the intermediate roll, and the lateral displacement of the intermediate roll are shown in Table 3.

[0069] Table 3. Values ​​for three types of plate actuators.

[0070]

[0071] In Table 3, the maximum set values ​​of the adjustment amounts of the three types of sheet shape actuators are all within the maximum allowable range for engineering applications. When studying the sheet shape control effect of the work roll bending and intermediate roll bending, the intermediate roll lateral movement is set to a constant value; when studying the sheet shape control effect of the intermediate roll lateral movement, the work roll bending force and the intermediate roll bending force are set to constant values.

[0072] Step 3.2: Extract the strip width and cross-sectional shape data of the finite element model of each stand during the stable rolling stage, and calculate the control efficiency coefficient data of the three plate shape actuators of each stand.

[0073] Step 4: Establish a mathematical model for predicting the cross-sectional morphology of the exit strip of a multi-stand machine and verify its accuracy. Then, encapsulate the mathematical model for predicting the cross-sectional morphology of the exit strip of a multi-stand machine to obtain two-dimensional and three-dimensional visualization software for predicting the cross-sectional morphology of the exit strip of each stand.

[0074] Step 4.1: Using the strip width and cross-sectional shape data of each stand obtained in Step 3.2 and the control efficiency coefficient data of the three types of plate shape actuators during the stable rolling stage, establish the mathematical calculation equation for predicting the cross-sectional morphology of the exit strip of each stand, and obtain the mathematical model for predicting the cross-sectional morphology of the exit strip of multiple stands.

[0075] Step 4.1.1: Normalize the strip width data extracted in Step 3.2; fit the strip cross-sectional shape data using a fifth-order polynomial to obtain the polynomial fitting coefficients. B 1. B 2. B 3. B 4. B 5; The fifth-degree polynomial is as follows:

[0076] ;

[0077] in, y This refers to the cross-sectional shape data of the strip steel. THK _Center The intercept; B 1. B 2. B 3. B 4. B 5 represents the fitting coefficients for the first, second, third, fourth, and fifth orders of the strip cross-sectional shape data, respectively; x The coordinates are dimensionless after normalization in the width direction of the strip. x ∈[-1,1].

[0078] Step 4.1.2: Fit the control efficacy coefficient data of the three types of plate actuators obtained in Step 3.2 using a sixth-order Legendre orthogonal polynomial to obtain the fitting coefficients of the control efficacy coefficient data of the three types of plate actuators. A 0、 A 1. A 2. A 4. A 6. The sixth-degree Legendre orthogonal polynomial used is as follows:

[0079] ;

[0080] in, E ( x () represents the strip steel control efficiency coefficient data; A 0 represents a constant term. A 1. A 2. A 4. A 6 represents the fitting coefficients of the first, second, fourth, and sixth orders of the control efficacy coefficient data, respectively. The magnitude of their absolute values ​​indicates the control components of the plate shape actuator on the first, second, fourth, and sixth plate shape defects. x The coordinates are dimensionless after normalization in the width direction of the strip. x ∈[-1,1]; e ( x ) represents the fitting error.

[0081] Step 4.1.3: Designate the area 100mm from the edge on both sides of the strip as the edge thinning zone, and the remaining portion as the center zone, using the normalized strip width as... x Shaft and thickness prediction values y A two-dimensional coordinate system is established along the axes. The calculation equation for the predicted thickness of each coordinate point in the central area of ​​the strip is as follows:

[0082] ;

[0083] in, H P The calculated predicted thickness value for each coordinate point;

[0084] ;

[0085] in, The basic thickness value for each coordinate point in the central area of ​​the strip is calculated based on the polynomial fitting coefficients of the normalized width data and cross-sectional morphology data obtained in step 4.1.1.

[0086] ; ; ;

[0087] in, V WRB For the bending force of the work roll, V IRB For the bending force of the intermediate roll, V IRS This is the amount of lateral movement of the intermediate roller. A 0W , A 0I and A 0S They are all constant terms. A 1W , A 2W , A 4W and A 6W These are the fitting coefficients for the first, second, fourth, and sixth orders of the curve representing the efficiency coefficient of the work roll bending control. A 1I , A 2I , A 4I and A 6I These are the fitting coefficients for the first, second, fourth, and sixth orders of the curve representing the efficiency coefficient of the intermediate roll bending control. A 1S , A 2S , A 4S and A 6S These are the fitting coefficients for the first, second, fourth, and sixth orders of the intermediate roller transverse movement control efficiency coefficient curve, respectively.

[0088] ;

[0089] ;

[0090] .

