A method for dividing a flatness preset layer table of a flatness unit

Abstract

This invention discloses a method for dividing a pre-set layer table for strip shape in a leveling mill, comprising: constructing an integrated simulation model of the rolling mill roll system based on the structure of a four-roll leveling mill; acquiring actual production data of the four-roll leveling mill; performing simulation calculations based on the actual production data and the integrated simulation model of the rolling mill roll system to obtain simulation big data; analyzing the simulation big data to obtain the correlation curves between various strip parameters and the secondary crown of the strip cross-section; and obtaining the optimal division scheme of the pre-set layer table based on the correlation curves between strip parameters and the secondary crown of the strip cross-section. This invention solves the problem of poor control accuracy of pre-set strip shape in the prior art, thereby significantly improving the strip shape quality of strip products.

Description

Technical Field

[0001] This invention belongs to the field of plate and strip steel rolling production technology, and particularly relates to a method for dividing a pre-set layer table of plate shape in a leveling unit. Background Technology

[0002] Leveling, a crucial process in strip steel production, directly impacts the product's surface quality, mechanical properties, and shape. During leveling, a shape control system adjusts the values ​​of various actuators to alter the roll gap shape, aiming to approximate the target strip shape. Currently, common hot-dip galvanizing lines for automotive steel typically employ single-stand four-roll leveling machines. These machines primarily rely on work roll bending control for shape adjustment. Furthermore, to ensure the surface quality of export products, most leveling units lack contact-type shape gauges or other inspection equipment at the end, preventing closed-loop feedback control of strip shape. Instead, they rely on shape presets and feedforward control for strip shape control. Therefore, ensuring the accuracy of shape preset control and feedforward control is a prerequisite for producing high-quality strip steel products.

[0003] Shape pre-setting control refers to setting initial values ​​for various control mechanisms based on incoming strip information such as width, thickness, crown, and other process information before the start of strip rolling. This minimizes the deviation from the target shape before stable rolling. Currently, shape pre-setting control mainly uses pre-setting tables. By pre-classifying the incoming strip specifications (width, thickness, elongation, etc.) into a shape pre-setting layer table, preset values ​​for various adjustment mechanisms are set for strips of different specifications. The rationality of the layer division in the shape pre-setting layer table significantly impacts the accuracy of the preset values. To better ensure the strip shape quality of the leveling unit, a suitable method for dividing the shape pre-setting layer table for leveling units needs to be proposed. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a method for dividing a pre-defined layer table for a leveling unit, thereby resolving the issues present in the prior art.

[0005] To achieve the above objectives, the present invention provides a method for dividing a pre-defined layer table for a leveling unit, comprising:

[0006] An integrated simulation model of the rolling mill roll system was constructed based on the structure of a four-roll leveler.

[0007] Obtain actual production data of the four-roll leveler, and perform simulation calculations based on the actual production data and the integrated simulation model of the rolling mill roll system to obtain simulation big data.

[0008] Based on the simulation big data analysis, the correlation curves between each strip parameter and the secondary convexity of the strip cross section are obtained;

[0009] The optimal division scheme of the predefined layer table is obtained based on the correlation curve between strip parameters and the secondary convexity of the strip cross section.

[0010] Optionally, the process of constructing an integrated simulation model of the rolling mill roll system includes:

[0011] Based on the structure of the four-roll leveler and the theory of elastic deformation of the rolls, an elastic deformation model of the roll system of the four-roll leveler is established. Based on the structure of the four-roll leveler and the theory of three-dimensional plastic deformation of the workpiece, a plastic deformation model of the workpiece is established. The elastic deformation model of the roll system of the four-roll leveler and the plastic deformation model of the workpiece are combined to obtain an integrated simulation model of the roll system of the workpiece.

[0012] Optionally, the process of obtaining simulation big data includes:

[0013] The actual production data of the four-roll leveler is obtained and preprocessed. A simulation working condition table is designed based on the preprocessed actual production data. Based on the integrated simulation model of the rolling mill roll system, all working condition parameters in the simulation working condition table are simulated and calculated to obtain simulation big data.

