Partitioning method suitable for preset layer table of plate shapes of temper mill unit

Abstract

The invention discloses a division method suitable for a temper mill strip shape preset layer table. The division method comprises the steps that a rolled piece roller system integrated simulation model is constructed based on the structure of a four-roller temper mill; actual production data of the four-roller temper mill are obtained, simulation calculation is conducted on the basis of the actual production data and the rolled piece roller system integrated simulation model, and simulation big data are obtained; a correlation curve of each strip steel parameter and the secondary convexity of the strip steel cross section is obtained based on simulation big data analysis; and obtaining an optimal division scheme of the preset layer table based on the correlation curve of the strip steel parameters and the secondary convexity of the strip steel cross section. The problem that in the prior art, the plate shape presetting control precision of a temper mill unit is poor is solved, and therefore the plate shape quality of plate strip steel products is remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of plate and strip steel rolling production, and in particular relates to a method for dividing a pre-set layer table of plate shape suitable for a leveling mill. Background Art

[0002] As a key process in the production of plate and strip steel, leveling directly affects the surface quality, mechanical properties and plate shape quality of the product. During the leveling production process, the plate shape control system controls the values ​​of each actuator to change the roll gap shape, so as to achieve the goal of the actual plate shape being close to the target plate shape. At present, the leveling machine of the common hot-dip galvanizing unit for producing automobile plates generally adopts a single-frame four-roller leveling machine. The main means of adjusting the plate shape of the four-roller leveling machine is the working roll bending control. At the same time, in order to ensure the surface quality of export products, most leveling machines are not equipped with contact plate shape meters and other detection equipment at the end, resulting in the leveling machine being unable to achieve closed-loop feedback control of the plate shape quality. It can only rely on plate shape pre-setting and feedforward control to achieve plate shape control of the strip steel. Therefore, ensuring the control accuracy of plate shape pre-setting control and feedforward control is a prerequisite for producing high-quality strip steel products.

[0003] Plate shape presetting control means that before the strip starts rolling, the initial setting values ​​of each regulating mechanism are given by its incoming material information, such as width, thickness, convexity, and other process information, so that the deviation from the target plate shape can be as small as possible before stable rolling. At present, plate shape presetting control mainly adopts the form of a preset table. By dividing the width, thickness, elongation and other specifications of the incoming strip in advance, a plate shape preset layer table is made, and the preset values ​​of each adjustment means are set for strips of different incoming material specifications. The rationality of the division level of the plate shape preset layer table has an important influence on the accuracy of the preset values ​​of the set plate shape preset table. In order to better ensure the plate shape quality of the flattening mill strip, a division method suitable for the plate shape preset layer table of the flattening mill is proposed. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a division method suitable for a pre-set layer table of plate shape of a leveling machine unit to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above object, the present invention provides a method for dividing a pre-set layer table of plate shape for a leveling mill, comprising:

[0006] Construct an integrated simulation model of the rolling mill roll system based on the structure of the four-roller skin-pass mill;

[0007] Acquire actual production data of a four-roller skin-pass mill, and perform simulation calculations based on the actual production data and the integrated simulation model of the rolling stock roll system to obtain simulation big data;

[0008] Obtaining a correlation curve between each strip parameter and the secondary convexity of the strip cross section based on the simulation big data analysis;

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

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

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

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

[0013] The actual production data of the four-roller skin-pass mill is obtained and preprocessed, and a simulation working condition table is designed based on the preprocessed actual production data; all working condition parameters in the simulation working condition table are simulated and calculated based on the integrated simulation model of the rolled piece roll system to obtain simulation big data.

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

[0015] According to the actual production data, the proportion of different strip steel specifications in the total data is counted, the weight is determined according to the proportion, and the density of the interval range division of the strip steel parameters is determined according to the weight value.

