A method and device for predicting the width between stands in hot rolling production process

Abstract

The present application proposes a method and device for predicting the width between racks in a hot rolling production process, which belongs to the technical field of automatic control of steel rolling. The method includes: obtaining various parameters of the rack entrance in the hot rolling production process; inputting various parameters of the rack entrance into a pre-established width prediction model to obtain the width of each rack in the hot rolling production process, and obtaining the final outlet width, the outlet width of each upstream rack, and the outlet width of each downstream rack according to the strip entrance width and the width of each rack. The pre-established width prediction model has the same model structure and different coefficients, and the model structure is determined by finite element analysis, and the optimal coefficient of each width prediction model is determined by the gray wolf algorithm. The present application takes into account the tension between racks and the deformation resistance of each rack rolled piece, and takes into account the hereditary influence of the upstream rack on the downstream rack, thereby improving the accuracy of width prediction.

Description

Technical Field

[0001] The invention belongs to the technical field of steel rolling automatic control, and in particular relates to a method and a device for predicting the width between stands in a hot rolling production process. Background Art

[0002] Strip width is one of the important quality indicators of hot-rolled products. Accurate prediction of width can not only improve the width control accuracy of the final product, but also provide a prerequisite for high-precision prediction of rolling force of downstream stands. With the improvement of customer requirements for product quality and the demand for high-quality and high-efficiency production by manufacturers, how to improve the prediction accuracy of the width of the rolling process has become a focus of attention in the hot-rolling production process.

[0003] The finishing process is an important part of hot rolling strip production, and the width prediction accuracy of the finishing process plays a decisive role in the quality of the final product. During the hot rolling process, the hot rolling production line generally installs side width meters at the rough rolling exit and the finishing rolling exit to measure the rolled product in real time. However, due to the close distance between the finishing rolling stands, it is impossible to install a measuring device to measure the width between the stands online, which makes it difficult to accurately obtain the width change of the rolled product during the rolling process.

[0004] Aiming at the prediction of rolled piece width in hot rolling process, many researchers have carried out extensive research work by using finite element simulation and data-driven modeling.

[0005] The Chinese patent "CN 106694570 A A method for calculating the width expansion of a hot rolling finishing zone stand after rolling" proposes a method for calculating the width expansion of a hot rolling finishing zone stand after rolling. The average thickness of the rolled piece is calculated based on the rolling force of each stand and the roll gap position. Based on the principle of constant flow rate per second, the exit width of each stand is calculated to obtain the width expansion of each stand after rolling. The Chinese patent "CN 113102516 A A method for predicting the head width of hot rolling strip by integrating rolling mechanism and deep learning" proposes a method for predicting the head width of hot rolling strip by integrating rolling mechanism and deep learning. First, the data of factors affecting the rolling width expansion are screened, and a prediction model of the rolling mechanism of each stand is constructed. The predicted reference value of the strip head width is calculated based on the data, and a deep confidence neural network model is constructed to predict the correction value of the strip head width. Finally, the predicted reference value and the predicted correction value are added to obtain the final predicted value of the width of the strip head at the measured position at the exit. The Chinese patent "CN117840232 A A method for predicting the width of a hot rolling process based on incremental learning" proposes a method for predicting the width of a hot rolling process based on incremental learning. It uses incremental learning to model the prediction of the width of the strip during the finishing process, and responds to changes in real-time production data to achieve accurate prediction of the finishing width of the strip.

[0006] The existing finite element (FEM) analysis in the field of finishing rolling is usually modeled in the form of an isolated single-stand model, abandoning the modeling of the continuous rolling method, and not considering the influence of changes in process parameters such as tension between adjacent stands on the width of the rolled piece, while ignoring the heredity of the upstream stand to the downstream stand. In addition, traditional finite element modeling is difficult to accurately simulate various working conditions in the production process, limiting the further application of this method. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present application proposes a method and device for predicting the width between stands in a hot rolling production process.

[0008] In a first aspect, the present application proposes a method for predicting the width between stands in a hot rolling production process, comprising:

[0009] Obtain various parameters of the stand entrance during hot rolling production, including: strip entrance width, entrance thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension;

[0010] The various parameters of the rack inlet are input into the pre-established width expansion prediction model to obtain the width expansion of each rack in the hot rolling production process, and the final outlet width, the outlet width of each upstream rack and the outlet width of each downstream rack are obtained according to the strip inlet width and the width expansion of each rack. The pre-established width expansion prediction model is a width expansion prediction model of multiple upstream racks and downstream racks pre-established under different working conditions, having the same model structure and different coefficients. The model structure is determined by finite element analysis, and the optimal coefficient of each width expansion prediction model is determined by the grey wolf algorithm.

[0011] The method of determining the model structure by finite element analysis comprises:

[0012] A three-dimensional elastic-plastic finite element model of hot rolling production process under different working conditions is established by using historical actual production data, wherein the three-dimensional elastic-plastic finite element model of hot rolling includes: a three-dimensional elastic-plastic finite element model of hot rolling of multiple upstream stands and downstream stands;

[0013] The finite element analysis of the three-dimensional elastic-plastic finite element model of continuous rolling was carried out to obtain the average width of the strip under different working conditions;

[0014] According to the results of finite element analysis, a width expansion prediction model with unknown coefficients under different working conditions is established, with strip entrance width, entrance thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension as input variables and the output being the average strip width under different working conditions.