[0014] Optionally, the process of designing a simulation condition table includes:

[0015] Based on actual production data, the proportion of different strip steel specifications in all data is statistically analyzed. The weights are determined based on the proportions, and the density of strip steel parameters is determined based on the weight values.

[0016] Optionally, based on the parameter combination in the working condition table, assume the initial lateral distribution of rolling force; calculate the exit cross-sectional shape of the strip after rolling based on the integrated simulation model of the rolling mill roll system; calculate the actual lateral distribution of rolling force based on the exit cross-sectional shape and reduction rate; compare the calculated lateral distribution of rolling force with the assumed value, and if the accuracy requirements are not met, correct the assumed value and recalculate until the error conditions are met.

[0017] Optionally, based on the simulation big data, the second-order convexity is obtained by Chebyshev polynomial fitting, and the correlation curves between each strip parameter and the second-order convexity of the strip cross section are plotted respectively; the strip parameters include thickness, elongation, deformation resistance and width.

[0018] Optionally, an optimization algorithm is used to find the optimal segmentation point value on each quadratic convexity curve, and the average value of the segmentation point values ​​on different curves is taken to obtain the optimal segmentation point configuration with a preset number of segmentation points; based on the requirements of storage space and classification accuracy, the number of segmentation points and the segmentation method are determined to obtain the determined layer table partitioning.

[0019] The present invention also provides a computer device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to implement the steps of the above method.

[0020] The present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the above-described method.

[0021] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0022] Compared with the prior art, the present invention has the following advantages and technical effects:

[0023] This invention establishes an integrated simulation model of the rolling mill roll system of a four-roll leveler through numerical simulation modeling and accumulation of production data. Based on the design and simulation calculation of the working condition table of the unit's production big data, the finite element simulation big data is analyzed to determine the layer table division principle. On this basis, the invention conducts research on the layer table division method for the pre-set plate shape of the leveler unit, solving the problem of poor control accuracy of the pre-set plate shape of the leveler unit in the existing technology, thereby significantly improving the plate shape quality of the strip steel products. Attached Figure Description

[0024] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings:

[0025] Figure 1 This is a schematic diagram of an integrated model of the rolling mill roll system according to an embodiment of the present invention;

[0026] Figure 2 This is the integrated calculation process for the rolling mill roll system according to an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram showing the relationship between strip width and secondary crown under different deformation resistances according to an embodiment of the present invention;

[0028] Figure 4 This is a schematic diagram illustrating the relationship between strip width and secondary crown under different elongation rates according to an embodiment of the present invention;

[0029] Figure 5 This is a schematic diagram showing the relationship between strip width and secondary crown at different thicknesses according to an embodiment of the present invention;

[0030] Figure 6 This is a schematic diagram showing the relationship between strip thickness and secondary crown under different deformation resistances according to an embodiment of the present invention;

[0031] Figure 7This is a schematic diagram illustrating the relationship between strip thickness and secondary crown under different elongation rates according to an embodiment of the present invention;

[0032] Figure 8 This is a schematic diagram showing the relationship between strip thickness and secondary crown at different widths according to an embodiment of the present invention;

[0033] Figure 9 This is a schematic diagram illustrating the relationship between strip elongation and secondary crown under different deformation resistances according to an embodiment of the present invention.

[0034] Figure 10 This is a schematic diagram showing the relationship between strip elongation and secondary crown at different thicknesses according to an embodiment of the present invention;

[0035] Figure 11 This is a schematic diagram showing the relationship between strip elongation and secondary crown at different widths according to an embodiment of the present invention;

[0036] Figure 12 This is a schematic diagram showing the relationship between deformation resistance and secondary convexity under different elongation rates according to an embodiment of the present invention;

[0037] Figure 13 This is a schematic diagram showing the relationship between deformation resistance and secondary convexity under different thicknesses in an embodiment of the present invention;

[0038] Figure 14 This is a schematic diagram showing the relationship between deformation resistance and secondary convexity under different widths in an embodiment of the present invention;

[0039] Figure 15 This is a schematic diagram illustrating the relationship between the number of dividing points and the expected value of the secondary convexity change in an embodiment of the present invention.