[0016] Optionally, based on the parameter combination in the working condition table, an initial lateral distribution of rolling force is assumed; based on the integrated simulation model of the rolling stock roll system, the outlet cross-sectional shape of the strip after rolling is calculated; based on the outlet cross-sectional shape and the reduction rate, the actual lateral distribution of rolling force is calculated; the calculated lateral distribution of rolling force is compared with the assumed value. If the accuracy requirement is not met, the assumed value is corrected and the calculation is performed again until the error condition is met.

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

[0018] Optionally, an optimization algorithm is used to find the optimal split point value on each quadratic convexity curve, and the split point values ​​on different curves are averaged to obtain the optimal split point configuration for a preset number of split points; based on the requirements of storage space and classification accuracy, the number of split points and the splitting method are determined to obtain a determined stratification table division.

[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, wherein the processor executes 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 implements the steps of the above method when executed by a processor.

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

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

[0023] The present invention establishes an integrated simulation model of the rolling mill roll system of a four-roller leveler through numerical simulation modeling and accumulation of production data. It designs and simulates the working condition table based on the unit's production big data, analyzes the finite element simulation big data, and determines the principle of dividing the layer table. On this basis, research is carried out on the method of dividing the leveler unit's plate shape preset layer table, which solves the problem of poor control accuracy of the leveler unit's plate shape preset in the existing technology, thereby significantly improving the plate shape quality of plate and strip steel products. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0025] Figure 1 Schematic diagram of an integrated model of a rolling stock roller system according to an embodiment of the present invention;

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

[0027] Figure 3 Schematic diagram of the relationship between strip width and secondary crown under different deformation resistances in an embodiment of the present invention;

[0028] Figure 4 Schematic diagram of the relationship between strip width and secondary crown at different elongation rates in an embodiment of the present invention;

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

[0030] Figure 6 Schematic diagram of the relationship between strip thickness and secondary crown under different deformation resistances in an embodiment of the present invention;

[0031] Figure 7Schematic diagram of the relationship between strip thickness and secondary crown at different elongations in an embodiment of the present invention;

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

[0033] Figure 9 Schematic diagram of the relationship between the elongation and secondary crown of the strip steel under different deformation resistances according to an embodiment of the present invention;

[0034] Figure 10 Schematic diagram of the relationship between the elongation and secondary crown of the strip steel at different thicknesses according to an embodiment of the present invention;

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

[0036] Figure 12 Schematic diagram of the relationship between deformation resistance and secondary convexity at different elongation rates according to an embodiment of the present invention;

[0037] Figure 13 Schematic diagram of the relationship between deformation resistance and secondary convexity at different thicknesses according to an embodiment of the present invention;

[0038] Figure 14 Schematic diagram of the relationship between deformation resistance and secondary convexity at different widths according to an embodiment of the present invention;

[0039] Figure 15 Schematic diagram of the relationship between the number of partition points and the expected value of the quadratic convexity change in an embodiment of the present invention;

[0040] Figure 16 Schematic diagram of the relationship between the number of division points and the rate of change of the expected value curve of the quadratic convexity change according to an embodiment of the present invention;

[0041] Figure 17 Schematic diagram of expected values ​​of quadratic convexity variation under different numbers of width division points according to an embodiment of the present invention;

[0042] Figure 18 Schematic diagram of the change rate of the expected value of the quadratic convexity change under different numbers of width division points in an embodiment of the present invention;

[0043] Figure 19 Schematic diagram of expected values ​​of quadratic convexity variation under different numbers of thickness division points according to an embodiment of the present invention;

[0044] Figure 20 Schematic diagram of the change rate of the expected value of the secondary convexity change amount under different numbers of thickness division points in an embodiment of the present invention;

[0045] Figure 21Schematic diagram of expected values ​​of quadratic convexity variation under different numbers of elongation division points according to an embodiment of the present invention;

[0046] Figure 22 Schematic diagram of the change rate of the expected value of the secondary convexity change amount under different numbers of elongation division points in an embodiment of the present invention;

[0047] Figure 23 Schematic diagram of expected values ​​of quadratic convexity variation under different numbers of deformation resistance division points according to an embodiment of the present invention;