[0091] Step 4.1.4: Due to the elastic deformation of the roll system and the three-dimensional deformation of the strip during the rolling process, the thickness will decrease sharply at a certain position from both sides. That is, the calculation equation for the cross-sectional morphology of the strip in the central area constructed above is not applicable to the thinning area at both sides. It is necessary to construct separate calculation equations for the cross-sectional morphology of the thinning area at both sides of the strip. The calculation equations for the predicted thickness of the strip at each coordinate point in the two thinning areas at both sides are as follows:

[0092] ;

[0093] ;

[0094] ;

[0095] in, The basic thickness value for each coordinate point is calculated based on the polynomial fitting coefficients of the normalized width data and cross-sectional morphology data obtained in step 4.1.1 for each edge thinning zone of the strip. and These are the basic thickness values ​​at each coordinate point of the strip operating side and driving side, calculated based on the polynomial fitting coefficients of the normalized width data and cross-sectional morphology data obtained in step 4.1.1. H OS and H DS The thickness values ​​of the outermost point of the steel strip center area near the operating side and the outermost point of the drive side should be considered. V e It is the edge thinning value; f t It is the weight of the degree of edge thinning; f b It is the position coefficient, that is, the width coordinate of each width coordinate point relative to the outermost position.

[0096] Step 4.1.5: After denormalizing the normalized width data obtained in Step 4.1.1, substitute it into the calculation equations of Steps 4.1.3 and 4.1.4 respectively to calculate the thickness value at each coordinate point and obtain the overall cross-sectional morphology of the strip. This yields a mathematical model for predicting the cross-sectional morphology of the exit strip on a multi-stand basis. The bending force of the work roll, the bending force of the intermediate roll, and the lateral displacement of the intermediate roll are all used as input variables in this model.

[0097] Step 4.2: Verify the accuracy of the established mathematical model for predicting the cross-sectional morphology of the multi-stand export strip;

[0098] The results of the exit strip cross-sectional morphology prediction mathematical model for each stand were compared with the simulation results of the corresponding stand's finite element model. The accuracy verification results of the multi-stand exit strip cross-sectional morphology prediction mathematical model are as follows: Figures 3a-3f As shown.

[0099] Step 4.3: Encapsulate the mathematical model for predicting the cross-sectional morphology of the strip exit at each stand to obtain two-dimensional and three-dimensional visualization software for predicting the cross-sectional morphology of the strip exit at multiple stands.

[0100] The above description is only a preferred embodiment of the present invention and is not intended to limit the ideas of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for intelligent control of cross-sectional morphology of multi-pass cold-rolled strip steel, characterized in that, Includes the following steps: Step 1: Under the condition that the thickness control of AGC is stable at the cold continuous rolling mill, perform an emergency stop of the rolling mill and collect rolling data for each stand; The rolling data for each stand includes: geometric data and roll material property data for all rolls in each stand, rolling process parameters for each stand, strip geometric data at the entrance of the first stand and at the exit of each stand, and stretching data of the strip at different positions in the width direction at the entrance of each stand. Step 1 specifically includes: Step 1.1: Obtain the geometric data and material property data of all rolls in each stand, including the length of the work roll, intermediate roll, support roll, material density, elastic modulus, and Poisson's ratio of each stand; Step 1.2: Obtain the rolling process parameters for each stand, including the coefficient of friction, front and rear tension, work roll bending force, intermediate roll bending force, and intermediate roll lateral displacement; Step 1.3: Obtain the strip geometry data at the entrance of the first rack and the exit of each rack. The strip geometry data includes strip width data and transverse thickness distribution data. Step 1.4: Cut a portion of the strip steel at the entrance of each frame, divide the strip steel into N strip steel samples at equal intervals along the width direction, perform a tensile test on each strip steel sample, obtain tensile data at different positions in the width direction of the strip steel at the entrance of each frame, and further process the tensile data to obtain the mechanical property data required for the finite element model of the strip steel. Step 2: Treat each stand in the cold rolling mill as a separate finite element model, and establish the finite element model of each stand in sequence, specifically as follows: Step 2.1: Based on the obtained roll geometry data, material property data, rolling process parameters, strip geometry, and mechanical property data at different locations along the width of the strip, perform finite element modeling of the first stand; Step 2.2: Conduct experiments on the established finite element model of the first stand and extract the nodal data of the strip cross section of the finite element model during the stable rolling stage; Step 2.3: To consider the rolling inheritance of strip geometry and mechanical properties, a finite element model of the second stand is established based on the roll geometry data and material property data of the second stand obtained in Step 1, the rolling process parameters, the mechanical property data of the strip at different positions in the width direction at the entrance of the second stand, and the strip cross-sectional node data of the first stand extracted in Step 2.2 during the stable rolling stage. Step 2.4: The establishment of the finite element model for subsequent stands is the same as in Step 2.