[0040] Figure 16 This is a schematic diagram illustrating the relationship between the number of dividing points and the rate of change of the expected value curve of the second convexity in an embodiment of the present invention.

[0041] Figure 17 This is a schematic diagram illustrating the expected value of the secondary convexity variation under different width division points according to an embodiment of the present invention.

[0042] Figure 18 This is a schematic diagram illustrating the rate of change of the expected value of the secondary convexity under different numbers of width division points in an embodiment of the present invention.

[0043] Figure 19 This is a schematic diagram illustrating the expected value of the secondary convexity variation under different numbers of thickness division points in an embodiment of the present invention.

[0044] Figure 20 This is a schematic diagram illustrating the rate of change of the expected value of the secondary convexity under different numbers of thickness division points in an embodiment of the present invention.

[0045] Figure 21This is a schematic diagram illustrating the expected value of the secondary convexity change under different numbers of elongation division points according to an embodiment of the present invention.

[0046] Figure 22 This is a schematic diagram illustrating the expected rate of change of the secondary convexity under different numbers of elongation division points according to an embodiment of the present invention.

[0047] Figure 23 This is a schematic diagram illustrating the expected value of the secondary convexity change under different numbers of deformation resistance division points in an embodiment of the present invention.

[0048] Figure 24 This is a schematic diagram illustrating the rate of change of the expected value of the secondary convexity under different numbers of deformation resistance division points in an embodiment of the present invention.

[0049] Figure 25 This is a flowchart of a method according to an embodiment of the present invention. Detailed Implementation

[0050] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0051] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0052] Example 1

[0053] like Figure 25 As shown, this embodiment provides a method for dividing a pre-defined layer table for a leveling unit, including:

[0054] An integrated simulation model of the rolling mill roll system was constructed based on the structure of a four-roll leveler.

[0055] As a specific implementation method, the process of constructing an integrated simulation model of the rolling mill roll system includes:

[0056] Based on the structure of the four-roll leveler and the theory of elastic deformation of the rolls, an elastic deformation model of the roll system of the four-roll leveler is established. Based on the structure of the four-roll leveler and the theory of three-dimensional plastic deformation of the workpiece, a plastic deformation model of the workpiece is established. The elastic deformation model of the roll system of the four-roll leveler and the plastic deformation model of the workpiece are combined to obtain an integrated simulation model of the roll system of the workpiece.

[0057] Specifically, the structural characteristics of the four-roll leveler are first analyzed, and two sub-models are established for the integrated rolling mill roll system. An elastic deformation model of the four-roll leveler roll system is established based on the theory of elastic deformation of rolls, and a plastic deformation model of the rolling mill is established based on the theory of three-dimensional plastic deformation of the rolling mill. The two sub-models are then combined to establish an integrated simulation model of the rolling mill roll system, such as... Figure 1 The diagram shown is a schematic of an integrated model of the rolling mill roll system.

[0058] Obtain actual production data of the four-roll leveler, and perform simulation calculations based on the actual production data and the integrated simulation model of the rolling mill roll system to obtain simulation big data.

[0059] As a specific implementation method, the process of obtaining simulation big data includes:

[0060] The actual production data of the four-roll leveler is obtained and preprocessed. A simulation working condition table is designed based on the preprocessed actual production data. Based on the integrated simulation model of the rolling mill roll system, all working condition parameters in the simulation working condition table are simulated and calculated to obtain simulation big data.

[0061] As a specific implementation method, the process of designing a simulation condition table includes:

[0062] Based on actual production data, the proportion of different strip steel specifications in all data is statistically analyzed. The weights are determined based on the proportions, and the density of strip steel parameters is determined based on the weight values.