[0015] The method of using historical actual production data to establish a continuous rolling three-dimensional elastic-plastic finite element model of the hot continuous rolling production process under different working conditions includes:

[0016] Step S2.1.1: Obtain material parameters of the strip steel and the roll, including: finger density, Poisson's ratio and Young's modulus;

[0017] Step S2.1.2: Obtain model process parameters of the strip steel and the roll, including: steel inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance, and unit tension;

[0018] Step S2.1.3: using the material parameters of the strip steel and the rolls and the historical actual production data of the model process parameters, a three-dimensional elastic-plastic finite element model of the hot rolling production process under different working conditions is established, and the three-dimensional elastic-plastic finite element model of the hot rolling production process is meshed;

[0019] Step S2.1.4: If the error between the continuous rolling three-dimensional elastic-plastic finite element model and the established finite element model is less than the preset threshold, the continuous rolling three-dimensional elastic-plastic finite element model has been successfully established; otherwise, change the historical actual production data and go to step S2.1.3 to re-establish the continuous rolling three-dimensional elastic-plastic finite element model of the hot rolling production process under different working conditions.

[0020] The finite element analysis of the continuous rolling three-dimensional elastic-plastic finite element model is performed to obtain the average width of the strip under different working conditions, including:

[0021] Determine the value range of the model process parameters of the strip and roll in the finite element analysis process;

[0022] According to the value range, a finite element analysis is performed on the continuous rolling three-dimensional elastic-plastic finite element model to obtain the width of each edge mesh node under different working conditions;

[0023] According to the width of each edge grid node under the different working conditions, the average width of the strip under the different working conditions is calculated.

[0024] The average width of the strip is calculated as follows:

[0025]

[0026] in, is the average width of the strip, , B min is the minimum width after rolling, B max is the maximum width after rolling, is the width of the i-th strip edge mesh node, B0 is the initial width of the entrance, i is the strip edge mesh node, and n is the total number of edge nodes.

[0027] According to the finite element analysis results, the strip inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension are used as input variables, and the output is the average strip width under different working conditions. A width prediction model with undetermined coefficients under different working conditions is established, including:

[0028] According to the results of finite element analysis, the corresponding relationship and change trend between various parameters of the stand inlet and the final outlet width during the hot rolling production process are obtained;

[0029] According to the corresponding relationship and changing trend, the strip entrance width, entrance thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension are taken as input variables, and the output is the average strip width under different working conditions. A width expansion prediction model with unknown coefficients under different working conditions is established.

[0030] The calculation formula of the widening prediction model with undetermined coefficients under different working conditions is as follows:

[0031]

[0032] in, is the outlet width, b0 is the strip inlet width, h0 is the inlet thickness, is the thickness reduction, R is the roller diameter, is the friction coefficient, σ s is the deformation resistance, τ i is the unit tension, a1 is the first coefficient to be determined, a2 is the second coefficient to be determined, a3 is the third coefficient to be determined, a4 is the fourth coefficient to be determined, and a5 is the fifth coefficient to be determined.

[0033] Determining the optimal coefficient of each widening prediction model by the grey wolf algorithm includes:

[0034] Step S1.4.1: inputting a set of historical data values ​​of various parameters at the stand inlet during the hot rolling production process into a pre-established upstream stand width prediction model under different working conditions to obtain a plurality of equations with undetermined coefficients;

[0035] Step S1.4.2: combining the plurality of equations with undetermined coefficients to obtain an equation system, and solving the equation system to obtain a set of determined coefficients;

[0036] Step S1.4.3: Repeat steps S1.4.1 to S1.4.2 to obtain multiple sets of determined coefficients;

[0037] Step S1.4.4: Use the Grey Wolf Algorithm to find the optimal value among multiple groups of determined coefficients, and obtain the optimal coefficients in the pre-established widening prediction model of multiple upstream racks and downstream racks under different working conditions.

[0038] In a second aspect, the present application proposes a device for predicting the width between stands in a hot rolling production process, comprising:

[0039] The data acquisition module is used to obtain various parameters of the stand entrance during the hot rolling production process, including: strip entrance width, entrance thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension;

[0040] The width prediction module is used to input various parameters of the rack inlet into a pre-established width prediction model to obtain the width of each rack in the hot rolling production process, and obtain the final outlet width, the outlet width of each upstream rack, and the outlet width of each downstream rack according to the strip inlet width and the width of each rack. The pre-established width prediction model is a width prediction model of multiple upstream racks and downstream racks pre-established under different working conditions, with the same model structure and different coefficients. The model structure is determined by finite element analysis, and the optimal coefficient of each width prediction model is determined by the grey wolf algorithm.

[0041] In a third aspect, the present application proposes a computer program product, including a computer program or instructions, which, when executed by a processor, implements the method for predicting the width between stands in a hot rolling production process.