3. When modeling each subsequent stand, the nodal data of the strip cross section of the finite element model of the previous stand during the stable rolling stage are used to replace the geometric data of the inlet strip. Step 3: Using the established finite element model, conduct controlled variable experiments on the three types of plate shape actuators for each stand, extract the strip width and cross-sectional shape data of the finite element model of each stand during the stable rolling stage, and calculate the control efficiency coefficient of the three types of plate shape actuators for each stand. Step 4: Establish a mathematical model for predicting the cross-sectional morphology of the exit strip of a multi-stand machine and verify its accuracy. Then, encapsulate the mathematical model for predicting the cross-sectional morphology of the exit strip of a multi-stand machine to obtain two-dimensional and three-dimensional visualization software for predicting the cross-sectional morphology of the exit strip of each stand.

2. The intelligent control method for cross-sectional morphology of multi-pass cold-rolled strip steel as described in claim 1, characterized in that, Step 1.4 specifically includes: Step 1.4.1: First, define the true stress-strain curve as consisting of an elastic deformation stage, a uniform plastic deformation stage, and a local plastic deformation stage; the boundary between the elastic deformation stage and the uniform plastic deformation stage is the yield point, and the stress at this point is called the yield stress; the boundary between the uniform plastic deformation stage and the local plastic deformation stage is the plastic instability point, and the stress at this point is called the tensile strength; the end of the true stress-strain curve is the termination point of plastic deformation, where the specimen fractures. Step 1.4.2: Obtain the nominal stress-strain curve: Obtain the nominal stress-strain curve consisting of elastic and plastic deformation stages through a static tensile test at room temperature. The nominal stress σ eng and relative linear strain ε eng Calculated using the following formula: ； ； Where P is the tensile load; A0 is the original cross-sectional area of ​​the specimen; l0 is the original length of the gauge length of the specimen; Δl is the elongation of the gauge length of the specimen. Step 1.4.3: Correcting the elastic modulus: Since the measured tensile curve does not have a clear yield plateau, the true stress σ at the yield point is calculated using the following formula. s With strain ε true-c value: ； ； ； Where, ε eng-c σ is the nominal strain at the yield point. 0.2 Let E be the stress at which the residual strain α = 0.2% of the specimen, and E be the measured elastic modulus. E is obtained by correcting the elastic modulus using the true strain and stress values ​​at the yield point. m : ； Step 1.4.4: Calculate the true stress-strain curve: It is generally assumed that the original cross-sectional area A0 of the specimen remains approximately constant. Therefore, according to the law of conservation of volume, the measured nominal stress and strain are converted into true stress σ. true With logarithmic strain ε true The actual stress-strain curve is obtained, and its calculation method is as follows: ； ； Where A is the cross-sectional area of ​​the specimen at each loading instant; l is the instantaneous elongation of the gauge length of the specimen. Step 1.4.5: Calculate the effective stress-strain curve: Calculate the effective stress and effective strain using the corrected elastic modulus, true stress, and true strain to obtain the effective stress-strain curve: ； ； Step 1.4.6: Correcting the necking stage: Using the data from the final stage of the ascending segment of the curve, correct the tangent stiffness of the descending segment: ； ； Among them, E T It is the tangent modulus; σ n It is the maximum stress value in the final stage of the rising phase; ε n It is the maximum strain value of the data in the final stage of the rising segment, and obtains the effective stress-strain curve of the strip at different positions along the width direction of the strip, that is, the mechanical property data required for the finite element modeling of the strip.

3. The intelligent control method for cross-sectional morphology of multi-pass cold-rolled strip steel as described in claim 1, characterized in that, Step 2.1 specifically involves: Step 2.1.1: Based on the roll geometry data, material property data and rolling process parameters of the first stand obtained in Step 1, use SOLID164 elements in ANSYS software to perform finite element geometric modeling of the roll and define the material properties of the roll model to complete the geometric and mechanical property modeling of the roll. Step 2.1.2: Perform finite element geometric modeling of the strip based on the strip geometric data obtained in Step 1.3, and then assign the strip width mechanical property data obtained in Step 1.4 to different positions on the strip finite element model; This completes the modeling of the geometric and transverse mechanical properties of the strip steel.

4. The intelligent control method for cross-sectional morphology of multi-pass cold-rolled strip steel as described in claim 1, characterized in that, Step 3 specifically involves: Step 3.1: Design and conduct control variable experiments for the plate-shaped actuator of the finite element model for each frame; Step 3.2: Extract the strip width and cross-sectional shape data of the finite element model of each stand during the stable rolling stage, and calculate the control efficiency coefficient data of the three plate shape actuators of each stand.