[0063] Specifically, the production data of the leveling unit over the past year were statistically analyzed. The data was cleaned and preprocessed, and null values, duplicate data, and obviously erroneous data were deleted from the dataset. The remaining dataset was statistically analyzed according to strip width, thickness, elongation, and deformation resistance. The statistical results are shown in Table 1 below.

[0064] Table 1

[0065]

[0066] When designing the working condition table for simulating the strip shape at the leveling machine outlet, it is necessary to cover all strip specifications based on the actual production data of the leveling machine unit. The range of strip parameters in the working condition table should first include the extreme values ​​of the actual produced strip specifications, and secondly, it is necessary to subdivide the specific parameter values ​​for simulation. The simulation working condition table can be designed using the method of equal intervals between parameter values. The smaller the interval, the denser the simulation working condition parameter values, and the more accurate the simulation calculation results. However, too small a parameter interval will result in an excessively large amount of simulation calculation data, long calculation time, and increased computational burden. Therefore, it is sufficient to choose an appropriate interval. Alternatively, the working condition table can be designed using the weights of parameter ranges in the actual production data. First, the proportion of different strip specifications in all data is statistically analyzed based on the actual production data, and these proportions are assigned as corresponding weights. When designing the working condition table, the intervals with larger specification weights are divided more densely, and the intervals with smaller specification weights are divided more sparsely.

[0067] Taking the design of the working condition table using the method of equal spacing of each parameter as an example, the width is divided at intervals of 100mm, the thickness at intervals of 0.1mm, the elongation at intervals of 0.1%, and the deformation resistance at intervals of 50MPa. The simulation working condition table of the strip shape at the outlet of the leveling machine is shown in Table 2 below.

[0068] Table 2

[0069]

[0070]

[0071] Based on the combinations of all the operating parameters in the table above, a total of 35,360 operating conditions are obtained. Simulation calculations are then performed using an integrated simulation model of the rolling mill roll system to determine the lateral distribution of rolling force, the thickness distribution at the rolling mill exit, and other parameters. The integrated calculation process for the rolling mill roll system is as follows: Figure 2 As shown.

[0072] As a specific implementation method, based on the parameter combination in the working condition table, the initial lateral distribution of rolling force is assumed; based on the integrated simulation model of the rolling mill roll system, the exit cross-sectional shape of the strip after rolling is calculated; based on the exit cross-sectional shape and reduction rate, the actual lateral distribution of rolling force is calculated; the calculated lateral distribution of rolling force is compared with the assumed value, and if the accuracy requirements are not met, the assumed value is corrected and the calculation is repeated until the error conditions are met.

[0073] Specifically, before starting the calculation, the transverse distribution of the rolling force is assumed. Based on the elastic deformation of the roll system, the cross-sectional shape of the strip exit is obtained, and the three-dimensional plastic deformation calculation of the rolled piece begins. The transverse distribution of the rolling force is calculated based on the reduction rate and the exit thickness distribution. This is compared with the assumed value. If it does not meet the allowable accuracy range, the transverse distribution of the strip rolling force is corrected, and the next round of calculation is performed until the error condition is met. The final output includes the transverse distribution of the rolling force and the exit thickness distribution of the rolled piece.

[0074] Based on the simulation big data analysis, the correlation curves between each strip parameter and the secondary convexity of the strip cross section are obtained;

[0075] As a specific implementation method, the quadratic convexity is obtained by Chebyshev polynomial fitting based on the simulation big data, and the correlation curves between each strip parameter and the quadratic convexity of the strip cross section are plotted respectively; the strip parameters include thickness, elongation, deformation resistance and width.

[0076] When describing the shape of strip steel, two indices can be used: the convexity and the flatness of the strip steel cross-section. Here, we choose the convexity of the strip steel cross-section to describe the shape. Given the known thickness distribution law of the strip steel exit cross-section, we use the Chebyshev polynomial to describe the thickness distribution of the strip steel cross-section:

[0077] h(x) = a0 + a1x + a2x 2 +a4x 4

[0078] In the formula, a0 is a constant term, which is the thickness at the center of the strip cross-section; a1, a2, and a4 are the coefficients of the first, second, and fourth terms, respectively.

