Finite element-based coated steel bar rolling process optimization method

Abstract

The invention discloses a method for optimizing a coated steel bar rolling process based on a finite element, which comprises the following steps of: S1, obtaining a base-coating material stress-strain curve at different temperatures by utilizing a thermal simulation experiment, obtaining material parameters of a base material and a coating material by utilizing Jmatpro software, and establishing a finite element model; s2, cladding steel bar rolling is divided into rough rolling, intermediate rolling and finish rolling, after rough rolling in the established finite element model is finished, the head and the tail of a blank are cut off, grids are re-divided, then intermediate rolling is conducted in the established finite element model, after intermediate rolling is finished, the head and the tail of the blank are cut off again, the grids are re-divided, and finish rolling is conducted till rolling is finished; and S3, based on the finite element model, hole patterns and rolling parameters are optimized. Compared with a traditional finite element model, the method has the advantages that the accuracy of a simulation result is improved, the process optimization period is shortened, and the optimization cost is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of rolling process optimization, and in particular relates to a method for optimizing the rolling process of coated steel bars based on finite elements. Background Art

[0002] In recent years, with the continuous advancement of my country's industrialization process, the construction of tunnels, bridges and marine engineering has increased day by day. As the most widely used construction steel, steel bars are in huge demand. Traditional carbon steel bars are prone to rust and expansion due to their low chromium and nickel content. When the volume increases beyond the elastic limit of concrete, it will cause damage to the concrete structure, resulting in a serious reduction in the life of the building. Coated steel bars are a new type of composite steel bar with carbon steel as the base and stainless steel wrapped on the outside. The corrosion resistance of this steel bar is close to that of stainless steel bars, but the cost is only one-third of that of stainless steel bars. It can be widely used in marine engineering, petroleum, transportation and infrastructure.

[0003] Steel bar rolling is a process of interaction between microstructure evolution and macro deformation, especially for composite rolling of two metals, the deformation trend is more complicated. At present, steel mills do not have mature processes for rolling coated steel bars, and relying on industrial production to explore the optimal rolling parameters is not only inefficient but also costly. Finite element technology uses mathematical approximation methods to simulate real physical systems. It not only has the advantages of short time consumption, but also can effectively obtain the stress and strain conditions of products during processing.

[0004] At present, there is no mature technology for finite element simulation and rolling process optimization of coated steel bars. Patent No.: CN202010429397.1 discloses a method for optimizing the rolling process of metal composite plates. This method does not optimize the rolling process, but mainly uses rapid cold quenching to fix the interface structure of the metal composite plate, thereby obtaining the interface characteristics of the intermediate link of composite plate rolling. Patent No.: CN201811130653.6 discloses a method for optimizing the DSIT rolling process of low-carbon steel. This method mainly optimizes the rolling temperature through thermal simulation, but due to its limitations, the thermal simulation equipment is mainly used to simulate plates, and the coated steel bars are bars, which are not suitable. Patent No.: CN202110732028.4 discloses a method for optimizing the rolling process of ball flat steel based on finite element simulation. This method mainly re-divides the finite element mesh, and does not introduce the process optimization process in detail.

[0005] In summary, the existing simulation and process optimization technologies cannot be applied to coated steel bars, and the use of traditional rolling technology to roll coated steel bars is prone to problems such as inconsistent deformation of the inner and outer layers, resulting in uneven distribution of the coating thickness. Therefore, it is urgent to design a simulation and process optimization method suitable for coated steel bars. Summary of the invention

[0006] The purpose of the present invention is to provide a method for optimizing the rolling process of coated steel bars based on finite elements, so as to solve the problem that the existing simulation and process optimization technologies proposed in the background technology cannot be applied to coated steel bars, and the coated steel bars rolled using traditional processes are prone to inconsistent deformation of the inner and outer layers, resulting in uneven distribution of the coating thickness.