[0042] Beneficial effects:

[0043] The present application proposes a method and device for predicting the width between stands in a hot rolling production process. The present application can not only predict the final outlet width, but also propose the width between stands in the hot rolling production process. The present application takes into account the tension between stands and the deformation resistance of the rolled pieces on each stand, and takes into account the genetic influence of the upstream stand on the downstream stand, thereby improving the accuracy of the prediction, providing guidance for subsequent width control, and can be widely used in industrial production. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 Flow chart of the method for predicting the width between stands in the hot rolling production process according to the embodiment of the present application;

[0045] Figure 2 The rack layout diagram of the hot rolling and finishing process based on the site in the embodiment of the present application;

[0046] Figure 3 A flow chart of hot rolling process width prediction based on finite element simulation and gray wolf optimization in the embodiment of the present application;

[0047] Figure 4 A finite element simulation model diagram based on field data in an embodiment of the present application;

[0048] Figure 5A comparison diagram of the strip cross-section before and after strip rolling established based on finite element simulation in an embodiment of the present application;

[0049] Figure 6 Parameters affecting the width variation based on finite element simulation in the embodiment of the present application;

[0050] Figure 7 Iterative diagram of the average width prediction model after rolling based on the GWO optimization model in the embodiment of the present application;

[0051] Figure 8 Comparison diagram of predicted width spread and actual width spread based on the GWO optimization model in the embodiment of the present application;

[0052] Fig. 9 Principle block diagram of the device for predicting the width between stands in the hot rolling production process according to the embodiment of the present application;

[0053] Among them, 1-strip steel, 2-rolling direction, 3-one-stand roll, 4-two-stand roll. DETAILED DESCRIPTION

[0054] The specific implementation of the present application is further described in detail below in conjunction with the drawings and examples.

[0055] Embodiment 1:

[0056] This embodiment proposes a method for predicting the width between stands in a hot rolling production process. Figure 1 As shown, including:

[0057] This embodiment provides a method for predicting the width between stands in a hot rolling production process, including:

[0058] Step S1: obtaining various parameters of the stand inlet during the hot rolling production process, including: strip inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension;

[0059] Step S2: Input various parameters of the rack inlet into the pre-established width prediction model to obtain the width of each rack in the hot rolling production process, and obtain the final outlet width, the outlet width of each upstream rack, and the outlet width of each downstream rack according to the strip inlet width and the width of each rack. The pre-established width prediction model is a width prediction model of multiple upstream racks and downstream racks pre-established under different working conditions, with the same model structure and different coefficients. The model structure is determined by finite element analysis, and the optimal coefficient of each width prediction model is determined by the grey wolf algorithm.

[0060] In this embodiment, the influence of multiple factors on the exit width, including the strip entrance width, entrance thickness, thickness reduction, roller diameter, friction coefficient, deformation resistance and unit tension, is taken into consideration, and the finite element simulation and the algorithm (Grey Wolf algorithm) are integrated to establish a width prediction model, wherein the width is an increment produced by the reduction during the rolling process. The width prediction model actually predicts the width of each stand in the hot rolling production process. If there are 6 stands in the hot rolling production process, the final exit width = strip entrance width + width of 6 stands.

[0061] The method of determining the model structure by finite element analysis comprises:

[0062] Step S2.1: using historical actual production data to establish a continuous rolling three-dimensional elastic-plastic finite element model of the hot rolling production process under different working conditions, the continuous rolling three-dimensional elastic-plastic finite element model includes: a continuous rolling three-dimensional elastic-plastic finite element model of multiple upstream stands and downstream stands;

[0063] The method of using historical actual production data to establish a continuous rolling three-dimensional elastic-plastic finite element model of the hot continuous rolling production process under different working conditions includes:

[0064] Step S2.1.1: Obtain material parameters of the strip steel and the roll, including: finger density, Poisson's ratio and Young's modulus;

[0065] Step S2.1.2: Obtain model process parameters of the strip steel and the roll, including: steel inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance, and unit tension;

[0066] Step S2.1.3: using the material parameters of the strip steel and the rolls and the historical actual production data of the model process parameters, a three-dimensional elastic-plastic finite element model of the hot rolling production process under different working conditions is established, and the three-dimensional elastic-plastic finite element model of the hot rolling production process is meshed;

[0067] Step S2.1.4: If the error between the continuous rolling three-dimensional elastic-plastic finite element model and the established finite element model is less than the preset threshold, the continuous rolling three-dimensional elastic-plastic finite element model has been successfully established; otherwise, change the historical actual production data and go to step S2.1.3 to re-establish the continuous rolling three-dimensional elastic-plastic finite element model of the hot rolling production process under different working conditions.

[0068] Step S2.2: Perform finite element analysis on the continuous rolling three-dimensional elastic-plastic finite element model to obtain the average width of the strip under different working conditions, including:

[0069] Step S2.2.1: Determine the value range of the model process parameters of the strip steel and the roll in the finite element analysis process;

[0070] Step S2.2.2: performing finite element analysis on the continuous rolling three-dimensional elastic-plastic finite element model according to the value range to obtain the width of each edge mesh node under different working conditions;

[0071] Step S2.2.3: Calculate the average width of the strip under different working conditions based on the width of each edge grid node under the different working conditions.

[0072] The average width of the strip is calculated as follows:

[0073]

[0074] in, is the average width of the strip, , B min B is the minimum width after rolling. max is the maximum width after rolling, is the width of the i-th strip edge mesh node, B0 is the initial width of the entrance, i is the strip edge mesh node, and n is the total number of edge nodes.