[0048] Figure 24 Schematic diagram of the change rate of the expected value of the secondary convexity change under different numbers of deformation resistance division points in an embodiment of the present invention;

[0049] Figure 25 Flowchart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0050] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

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

[0052] Example 1

[0053] like Figure 25 As shown, this embodiment provides a method for dividing a pre-set layer table of plate shape for a leveling mill, including:

[0054] Construct an integrated simulation model of the rolling mill roll system based on the structure of the four-roller skin-pass mill;

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

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

[0057] Specifically, the structural characteristics of the four-roller skin-pass mill are first analyzed, and two sub-models of the integrated rolling mill roll system are established respectively. The elastic deformation model of the four-roller skin-pass mill roll system is established based on the elastic deformation theory of the rolls, and the plastic deformation model of the rolled piece is established based on the three-dimensional plastic deformation theory of the rolled piece. The two sub-models are combined to establish the integrated simulation model of the rolled piece roll system, as shown in the following figure. Figure 1 Shown is a schematic diagram of the integrated model of the rolling stock roller system.

[0058] Acquire actual production data of a four-roller skin-pass mill, and perform simulation calculations based on the actual production data and the integrated simulation model of the rolling stock 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-roller skin-pass mill is obtained and preprocessed, and a simulation working condition table is designed based on the preprocessed actual production data; all working condition parameters in the simulation working condition table are simulated and calculated based on the integrated simulation model of the rolled piece roll system to obtain simulation big data.

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

[0062] According to the actual production data, the proportion of different strip steel specifications in the total data is counted, the weight is determined according to the proportion, and the density of the interval range division of the strip steel parameters is determined according to the weight value.

[0063] Specifically, the production data of the leveling mill for the past year was collected, and the data was cleaned and preprocessed. Empty values, identical data, and obviously erroneous data in the data set were deleted. The data in the remaining data set were counted according to the 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 the simulation calculation of the strip shape at the outlet of the leveling mill, all specifications of strip steel should be covered according to the actual production data of the leveling mill unit. The range of strip steel parameters in the working condition table should first include the extreme values ​​of the actual produced strip steel specifications. Secondly, the specific simulation parameter values ​​need to be subdivided. The simulation working condition table can be designed by using the method of equal spacing of each parameter value. The smaller the division interval, the denser the simulation working condition parameter values ​​and the more accurate the simulation calculation results. However, an excessively small parameter interval will result in an excessively large amount of simulation calculation working condition data, a long calculation time, and an increased calculation burden. Therefore, a suitable interval can be selected. The weight of the parameter range in the actual production data can also be used to design the working condition table. First, the proportion of different strip steel specifications in the total data is counted according to the actual production data, and it is set as the corresponding weight. When designing the working condition table, the interval range with large specification weight is divided more densely, and the interval range with small specification weight is divided more sparsely.

[0067] The present invention takes the method of designing the working condition table with equal spacing of various parameters as an example. The width is divided into intervals of 100 mm, the thickness is divided into intervals of 0.1 mm, the elongation is divided into intervals of 0.1%, and the deformation resistance is divided into intervals of 50 MPa. The simulated 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] According to the combination of all the working condition parameters in the above table, there are a total of 35,360 working conditions. The integrated simulation model of the rolling mill roll system is used for simulation calculation to calculate the lateral distribution of rolling force, the thickness distribution of the rolled product outlet, etc. Figure 2 shown.

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

[0073] Specifically, before the calculation begins, an assumption is made about the lateral distribution of rolling force. Based on the elastic deformation of the roll system, the exit cross-sectional shape of the strip is calculated, which then proceeds to the 3D plastic deformation calculation of the rolled piece. The lateral distribution of rolling force is calculated based on the reduction ratio and exit thickness distribution. This value is then compared with the assumed value. If it does not meet the accuracy tolerance, the lateral distribution of rolling force is corrected, and the next round of calculations is performed until the tolerance is met. The final output includes the lateral distribution of rolling force and the exit thickness distribution of the rolled piece.