[0079] The quadratic component of the Chebyshev polynomial is the quadratic convexity C2, which is obtained from the following formula:

[0080] C2 = -(a2 + a4)

[0081] The quartic component of the Chebyshev polynomial is the quartic convexity C4, which is determined by the following formula:

[0082]

[0083] Since the order of magnitude of the fourth convexity C4 is smaller than that of the second convexity C2, it can be ignored. The second convexity C2 is used to represent the cross-sectional thickness distribution of the strip exit, i.e., the strip exit shape.

[0084] The simulation results were processed, and the quadratic convexity was obtained by fitting a Chebyshev polynomial. Some of the simulation results are shown in Table 3 below.

[0085] Table 3

[0086]

[0087] Based on the simulation results, the influence of various parameters of the strip on the secondary crown of the exit strip is analyzed, and the secondary crown curve is plotted.

[0088] The influence of strip width:

[0089] Strips of the same specifications with the same thickness, elongation, and deformation resistance are arranged in order of increasing width from minimum to maximum. The secondary convexity values ​​are connected by a smooth curve to form a secondary convexity curve. The relationship between width and secondary convexity is studied under different strip thicknesses, elongations, and deformation resistances.

[0090] Depend on Figures 3-5 It can be seen that the width and secondary crown are non-linear. As the strip width increases, the secondary crown also increases. Under the same width, as the thickness, elongation and deformation resistance increase, the secondary crown value also increases. The increase in elongation has the greatest impact on the change in secondary crown.

[0091] The effect of strip thickness:

[0092] Depend on Figures 6-8 It can be seen that the strip thickness and the secondary crown are nonlinear under different parameters. As the strip thickness increases, the secondary crown also increases. When other parameters increase, the secondary crown also increases.

[0093] The effect of strip elongation:

[0094] Depend on Figures 9-11 It can be seen that under different parameters, the strip elongation and secondary crown have a nonlinear relationship. As the strip elongation increases, the secondary crown also increases, and the change in secondary crown also increases. When other parameters increase, the secondary crown also increases.

[0095] Influence of deformation resistance:

[0096] Depend on Figures 12-14 It can be seen that as the deformation resistance of the strip increases, the secondary convexity of the strip cross section also increases, showing a basically linear relationship.

[0097] The optimal division scheme of the predefined layer table is obtained based on the correlation curve between strip parameters and the secondary convexity of the strip cross section.

[0098] As a specific implementation method, an optimization algorithm is used to find the optimal segmentation point value on each quadratic convexity curve, and the average value of the segmentation point values ​​on different curves is taken to obtain the optimal segmentation point configuration with a preset number of segmentation points; based on the requirements of storage space and classification accuracy, the number of segmentation points and the segmentation method are determined, and the hierarchical table is determined.

[0099] When determining the pre-set stratification table division principles, two aspects need to be considered: the determination of the stratification table division point intervals and the selection of the number of stratification table division points. First, the strip shape is represented by the secondary convexity of the strip cross-section. Based on the relationship between strip parameters and secondary convexity analyzed above, the principle of the pre-set stratification table division is determined. Different division points are found on the secondary convexity curve, and the secondary convexity values ​​of the division points are obtained. This ensures that the variation in the secondary convexity of the strip cross-section within each divided interval is uniformly minimized, ultimately resulting in a basically identical strip exit shape within each interval.

[0100] For the study of stratification table segmentation intervals, based on the principle of pre-defined stratification table division, the optimal segmentation point value is found on each quadratic convexity curve using an optimization algorithm. The average value of the segmentation point values ​​on different curves is then taken to obtain the optimal segmentation point value under a certain number of segmentation points.