[0075] Step S2.3: Based on the finite element analysis results, the strip inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension are used as input variables, and the output is the average strip width under different working conditions. A width prediction model with undetermined coefficients under different working conditions is established, including:

[0076] Step S2.3: Obtain the corresponding relationship and change trend between various parameters of the stand inlet and the final outlet width during the hot rolling production process according to the finite element analysis results;

[0077] Step S2.4: According to the corresponding relationship and changing trend, the strip entrance width, entrance thickness, thickness reduction, roller diameter, friction coefficient, deformation resistance and unit tension are used as input variables, and the output is the average width of the strip under different working conditions. A width expansion prediction model with unknown coefficients under different working conditions is established.

[0078] The calculation formula of the widening prediction model with undetermined coefficients under different working conditions is as follows:

[0079]

[0080] in, is the outlet width, b0 is the strip inlet width, h0 is the inlet thickness, is the thickness reduction, R is the roller diameter, is the friction coefficient, σ s is the deformation resistance, τ iis the unit tension, a1 is the first coefficient to be determined, a2 is the second coefficient to be determined, a3 is the third coefficient to be determined, a4 is the fourth coefficient to be determined, and a5 is the fifth coefficient to be determined.

[0081] Step S2.5: Determine the optimal coefficient of each widening prediction model by using the Grey Wolf Algorithm, including:

[0082] Step S2.5.1: inputting a set of historical data values ​​of various parameters at the stand inlet during the hot rolling production process into a pre-established upstream stand width prediction model under different working conditions to obtain a plurality of equations with undetermined coefficients;

[0083] In this embodiment, a set of parameters of the frame entrance during the hot rolling production process includes 5 strip entrance widths, entrance thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension, and 5 equations with unknown coefficients are obtained.

[0084] Step S2.5.2: combining the plurality of equations with undetermined coefficients to obtain an equation system, and solving the equation system to obtain a set of determined coefficients;

[0085] In this embodiment, five equations with undetermined coefficients are combined into a system of simultaneous equations and solved to obtain a set of determined coefficients, including: a1 to a5;

[0086] Step S2.5.3: Repeat steps S2.5.1 to S2.5.2 to obtain multiple sets of determined coefficients;

[0087] In this embodiment, steps S2.5.1 to S2.5.2 are repeated continuously to obtain 200 groups a1 to a5;

[0088] Step S2.5.4: Use the Grey Wolf Algorithm to find the optimal value among multiple groups of determined coefficients, and obtain the optimal coefficients in the pre-established widening prediction model of multiple upstream racks and downstream racks under different working conditions.

[0089] In this embodiment, after obtaining the optimal coefficient, it means that the width prediction models of multiple upstream racks and downstream racks pre-established under different working conditions have been established. In actual production, various parameters of the rack entrance during the hot rolling production process are obtained, and the various parameters of the rack entrance are input into the pre-established width prediction model to obtain the width of each rack in the hot rolling production process. According to the strip entrance width and the width of each rack, the final outlet width, the outlet width of each upstream rack, and the outlet width of each downstream rack are obtained. This embodiment establishes a multi-rack continuous rolling finite element model, reveals the strip width variation law under different processes, establishes a width prediction model, and introduces an optimization algorithm to search for the optimal model parameters to achieve accurate prediction of the strip width in the finishing process. It is intended to achieve predictive modeling of the strip width in the hot rolling process by combining finite element modeling simulation with optimization algorithms, and to achieve accurate prediction of the strip width in the hot rolling process.

[0090] In order to verify the effectiveness of a method for predicting the width between stands in hot rolling production process, an example is given. Figure 2 Taking the six-stand continuous rolling mill and its production data of a certain on-site finishing rolling process as an example, the strip width expansion is predicted. Taking the model with different parameters as an example for analysis, all the data are used for fitting. The process is as follows Figure 3 As shown, proceed as follows:

[0091] Step 1: Establish finite element model;

[0092] Determine the equipment layout of the hot rolling production line in the actual production process, and obtain the rolling process, equipment parameters, strip composition and size parameters of the hot rolling site finishing process. The production data described in this example includes on-site instrument detection data, secondary calculation data, production reports, etc., which provide a basis for finite element modeling. Based on the knowledge of the rolling field, the four-high mill roll-strip thermal-mechanical coupling model is established using actual on-site data.

[0093] Step 1.1: Model material selection;

[0094] The material properties of the strip and roll are characterized by material parameters, including density ρ (kg / m 3 ), Young's modulus E (GPa), Poisson's ratio ν; the material properties of the strip and roll are calculated by on-site material data and professional software JMATPRO, as shown in Table 1:

[0095] Table 1 Strip and roll material properties

[0096] <![CDATA[Density ρ (kg / m 3 )]]> Young's modulus E(GPa) Poisson's ratio ν Strip Steel 7850 107 0.36 Roller 7850 210 0.3

[0097] Step 1.2: Determine the strip and roll model parameters in the finite element modeling process;

[0098] Obtain the strip dimensions and roll motion parameters during the rolling process. In the finite element simulation process, research is conducted based on actual production data. Process parameters include strip inlet width, inlet thickness, inlet temperature, inlet speed, work roll and support roll diameter, roll speed, reduction rate, friction coefficient, unit tension, etc.

[0099] Step 1.3: Establishment of finite element model;

[0100] First, a three-dimensional elastic-plastic finite element model is established. Based on the field data, the material properties of the strip and the rollers are set using the commercial finite element software ANSYS based on the material parameters in step 1.1. According to the deformation characteristics of the strip during rolling, a continuous rolling model of the finishing process is established based on the actual production data on site in step 1.2, and the full model is used for modeling. At the same time, the strip length is reduced and set to 3000 mm without affecting the results.