[0074] Obtaining a correlation curve between each strip parameter and the secondary convexity of the strip cross section based on the simulation big data analysis;

[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 drawn respectively; the strip parameters include thickness, elongation, deformation resistance and width.

[0076] When describing the strip shape, the convexity and flatness of the strip cross section can be used respectively. Here, the convexity of the strip cross section is selected to describe the strip shape. Given the thickness distribution law of the strip outlet cross section, the Chebyshev polynomial is used to describe the strip cross section thickness distribution:

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

[0078] Where, 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-order term, the second-order term, and the fourth-order term, respectively.

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

[0080] C2=-(a2+a4)

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

[0082]

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

[0084] The simulation results were processed and the quadratic convexity was fitted using Chebyshev polynomials. Some simulation results are shown in Table 3 below.

[0085] Table 3

[0086]

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

[0088] Influence of strip width:

[0089] Strip steels with the same specifications of thickness, elongation and deformation resistance are arranged in order of width from the minimum to the maximum value, and the secondary convexity values ​​are connected with a smooth curve to draw a secondary convexity curve. The relationship between width and secondary convexity under different strip thickness, elongation and deformation resistance is studied respectively.

[0090] Depend on Figure 3-Figure 5 It can be seen that the width and the secondary crown are nonlinear. As the width of the strip 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] Influence of strip thickness:

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

[0093] Influence of strip elongation:

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

[0095] Influence of deformation resistance:

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

[0097] The optimal division scheme of the pre-set layer table is obtained based on the correlation curve between the 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 split point value on each quadratic convexity curve, and the split point values ​​on different curves are averaged to obtain the optimal split point configuration for a preset number of split points; according to the requirements of storage space and classification accuracy, the number of split points and the split method are determined to determine the layer table division.

[0099] When determining the principles for pre-set stratification table division, two key aspects need to be considered: one is the determination of the stratification table segmentation point intervals, and the other is the selection of the number of stratification table segmentation points. First, the secondary convexity of the strip cross section is used to represent the strip shape. Based on the relationship between strip parameters and secondary convexity analyzed above, the principle of pre-set stratification table division is determined. Different segmentation points are found on the secondary convexity curve, and the secondary convexity values ​​of the segmentation points are obtained. This ensures that the variation in secondary convexity of the strip cross section within each segmentation interval uniformly reaches the minimum value, ultimately achieving essentially the same strip export shape within each segment.

[0100] For the study of the segmentation interval of the stratification table, based on the principle of pre-set stratification table division, the optimization algorithm is used to find the optimal segmentation point value on each quadratic convexity curve, and the segmentation point values ​​on different curves are averaged to obtain the optimal segmentation point value under a certain number of segmentation points.

[0101] Regarding the study on the number of division points in the layer table, when the number of division points is greater, the more intervals each parameter is divided into, the smaller the secondary convexity change in the division interval, the more similar the strip outlet plate shape is, and the effect after rolling is obvious when the same preset value is used for processing. However, too many division points will cause the intervals of each parameter in the layer table to be too dense, the content of the layer table to be too much, and the amount of data stored in the plate shape preset control system of the rolling mill to be too large, which is not conducive to the rolling mill to call accurate preset values ​​when rolling multiple specifications rapidly; when the number of division points is small, the content of the layer table formed is small, which is convenient for the plate shape preset control system to store, but at the same time, the interval range of the layer table parameters is too large, and the corresponding secondary convexity change is too large, which will lead to differences in the strip outlet plate shape after rolling using the same preset value. Therefore, it is necessary to select an appropriate number of division points for the preset layer table according to different rolling mill types and usage conditions.