[0101] Regarding the study of the number of division points in the layer table, a larger number of division points results in more intervals for each parameter, smaller variations in the secondary convexity of the intervals, and more similar strip exit shapes. This leads to a noticeable improvement in the post-rolling effect when using the same preset value. However, too many division points result in overly dense intervals for each parameter in the layer table, excessive content in the layer table, and a large amount of data stored in the mill's shape preset control system. This hinders the mill from calling accurate preset values ​​during multi-specification rapid rolling. Conversely, fewer division points result in less content in the layer table, facilitating storage by the shape preset control system. However, this leads to excessively large intervals for the layer table parameters and correspondingly large variations in the secondary convexity, causing differences in the strip exit shapes after rolling with the same preset value. Therefore, it is necessary to select an appropriate number of division points for the preset layer table based on different mill types and usage conditions.

[0102] First, based on the principle of minimizing the change in quadratic convexity within each interval after partitioning, an optimization algorithm is used to optimize the range of partition points with different numbers of partition points, and the average value is taken to obtain the optimal partition point value for each number of partition points. According to the known quadratic convexity curve, the quadratic convexity value of the optimal partition point is calculated using interpolation, and numerical calculations are performed to obtain the expected value of the change in quadratic convexity at the optimal partition point. A graph showing the relationship between the number of partition points and the expected value of the change in quadratic convexity is then plotted. Here, we take... Figure 15-16For example, as the number of partition points increases, the expected value of the change in quadratic convexity gradually decreases, and the rate of decrease also gradually decreases. Analyzing the rate of change of the expected value curve of the change in quadratic convexity under different numbers of partition points, the region with a rapid change in the expected value of the change in quadratic convexity is named the high-efficiency region, the region with a moderate rate of change is named the medium-efficiency region, and the region with a gradual rate of change is named the low-efficiency region. Depending on the requirements of the pre-defined tier table, a suitable number of partition points can be selected. For some enterprises that require saving storage space in the pre-defined system, the last number of partition points in the high-efficiency region can be used. If the enterprise requires the pre-defined tier table to save storage space while maintaining detailed classification, the number of partition points in the medium-efficiency region should be used. If the enterprise does not consider storage space and requires more accurate classification of the tier table, the first number of partition points in the low-efficiency region should be used.

[0103] Selection and design of the predefined layer division method for plate shape:

[0104] This embodiment selects the seasonal optimization algorithm to optimize the range and number of division points of the pre-set layer table of plate shape. The following is an introduction to the optimization algorithm.

[0105] 1) Seasonal optimization algorithm;

[0106] Based on the above-mentioned pre-defined stratification table partitioning principles, the Seasonal Optimization Algorithm (SOA) is used to optimize the pre-defined stratification table of the board shape. The Seasonal Optimization Algorithm is a biomimetic optimization algorithm inspired by the four-season growth cycle of trees. Its core idea is to achieve a balance between global search and local search by simulating the ecological behavior of trees in spring, summer, autumn and winter (updating, competition, sowing, removal).

[0107] Spring Update Phase:

[0108] Simulates the process of seeds germinating and producing new seedlings in spring:

[0109] F y =F y-1 ∪R

[0110] R=φ(P r ×A y-1 )

[0111]

[0112] In the formula, F y —The solution set of the y-th iteration;

[0113] R — the newly generated set of seedlings, generated by a random function φ;

[0114] P r —Dynamic update rate;

[0115] Y — Maximum number of iterations;

[0116] P maxP min — Boundary values ​​for update rate;

[0117] A y-1 —The number of seeds sown in the previous autumn.

[0118] Summer competition phase:

[0119] Simulate summer resource competition to enhance the local exploitation capabilities of high-quality solutions:

[0120] N c =P C ×N

[0121]

[0122] In the formula, N c —The number of core trees is determined by a random proportion P. C Decide.

[0123] —Location of the core tree;

[0124] Λ j — The crowding coefficient reflects the competitive pressure within the neighborhood.

[0125] Autumn sowing stage:

[0126] Simulate strong tree seed dispersal to preserve dominant genes:

[0127] A = P s ×N

[0128] In the formula, P s —Sowing rate, and P r similar.