[0101] Table 2 Actual data on site

[0102] Strip parameters Entrance width (mm) Inlet thickness (mm) Inlet temperature(℃) Outlet width(mm) Export thickness(mm) value 995 42 1050 1009 2.85 Process data Roller diameter (mm) Roller speed (m / s) Unit tension (MPa) Thickness reduction rate (%) Friction coefficient F1 820 1.33 0 50 0.257 F2 800 2.61 6.5 50 0.259 F3 800 4.34 8.5 40 0.26 F4 740 6.36 12 30 0.26 F5 730 8.625 14.5 25 0.261 F6 690 10.468 16.5 14 0.258

[0103] The finite element model is meshed using the ANSYS meshing tool. The strip and rollers are both meshed using SOLID164 units and hexahedral meshes. The mesh sizes of the model strip and roller are 5 mm and 8 mm, respectively. The finite element mesh model is as follows: Figure 4 As shown, it includes: strip steel 1, first-stand roll 3 and second-stand roll 4. Strip steel 1 will be rolled according to rolling direction 2; the roll and strip steel are set to be in surface-to-surface contact, the dynamic friction factor is 0.26, and constraints and boundary conditions are added in the width and thickness directions. In this example, a finite element model corresponding to the set working conditions is established for each stand (i.e., F1-F6 stands). It can also be understood that the finite element model of the entire hot rolling production process includes a sub-finite element model of each stand, F1 is the upstream stand of F2, F2 is called the downstream stand of F1, and the entire hot rolling production process has multiple upstream stands and multiple downstream stands.

[0104] Set the initial forward speed of the strip and set the rotation linear speed of the F1-F6 working rolls respectively.

[0105] Step 1.4: Verify the accuracy of the finite element model;

[0106] The rolling force of the established finite element model of the rolled product and the roll was verified. The comparison error between the verified model and the field measured rolling force is shown in Table 3. The model accuracy is within an acceptable range.

[0107] Table 3 Rolling force comparison verification

[0108] Working conditions Simulated rolling force / MN Measured rolling force / MN error 1 15.72 15.05 4.47% 2 12.6 13.41 6.04% 3 21.53 23.01 6.41% 4 17.4 16.47 5.66%

[0109] Step 2: Finite element analysis;

[0110] Step 2.1: Determine the process parameters that affect the width of the rolled piece;

[0111] In order to fully analyze the influence of hot rolling width, the influencing factors in the existing width expansion formulas as shown in Table 4 are first summarized. Most of the existing formulas focus on the influence of the rolling reduction, reduction rate, roll diameter, friction coefficient, entry width, and contact arc length on the width expansion.

[0112] Therefore, based on the above influencing factors, multiple groups of finite element models are established, and the control variable method is adopted to set the process parameters for finite element simulation experiments. The parameters are shown in Tables 5 and 6, including: working roll diameter, workpiece entrance width, workpiece entrance thickness, workpiece entrance speed, deformation resistance, reduction rate, friction coefficient, unit tension, roll speed, etc.

[0113] This example uses the control variable method to select process parameters, uses a variety of different process parameters to establish multiple groups of finite element models, and uses the finite element software ANSYA / LS-DYNA for calculations.

[0114] The process parameters that affect the width of the rolled product are finally determined to be: the inlet width of the rolled product, the inlet thickness, the thickness reduction, the friction coefficient, the roll diameter, the unit tension, and the deformation resistance. The above finite element model data are extracted to finally obtain the width result of the strip after rolling.

[0115] Table 4 Analysis of factors considered in existing widening formulas

[0116]

[0117] Table 5 Parameter ranges of strip steel model in finite element modeling process

[0118] Strip parameters Entrance width (mm) Inlet thickness (mm) Inlet temperature(℃) Inlet velocity (m / s) value 800-1200 30-48 900-1100 0.6-1

[0119] Table 6 Roller model parameter ranges during finite element modeling

[0120] Roll parameters Roller diameter (mm) Roller speed (m / s) Thickness reduction rate (%) Friction coefficient Unit tension (MPa) Deformation resistance (MPa) F1 820-1000 1.33-1.5 40-50 0.2-0.4 0 105.296 F2 800-1000 2.65-2.8 40-50 0.2-0.4 6.5-8 112.5 F3 800-1000 4.34-4.5 30-40 0.2-0.4 8.5-10 147.654 F4 740-1000 6.36-6.5 30-40 0.2-0.4 12.5-14 181.388 F5 730-1000 8.63-8.8 25-30 0.2-0.4 14.5-16 185.193 F6 690-1000 10.47-10.6 15-20 0.2-0.4 16.5-18 197.726

[0121] Step 2.2: Obtain the width of the rolled product after different rolling processes

[0122] like Figure 5 The figure shows the cross-sectional shape comparison of the strip before and after hot rolling. The strip width data is extracted based on the finite element simulation results, and the average width of the strip is calculated based on the cross-sectional shape of the strip. :

[0123] (1)

[0124] in, is the average width of the strip, , B min B is the minimum width after rolling. max is the maximum width after rolling, is the width of the i-th strip edge mesh node, B0 is the initial width of the entrance, i is the strip edge mesh node, and n is the total number of edge nodes.