[0102] First, based on the principle that the change in quadratic convexity in each interval after division reaches the minimum value, the optimization algorithm is used to optimize the range of division points with different numbers of division points, and the average value is taken to obtain the optimal division point value under each number of division points; according to the known quadratic convex curve, the quadratic convexity value of the optimal division point value is calculated by interpolation method, and the expected value of the quadratic convexity change of the optimal division point is obtained by numerical calculation, and the relationship between the number of division points and the expected value of the quadratic convexity change is plotted. Figure 15-16For example, as the number of division points increases, the expected value of the quadratic convex change gradually decreases and the rate of decrease gradually decreases. The rate of change of the expected value curve of the quadratic convex change under different numbers of division points is analyzed, and the area with a fast change in the expected value of the quadratic convex change is named the high-efficiency area, the area with a general rate of change is named the medium-efficiency area, and the area with a gentle rate of change is named the low-efficiency area. The appropriate number of division points is selected according to the different requirements of the preset layer table. For some companies that require saving storage space in the preset system, the last number of division points in the high-efficiency area can be selected. If the company requires the preset stratification table to save storage space and have detailed classification, the number of division points in the medium-efficiency area should be selected. If the company does not consider storage space and requires more accurate classification of the stratification table, the first number of division points in the low-efficiency area should be selected.

[0103] Selection and design of the division method of the plate shape preset layer table:

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

[0105] 1) Seasonal optimization algorithm;

[0106] Based on the above-mentioned pre-set stratification table division principle, the seasonal optimization algorithm (SOA) is used to optimize the board shape pre-set stratification table. The seasonal optimization algorithm is a bionic 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 (renewal, competition, seeding, and removal) of trees in spring, summer, autumn, and winter.

[0107] Spring update phase:

[0108] Simulate the process of spring seed germination to produce new seedlings:

[0109] F y =F y-1 ∪R

[0110] R=φ(P r ×A y-1 )

[0111]

[0112] Where, F y ——the solution set of the yth iteration;

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

[0114] P r ——Dynamic update rate;

[0115] Y——maximum number of iterations;

[0116] P max、P min - the boundary value of the update rate;

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

[0118] Summer competition stage:

[0119] Simulate summer resource competition and enhance local development capabilities of high-quality solutions:

[0120] N c =P C ×N

[0121]

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

[0123] ——Core tree location;

[0124] Λ j ——Crowding coefficient, reflecting the competitive pressure within the neighborhood.

[0125] Autumn sowing stage:

[0126] Simulate strong trees to spread seeds and retain dominant genes:

[0127] A=P s ×N

[0128] Where, P s ——seeding rate, and P r similar.

[0129] Winter Removal Phase:

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

[0131] W=χ(P w ×N)

[0132]

[0133] Where W is the set of weak trees to be removed;

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

[0135] The seasonal optimization algorithm dynamically adjusts its search strategy through the four-season cycle, focusing on global search in spring, local development in summer, and forcing it to escape local optima in winter to maintain population diversity. This seasonal switching mechanism periodically resets the search strategy, effectively reducing the risk of falling into local optima and achieving a better balance between global search and local optimization.

[0136] 2) Optimization results of the plate shape preset layer table;

[0137] Based on the quadratic convexity curve calculated by simulation, the seasonal optimization algorithm is used to optimize the values ​​and numbers of different division points. The number of division points is increased from 5 to 21, and the optimal division point values ​​of the layer table are optimized respectively. The expected value of the quadratic convexity change of the division interval under each number of division points is calculated, and a graph of the number of division points and the expected value of the quadratic convexity change is drawn. The area is divided into high-efficiency area, medium-efficiency area and low-efficiency area according to the rate of change of the expected value of the quadratic convexity change. The division results are shown as follows: Figure 17 、 18 shown.

[0138] Depend on Figure 17 、 18 It can be seen that the more dividing points there are, the more intervals there are, which leads to a smaller and smaller expected value of the quadratic convex change. At the same time, as the number of dividing points increases, the change in the expected value of the quadratic convex change becomes smaller and smaller, and the rate of change of the expected value of the quadratic convex change curve shows a decreasing trend and becomes more and more gentle. Therefore, according to the size of the rate of change of the expected value of the quadratic convex change, it is divided into three parts.

[0139] The expected value and expected value change rate 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 are as follows: Figures 19-24 shown.