[0129] Winter removal phase:

[0130] Simulate natural selection of weak trees to maintain population quality:

[0131] W = χ(P) w ×N)

[0132]

[0133] In the formula, W represents the set of weak trees that were removed.

[0134] χ—Removal function, eliminating the P with the lowest fitness. w ×N trees.

[0135] In the seasonal optimization algorithm's four-season cycle, the search strategy is dynamically adjusted by the changing seasons: spring focuses on global search, summer on local exploration, and winter forces the algorithm to escape local optima, maintaining population diversity. This seasonal switching mechanism periodically resets the search strategy, effectively reducing the risk of getting trapped in local optima and achieving a better balance between global search and local optimization.

[0136] 2) Optimization results of the pre-defined layer table for plate shape;

[0137] Based on the simulated quadratic convexity curve, a seasonal optimization algorithm was used to optimize the stratification table for different values ​​and numbers of dividing points. The number of dividing points was increased from 5 to 21, and the optimal dividing point values ​​were determined for each value. The expected value of the quadratic convexity change in the divided interval was calculated for each number of dividing points. A graph showing the relationship between the number of dividing points and the expected value of the quadratic convexity change was plotted. Based on the rate of change of the expected value of the quadratic convexity change, the stratification area was divided into high-efficiency, medium-efficiency, and low-efficiency regions. The division results are shown below. Figure 17 , 18 As shown.

[0138] Depend on Figure 17 , 18 It can be seen that the more division points there are, the more intervals are divided, resulting in a smaller and smaller expected value of the change in the second convexity. At the same time, as the number of division points increases, the change in the expected value of the change in the second convexity decreases and the rate of change of the expected value curve of the second convexity decreases and becomes flatter. Therefore, based on the magnitude of the rate of change of the expected value of the change in the second convexity, it is divided into three parts.

[0139] The expected values ​​and rates of change of the secondary convexity under different numbers of thickness division points, different numbers of elongation division points, and different numbers of deformation resistance division points, such as... Figures 19-24 As shown.

[0140] Choose the appropriate hierarchical table partitioning method based on the company's requirements for the pre-defined system. The following are some partitioning methods:

[0141] ① The enterprise requires the hierarchical table to have minimal storage space. It selects the last partition point in the high-efficiency region, setting the number of partition points to 10. The partitioning result is as follows:

[0142] Width division [800, 908, 1008, 1101, 1191, 1279, 1368, 1460, 1564, 1700].

[0143] Thickness division [0.5, 0.62, 0.76, 0.91, 1.08, 1.26, 1.46, 1.66, 1.87, 2.1].

[0144] Elongation rate is divided into [0.50, 0.65, 0.79, 0.93, 1.07, 1.20, 1.33, 1.46, 1.58, 1.70].

[0145] Deformation resistance classification [50, 134, 218, 301, 384, 467, 549, 633, 716, 800].

[0146] ② The enterprise needs a pre-defined stratification table that requires both minimal storage space and detailed categorization. It selects 15 partition points for the medium-efficiency region. The partitioning result is as follows:

[0147] Width division [800, 870, 936, 1000, 1062, 1123, 1183, 1243, 1303, 1363, 1423, 1484, 1547, 1615, 1700].

[0148] Thickness division [0.50, 0.59, 0.68, 0.79, 0.89, 1.00, 1.12, 1.24, 1.35, 1.47, 1.59, 1.71, 1.84, 1.96, 2.10].

[0149] Elongation rates are divided into [0.50, 0.59, 0.68, 0.77, 0.86, 0.95, 1.03, 1.12, 1.20, 1.29, 1.37, 1.45, 1.54, 1.62, 1.70].

[0150] Deformation resistance classification [50, 104, 158, 211, 265, 318, 371, 424, 477, 530, 584, 637, 691, 745, 800].

[0151] ③ If the enterprise does not consider storage space and requires more detailed categorization of the hierarchical table, select the number of partition points for the inefficient area, with 17 partition points. The partitioning result is as follows:

[0152] Width division [800, 861, 919, 975, 1030, 1084, 1138, 1192, 1245, 1298, 1351, 1405, 1458, 1512, 1568, 1628, 1700].