[0125] Step 3: Determine the widened model structure based on the finite element results;

[0126] Step 3.1: Obtain the corresponding relationship and change trend between each process parameter and the width after rolling by analyzing the finite element results;

[0127] Through the above finite element analysis, the specific data of the average width after rolling was obtained, and the variation trend of the width after rolling under different process parameters was further analyzed. A simulation data set of strip width was constructed, and a qualitative analysis of the width expansion law was carried out.

[0128] Based on the average width results calculated above, the simulation data analysis results are as follows: Figure 6 shown.

[0129] Step 3.2: Determine the model structure and unknown coefficients based on the finite element results;

[0130] Based on the results of finite element analysis, the strip inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, unit tension and deformation resistance are used as input variables, and the output is the average width after rolling B. e ; Establish post-rolling width B e The prediction model is expressed as follows:

[0131] (2)

[0132] in, is the outlet width, b0 is the strip inlet width, h0 is the inlet thickness, is the thickness reduction, R is the roller diameter, is the friction coefficient, σ s is the deformation resistance, τ i is the unit tension, a1 is the first coefficient to be determined, a2 is the second coefficient to be determined, a3 is the third coefficient to be determined, a4 is the fourth coefficient to be determined, and a5 is the fifth coefficient to be determined.

[0133] Step 4: Determination of the unknown coefficients;

[0134] Step 4.1: Repeat the iterative width prediction model to obtain the total width of the rolling outlet;

[0135] Use the model results in step 3 as the inlet width calculation for the downstream rack, calculate the outlet width of the strip for the next rack, repeat the iteration until the final outlet width, and compare it with the on-site inlet and outlet data.

[0136] Step 4.2: Determine the unknown coefficients by using the Grey Wolf Algorithm;

[0137] Based on historical data sets, the Grey Wolf Algorithm is introduced to optimize the existing strip width prediction model, search for the optimal unknown coefficients and improve the model's adaptability to field data, so as to achieve accurate prediction of strip width.

[0138] Substitute the data shown in Table 5 and Table 6 into the widening model to calculate the F1 exit widening B e1 for:

[0139]

[0140] And B e1 As the entrance width, the F2 exit width is brought into the model to predict:

[0141]

[0142] Repeat the iteration until the final outlet width of F6 is obtained, and the measured final outlet width B is obtained. e6 And the expression of the coefficient to be determined:

[0143]

[0144] Similarly, multiple sets of process data are introduced into the widening model to determine the unknown coefficients of the widening prediction model.

[0145] Step 4.3: Determine the values ​​of each undetermined coefficient by using the Grey Wolf Algorithm;

[0146] The Grey Wolf Algorithm is introduced to search for the optimal unknown coefficients, improve the adaptability to the data, and achieve accurate prediction of the strip width.

[0147] Grey Wolf Optimizer (GWO) is a swarm intelligence algorithm developed under the inspiration of the grey wolf's hunting activities. GWO algorithm has the characteristics of simple structure, few parameters to be adjusted, and easy implementation. It has a convergence factor that can be adjusted adaptively and an information feedback mechanism, which can achieve a balance between local optimization and global search, and has good performance in terms of problem solving accuracy and convergence speed.

[0148] Therefore, based on the simulation data set, the Grey Wolf Optimization Algorithm is used to optimize the undetermined coefficients of Formula 1, search for undetermined coefficients with high adaptability to the simulation data, and achieve accurate prediction of the strip width. The specific steps are as follows:

[0149] The number of gray wolf groups is set to 7, and the upper and lower bounds of each coefficient of the prediction model are set to: -10~10; the minimum mean square error value is set as the fitness function of the optimization algorithm, the population number is set to 50, and the maximum number of iterations is 100. The optimization iteration process is as follows Figure 7 As shown in the figure, when the number of iterations reaches 89, the termination condition is met and the optimal solution is output, that is, the optimal model coefficients to be determined are obtained;

[0150] Based on the optimized model coefficients, the calculation model of the post-rolling width is obtained, and the obtained function coefficients are shown in Table 7:

[0151] Table 7 Undetermined coefficients and correlation coefficients of strip width prediction model

[0152]

[0153] Therefore, the final widening prediction model obtained by the above algorithm is:

[0154]

[0155] Step 5: Calculate the width between each rack in turn based on the unknown coefficients and the final outlet width results.

[0156] Substituting the working condition data of each stand into the width prediction model, the width data after rolling of each stand can be obtained as shown in Table 8:

[0157] Table 8 Results of outlet width of each rack

[0158] Number of racks Exit Width F1 1.00103 F2 1.0054 F3 1.00731 F4 1.00801 F5 1.00844 F6 1.00864

[0159] Step 5.1: Verification of the results of the extended prediction model

[0160] Other process parameters were introduced into the width prediction model to obtain multiple sets of rolling width results and compared with the actual outlet width results on site. The verification results are as follows Figure 8 shown.

[0161] The width prediction method of the finishing rolling process proposed in this example fully considers the influence of roll speed. Through finite element simulation and function fitting, the width prediction model R is established. 2 It is 0.974, which can better perceive the data information and accurately predict the strip width after continuous rolling, solving the problem that the strip width is difficult to measure during the finishing rolling process.