[0140] Select the appropriate stratification table division method based on the company's requirements for the pre-set system. The following are several division methods:

[0141] ① The enterprise requires that the storage space of the hierarchical table be as small as possible. The last number of partition points in the high-efficiency zone is selected, and the number of partition points is 10. The partition result is:

[0142] Width partition [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 ratio 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 to save storage space and have detailed classification for the pre-set stratification table. The number of division points in the medium efficiency zone is selected as 15. The division results are as follows:

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

[0148] Thickness divided by [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 is 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 a more detailed classification of the stratification table, the number of division points in the inefficient area is selected, and the number of division points is 17. The division results are:

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

[0153] Thickness divided by [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 is 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 further provides a computer device, comprising: 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 further provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

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

[0159] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A method for dividing a pre-set layer table of plate shape for a flattening mill, characterized in that: The following steps are involved: Construct an integrated simulation model of the rolling mill roll system based on the structure of the four-roller skin-pass mill; Acquire actual production data of a four-roller skin-pass mill, and perform simulation calculations based on the actual production data and the integrated simulation model of the rolling stock roll system to obtain simulation big data; Obtaining a correlation curve between each strip parameter and the secondary convexity of the strip cross section based on the simulation big data analysis; The optimal division scheme of the pre-set layer table is obtained based on the correlation curve between the strip parameters and the secondary convexity of the strip cross section.

2. The method for dividing the pre-set layer table of plate shape applicable to the leveling mill according to claim 1, characterized in that: The process of building an integrated simulation model of the rolling stock roll system includes: Based on the structure of the four-roller skin-pass mill and the elastic deformation theory of the rolls, an elastic deformation model of the four-roller skin-pass mill roll system is established. Based on the structure of the four-roller skin-pass mill and the three-dimensional plastic deformation theory of the rolled piece, a plastic deformation model of the rolled piece is established. The elastic deformation model of the four-roller skin-pass mill roll system and the rolled piece plastic deformation model are combined to obtain an integrated simulation model of the rolled piece roll system.

3. The method for dividing the pre-set layer table of plate shape applicable to the leveling mill according to claim 1, characterized in that: The process of obtaining simulation big data includes: The actual production data of the four-roller skin-pass mill is obtained and preprocessed, and a simulation working condition table is designed based on the preprocessed actual production data; all working condition parameters in the simulation working condition table are simulated and calculated based on the integrated simulation model of the rolled piece roll system to obtain simulation big data.

4. The method for dividing the pre-set layer table of plate shape applicable to the leveling mill according to claim 3, characterized in that: The process of designing a simulation load table includes: According to the actual production data, the proportion of different strip steel specifications in the total data is counted, the weight is determined according to the proportion, and the density of the interval range division of the strip steel parameters is determined according to the weight value.

5. The method for dividing the pre-set layer table of plate shape applicable to the leveling mill according to claim 1, characterized in that: 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 stock roll system, the exit cross-sectional shape of the strip after rolling is calculated; The actual lateral distribution of rolling force is calculated based on the outlet cross-sectional shape and the reduction rate; the calculated lateral distribution of rolling force is compared with the assumed value. If the accuracy requirements are not met, the assumed value is corrected and the calculation is repeated until the error conditions are met.

6. The method for dividing the pre-set layer table of plate shape applicable to the leveling mill according to claim 1, characterized in that: Based on the simulation big data, the quadratic convexity is obtained by fitting the Chebyshev polynomial, and correlation curves between each strip parameter and the quadratic convexity of the strip cross section are drawn respectively; the strip parameters include thickness, elongation, deformation resistance and width.

7. The method for dividing the pre-set layer table of plate shape applicable to a flattening mill according to claim 1, characterized in that: An optimization algorithm is used to find the optimal split point value on each quadratic convexity curve, and the split point values ​​on different curves are averaged to obtain the optimal split point configuration for the preset number of split points. According to the requirements of storage space and classification accuracy, the number of split points and the split method are determined to obtain the stratification table division.

8. A computer device comprising: 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 method according to any one of claims 1 to 7.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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