[0153] Thickness division [0.50, 0.58, 0.67, 0.76, 0.85, 0.95, 1.05, 1.15, 1.25, 1.36, 1.46, 1.56, 1.67, 1.77, 1.88, 1.98, 2.10].

[0154] Elongation rates are divided into [0.50, 0.58, 0.66, 0.74, 0.81, 0.89, 0.96, 1.04, 1.11, 1.19, 1.26, 1.34, 1.41, 1.48, 1.55, 1.63, 1.70].

[0155] Deformation resistance classification [50, 98, 145, 192, 239, 285, 332, 379, 425, 472, 519, 565, 612, 659, 705, 753, 800].

[0156] This embodiment also provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above method.

[0157] This embodiment also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the above-described method.

[0158] This embodiment also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the above-described method.

[0159] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for dividing a profile preset layer table for a flatness unit, characterized in that, The method comprises the following steps: An integrated simulation model of the rolling piece roller system is constructed based on the structure of the four-high temper mill; Actual production data of the four-high temper mill is obtained, and simulation calculation is performed based on the actual production data and the integrated simulation model of the rolling piece roller system to obtain simulation big data; Based on the simulation big data analysis, a correlation curve of each strip parameter and the second convexity of the strip cross section is obtained; Based on the correlation curve of the strip parameter and the second convexity of the strip cross section, an optimal division scheme of the preset layer table is obtained; According to the parameter combination in the working condition table, an initial rolling force transverse distribution is assumed; Based on the integrated simulation model of the rolling piece roller system, the exit cross section shape of the strip after rolling is calculated; Based on the exit cross section shape and the reduction rate, the actual rolling force transverse distribution is calculated; The calculated rolling force transverse distribution is compared with the assumed value, and if the accuracy requirement is not met, the assumed value is corrected and the calculation is performed again until the error condition is met; Based on the simulation big data, the second convexity is fitted by Chebyshev polynomial to draw a correlation curve of each strip parameter and the second convexity of the strip cross section; the strip parameters include thickness, elongation, deformation resistance and width; An optimal division point configuration of the preset number of division points is obtained by finding the optimal division point value on each second convexity curve and taking the average value of the division point values on different curves; the number of division points and the division method are determined according to the storage space and the classification accuracy requirement to obtain the layer table division.

2. The division method for the plate shape preset layer table of the temper mill set according to claim 1, characterized in that, The process of constructing the integrated simulation model of the rolling piece roller system comprises: Based on the structure of the four-high temper mill and the elastic deformation theory of the rolling roller, an elastic deformation model of the four-high temper mill roller system is established, and based on the structure of the four-high temper mill and the three-dimensional plastic deformation theory of the rolling piece, a rolling piece plastic deformation model is established, and the elastic deformation model of the four-high temper mill roller system and the rolling piece plastic deformation model are combined to obtain the integrated simulation model of the rolling piece roller system.

3. The division method for the plate shape preset layer table of the temper mill set according to claim 1, characterized in that, The process of obtaining simulation big data comprises: Actual production data of the four-high temper mill is obtained and preprocessed, and a simulation working condition table is designed based on the preprocessed actual production data; simulation calculation is performed on all working condition parameters in the simulation working condition table based on the integrated simulation model of the rolling piece roller system to obtain simulation big data.

4. The division method for the plate shape preset layer table of the temper mill set according to claim 3, characterized in that, The process of designing the simulation working condition table comprises: According to the actual production data, the proportion of different strip specifications in all data is counted, the weight is determined according to the proportion, and the interval range division density of the strip parameters is determined according to the weight value.

5. A computer apparatus comprising: A memory and a processor for storing a computer program stored on the memory and executable on the processor, characterized in that the processor executes the computer program to implement the steps of the method of any one of claims 1-4.

6. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1-4.

7. A computer program product comprising a computer program, characterized in that, The computer program, which is executed by the processor, implements the steps of the method according to any one of claims 1 to 4.

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