[0162] This example uses on-site process regulations and rolling parameters for finite element modeling. The proposed method for predicting the width of the finishing rolling process makes up for the lack of considerations in previous models, fully considers the influence of changes in tension and deformation resistance during hot rolling, and restores the actual situation on site to the maximum extent. Through finite element simulation and function fitting, the strip width is accurately predicted during the continuous rolling process of the strip, solving the problem of difficulty in measuring the strip width during the finishing rolling process. This example can improve control accuracy, provide guidance for subsequent width control, and improve productivity.

[0163] This embodiment proposes a prediction of the width between stands in the hot rolling production process, which involves the intelligent core technology field of steel production. First, by establishing and verifying a finite element simulation model, the influence of the process parameters of the hot rolling process on the width of the strip is obtained to improve the accuracy of the subsequent model; a width prediction model containing various process parameters is established and the undetermined coefficients of the variables are optimized through the Grey Wolf algorithm to achieve the purpose of predicting the width of the strip after rolling. By combining finite element simulation with the Grey Wolf optimization algorithm, accurate prediction of the width of the strip can be achieved. The method of this embodiment has high accuracy, provides guidance for subsequent width control, and can be widely used in industrial production.

[0164] The above embodiments are only used to illustrate one implementation method of the present application, and their purpose is to enable technicians in this field to understand the content of the present application and implement it accordingly. The protection scope of the present application is not limited to the above embodiments. All equivalent changes made according to the technical solutions disclosed in the present application are within the protection scope of the present application.

[0165] Embodiment 2:

[0166] This embodiment proposes a device for predicting the width between stands in a hot rolling production process. Fig. 9 As shown, it includes: a data acquisition module and a width prediction module, wherein the data acquisition module is connected to the width prediction module;

[0167] The data acquisition module is used to obtain various parameters of the stand entrance during the hot rolling production process, including: strip entrance width, entrance thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension;

[0168] The width prediction module is used to input various parameters of the rack inlet into a pre-established width prediction model to obtain the width of each rack in the hot rolling production process, and obtain the final outlet width, the outlet width of each upstream rack, and the outlet width of each downstream rack according to the strip inlet width and the width of each rack. The pre-established width prediction model is a width prediction model of multiple upstream racks and downstream racks pre-established under different working conditions, with the same model structure and different coefficients. The model structure is determined by finite element analysis, and the optimal coefficient of each width prediction model is determined by the grey wolf algorithm.

[0169] Embodiment 3:

[0170] This embodiment provides a computer program product, including a computer program or instructions, which, when executed by a processor, implements the method for predicting the width between stands in a hot rolling production process.

[0171] Based on such understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a computer program product.

[0172] The various embodiments in the present application are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0173] The protection scope of the present application is not limited to the above-mentioned embodiments. Obviously, those skilled in the art can make various changes and modifications to the present disclosure without departing from the scope and spirit of the present disclosure. If these changes and modifications fall within the scope of the claims of the present disclosure and their equivalents, the intention of the present disclosure also includes these changes and modifications.

Claims

1. A method for predicting the width between stands in a hot rolling production process, characterized in that: include: Obtain various parameters of the stand entrance during hot rolling production, including: strip entrance width, entrance thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension; Input various parameters of the rack inlet into a pre-established width prediction model to obtain the width of each rack in the hot rolling production process, and obtain the final outlet width, the outlet width of each upstream rack, and the outlet width of each downstream rack according to the strip inlet width and the width of each rack. The pre-established width prediction model is a width prediction model of multiple upstream racks and downstream racks pre-established under different working conditions, having the same model structure and different coefficients. The model structure is determined by finite element analysis, and the optimal coefficient of each width prediction model is determined by the gray wolf algorithm. The method of determining the model structure by finite element analysis comprises: A three-dimensional elastic-plastic finite element model of hot rolling production process under different working conditions is established by using historical actual production data, wherein the three-dimensional elastic-plastic finite element model of hot rolling includes: a three-dimensional elastic-plastic finite element model of hot rolling of multiple upstream stands and downstream stands; The finite element analysis of the three-dimensional elastic-plastic finite element model of continuous rolling was carried out to obtain the average width of the strip under different working conditions; According to the results of finite element analysis, the strip inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension are taken as input variables, and the output is the average strip width under different working conditions. A width prediction model with undetermined coefficients under different working conditions is established. According to the finite element analysis results, the strip inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension are used as input variables, and the output is the average strip width under different working conditions. A width prediction model with undetermined coefficients under different working conditions is established, including: According to the results of finite element analysis, the corresponding relationship and change trend between various parameters of the stand inlet and the final outlet width during the hot rolling production process are obtained; According to the corresponding relationship and change trend, the strip inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension are used as input variables, and the output is the average strip width under different working conditions, and a width prediction model with undetermined coefficients under different working conditions is established; The calculation formula of the widening prediction model with undetermined coefficients under different working conditions is as follows: Among them, B e is the exit width, b0 is the strip entrance width, h0 is the entrance thickness, Δh is the thickness reduction, R is the roll diameter, μ is the friction coefficient, σ s is the deformation resistance, τ i is the unit tension, a1 is the first coefficient to be determined, a2 is the second coefficient to be determined, a3 is the third coefficient to be determined, a4 is the fourth coefficient to be determined, and a5 is the fifth coefficient to be determined.