5. The intelligent control method for cross-sectional morphology of multi-pass cold-rolled strip steel as described in claim 4, characterized in that, Step 4 specifically involves: Step 4.1: Using the strip width and cross-sectional shape data of each stand obtained in Step 3.2 and the control efficiency coefficient data of the three types of plate shape actuators during the stable rolling stage, establish the mathematical calculation equation for predicting the cross-sectional morphology of the exit strip of each stand, and obtain the mathematical model for predicting the cross-sectional morphology of the exit strip of multiple stands. Step 4.2: Verify the accuracy of the established mathematical model for predicting the cross-sectional morphology of the multi-stand export strip; Step 4.3: Encapsulate the mathematical model for predicting the cross-sectional morphology of the strip exit at each stand to obtain two-dimensional and three-dimensional visualization software for predicting the cross-sectional morphology of the strip exit at multiple stands.

6. The intelligent control method for cross-sectional morphology of multi-pass cold-rolled strip steel as described in claim 5, characterized in that, Step 4.1 specifically involves: Step 4.1.1: Normalize the strip width data extracted in Step 3.2; fit the strip cross-sectional shape data using a fifth-order polynomial to obtain polynomial fitting coefficients B1, B2, B3, B4, and B5; the fifth-order polynomial is as follows: ； Where y is the strip cross-sectional shape data; THK_Center is the intercept; B1, B2, B3, B4, and B5 are the fitting coefficients of the first, second, third, fourth, and fifth orders of the strip cross-sectional shape data, respectively; and x is the dimensionless coordinate of the strip width direction after normalization, x∈[-1,1]. Step 4.1.2: The regulation efficiency coefficient data of the three types of plate actuators obtained in Step 3.2 are fitted using a sixth-order Legendre orthogonal polynomial to obtain the fitting coefficients A0, A1, A2, A4, and A6 of the regulation efficiency coefficient data of the three types of plate actuators. The sixth-order Legendre orthogonal polynomial used is as follows: ； Where E(x) represents the strip steel control efficiency coefficient data; A0 is the constant term; A1, A2, A4, and A6 are the fitting coefficients of the first, second, fourth, and sixth degree terms of the control efficiency coefficient data, respectively, and their absolute values ​​indicate the control components of the strip shape actuator on the first, second, fourth, and sixth degree strip shape defects; x is the dimensionless coordinate after normalization in the strip width direction, x∈[-1,1]; e(x) is the fitting error; Step 4.1.3: Designate the area 100mm from the edge on both sides of the strip as the edge thinning zone, and the remaining part as the center zone. Establish a two-dimensional coordinate system with the normalized strip width as the x-axis and the predicted thickness as the y-axis. The calculation equation for the predicted thickness at each coordinate point in the center zone of the strip is as follows: ； Among them, H P The calculated predicted thickness value for each coordinate point; ； in, The basic thickness value for each coordinate point in the central area of ​​the strip is calculated based on the polynomial fitting coefficients of the normalized width data and cross-sectional shape data obtained in step 4.1.

1. ； ； ； Among them, V WRB V is the bending force of the work roll. IRB V is the bending force of the intermediate roll. IRS A represents the lateral displacement of the intermediate roller. 0W A 0I and A 0S All are constant terms, A 1W A 2W A 4W and A 6W These are the fitting coefficients for the first, second, fourth, and sixth orders of the work roll bending control efficiency coefficient curve; A 1I A 2I A 4I and A 6I These are the fitting coefficients for the first, second, fourth, and sixth orders of the intermediate roll bending control efficiency coefficient curve, respectively; A 1S A 2S A 4S and A 6S These are the fitting coefficients for the first, second, fourth, and sixth orders of the intermediate roller transverse movement control efficiency coefficient curve, respectively. ； ； ； Step 4.1.4: Construct separate calculation equations for each coordinate point of the cross-sectional morphology of the thinning zone at both sides of the strip, and establish the calculation equations for the predicted thickness of each coordinate point of the thinning zone at both sides of the strip as follows: ； ； ； in, The basic thickness value for each coordinate point is calculated based on the polynomial fitting coefficients of the normalized width data and cross-sectional shape data obtained in step 4.1.1 for each edge thinning zone of the strip. and These are the basic thickness values ​​at each coordinate point for the strip's operating side and driving side, calculated using polynomial fitting coefficients based on the normalized width and cross-sectional shape data obtained in step 4.1.1; H OS and H DS These represent the thickness values ​​of the outermost point near the operating side and the outermost point near the drive side of the strip's central area, respectively; V e It is the edge thinning value; f t It is the weight of the degree of edge thinning; f b It is the position coefficient, that is, the width coordinate of each width coordinate point relative to the outermost position; Step 4.1.5: After denormalizing the normalized width data obtained in Step 4.1.1, substitute it into the calculation equations of Step 4.1.3 and 4.1.4 respectively to calculate the thickness value at each coordinate point and obtain the overall cross-sectional morphology of the strip. This yields the mathematical model for predicting the cross-sectional morphology of the exit strip of the multi-stand mill. The bending force of the work roll, the bending force of the intermediate roll, and the lateral displacement of the intermediate roll are all used as input variables in this model.

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