2. The method for predicting the width between stands in a hot rolling production process according to claim 1, characterized in that: The method of using historical actual production data to establish a continuous rolling three-dimensional elastic-plastic finite element model of the hot continuous rolling production process under different working conditions includes: Step S2.1.1: Obtain material parameters of the strip steel and the roll, including: finger density, Poisson's ratio and Young's modulus; Step S2.1.2: Obtain model process parameters of the strip steel and the roll, including: steel inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance, and unit tension; Step S2.1.3: using the material parameters of the strip steel and the rolls and the historical actual production data of the model process parameters, a three-dimensional elastic-plastic finite element model of the hot rolling production process under different working conditions is established, and the three-dimensional elastic-plastic finite element model of the hot rolling production process is meshed; Step S2.1.4: If the error between the continuous rolling three-dimensional elastic-plastic finite element model and the established finite element model is less than the preset threshold, the continuous rolling three-dimensional elastic-plastic finite element model has been successfully established; otherwise, change the historical actual production data and go to step S2.1.3 to re-establish the continuous rolling three-dimensional elastic-plastic finite element model of the hot rolling production process under different working conditions.

3. The method for predicting the width between stands in a hot rolling production process according to claim 1, characterized in that: The finite element analysis of the continuous rolling three-dimensional elastic-plastic finite element model is performed to obtain the average width of the strip under different working conditions, including: Determine the value range of the model process parameters of the strip and roll in the finite element analysis process; According to the value range, a finite element analysis is performed on the continuous rolling three-dimensional elastic-plastic finite element model to obtain the width of each edge mesh node under different working conditions; According to the width of each edge grid node under the different working conditions, the average width of the strip under the different working conditions is calculated.

4. The method for predicting the width between stands in a hot rolling production process according to claim 3, characterized in that: The average width of the strip is calculated as follows: in, is the average width of the strip, B min ≤B i ≤B max , B min B is the minimum width after rolling. max is the maximum width after rolling, B i is the width of the i-th strip edge mesh node, B0 is the initial width of the entrance, i is the strip edge mesh node, and n is the total number of edge nodes.

5. The method for predicting the width between stands in a hot rolling production process according to claim 1, characterized in that: Determining the optimal coefficient of each widening prediction model by the grey wolf algorithm includes: Step S1.4.1: inputting a set of historical data values ​​of various parameters at the stand inlet during the hot rolling production process into a pre-established upstream stand width prediction model under different working conditions to obtain a plurality of equations with undetermined coefficients; Step S1.4.2: combining the plurality of equations with undetermined coefficients to obtain an equation system, and solving the equation system to obtain a set of determined coefficients; Step S1.4.3: Repeat steps S1.4.1 to S1.4.2 to obtain multiple sets of determined coefficients; Step S1.4.4: Use the Grey Wolf Algorithm to find the optimal value among multiple groups of determined coefficients, and obtain the optimal coefficients in the pre-established widening prediction model of multiple upstream racks and downstream racks under different working conditions.

6. A device for predicting the width between stands in a hot rolling production process, characterized in that: include: The data acquisition module is used to obtain various parameters of the stand entrance during the hot rolling production process, including: strip entrance width, entrance thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension; The width prediction module is used to input various parameters of the rack inlet into a pre-established width prediction model to obtain the width of each rack in the hot rolling production process, and obtain the final outlet width, the outlet width of each upstream rack, and the outlet width of each downstream rack according to the strip inlet width and the width of each rack. The pre-established width prediction model is a width prediction model of multiple upstream racks and downstream racks pre-established under different working conditions, having the same model structure and different coefficients. The model structure is determined by finite element analysis, and the optimal coefficient of each width prediction model is determined by the gray wolf algorithm; the determination of the model structure by finite element analysis includes: A three-dimensional elastic-plastic finite element model of hot rolling production process under different working conditions is established by using historical actual production data, wherein the three-dimensional elastic-plastic finite element model of hot rolling includes: a three-dimensional elastic-plastic finite element model of hot rolling of multiple upstream stands and downstream stands; The finite element analysis of the three-dimensional elastic-plastic finite element model of continuous rolling was carried out to obtain the average width of the strip under different working conditions; According to the results of finite element analysis, the strip inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension are taken as input variables, and the output is the average strip width under different working conditions. A width prediction model with undetermined coefficients under different working conditions is established. According to the finite element analysis results, the strip inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension are used as input variables, and the output is the average strip width under different working conditions. A width prediction model with undetermined coefficients under different working conditions is established, including: According to the results of finite element analysis, the corresponding relationship and change trend between various parameters of the stand inlet and the final outlet width during the hot rolling production process are obtained; According to the corresponding relationship and change trend, the strip inlet width, inlet thickness, thickness reduction, roll diameter, friction coefficient, deformation resistance and unit tension are used as input variables, and the output is the average strip width under different working conditions, and a width prediction model with undetermined coefficients under different working conditions is established; The calculation formula of the widening prediction model with undetermined coefficients under different working conditions is as follows: Among them, B e is the exit width, b0 is the strip entrance width, h0 is the entrance thickness, Δh is the thickness reduction, R is the roll diameter, μ is the friction coefficient, σ s is the deformation resistance, τ i is the unit tension, a1 is the first coefficient to be determined, a2 is the second coefficient to be determined, a3 is the third coefficient to be determined, a4 is the fourth coefficient to be determined, and a5 is the fifth coefficient to be determined.

7. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instruction is executed by a processor, the prediction of the width between stands in the hot rolling production process as claimed in any one of claims 1 to 5 is realized.

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