[0007] To achieve the above object, the present invention provides a method for optimizing the rolling process of coated steel bars based on finite element method, comprising the following steps:

[0008] S1. Using thermal simulation experiments, the stress-strain curves of the base and cladding materials at different temperatures are obtained. The material parameters of the base and cladding are obtained using Jmatpro software, and a finite element model is established.

[0009] S2, the rolling of the coated steel bar is divided into rough rolling, medium rolling and finishing rolling. After the rough rolling is completed in the established finite element model, the head and tail of the billet are cut off, and the grid is re-divided. Then, the medium rolling is performed in the established finite element model. After the medium rolling is completed, the head and tail of the billet are cut off again, and the grid is re-divided, and the finishing rolling is performed again until the rolling is completed;

[0010] S3. Based on the finite element model, the hole type and rolling parameters are optimized.

[0011] In a specific implementation, the S1 specific step includes:

[0012] S11, processing the covered steel bar into a thermal simulation specimen of a specified shape;

[0013] S12, clamping the thermal simulation sample, and then heating it to 900° C., 950° C., 1000° C., 1050° C., and 1100° C. respectively;

[0014] S13, after heating, keep warm, then heat for 1s -1 After compression, the stress-strain curve is derived.

[0015] In a specific implementation, the S1 specific step further includes:

[0016] S14, measuring the content of each element in the coated steel bar using a spectrometer;

[0017] S15, then input the content of each element into Jmatpro software;

[0018] S16. Use Jmatpro software to export the material parameters of the base layer and the covering layer.

[0019] In a specific embodiment, the material parameters include thermal conductivity, specific heat, and Poisson's ratio.

[0020] In a specific implementation, the S2 specific step includes:

[0021] S21. Use ABAQUS software to establish a simulation model for rough rolling of coated steel bars and submit the calculation;

[0022] S22. After the rough rolling simulation model is calculated, the rough rolling simulation results are imported into the HYPERMESH software;

[0023] S23, using a cutting plane to cut off the head and tail of the rough rolling simulation result, and re-dividing the grid;

[0024] S24, importing the rough rolling simulation results after mesh re-division into ABAQUS, and establishing a medium rolling simulation model based on the results;

[0025] S25, after the calculation of the intermediate rolling simulation model is completed, the intermediate rolling simulation results are imported into the HYPERMESH software;

[0026] S26, using a cutting plane to cut off the head and tail of the rolling simulation result, and re-dividing the grid;

[0027] S27, importing the intermediate rolling simulation results after mesh re-division into ABAQUS, and establishing a finishing rolling simulation model based on the results;

[0028] S28. After the calculation of the finishing rolling simulation model is completed, the finished coated steel bars are rolled out.

[0029] In a specific implementation, the specific step of S3 includes:

[0030] S31. Determine the rolling process parameters of the coated steel bars according to the industrial site conditions;

[0031] S32. Preliminary screening of rolling process parameters based on limited industrial experiments on coated steel bars;

[0032] S33, performing secondary screening on the rolling process parameters after the preliminary screening by using orthogonal variance analysis to determine the rolling process parameters to be optimized;

[0033] S34, taking the rolling process parameters to be optimized as variables, constructing a BP neural network algorithm to achieve prediction of the minimum coating thickness;

[0034] S35, taking the BP neural network prediction value as the adaptability value, and optimizing the rolling parameters using the genetic algorithm, wherein the optimization goal is to maximize the adaptability value, that is, the larger the minimum value of the coating thickness, the better;

[0035] S36. Output the optimized result.

[0036] In a specific embodiment, in the step S34:

[0037] In the constructed BP neural network algorithm, the number of hidden layers and nodes is automatically optimized through the embedded particle swarm algorithm.

[0038] In a specific implementation, the specific steps of the embedded particle swarm algorithm are as follows:

[0039] S341, taking the number of hidden layers and the number of nodes as independent variables for particle swarm optimization;

[0040] S342, determining the range of the number of hidden layers and the number of nodes;

[0041] S343, determining the number of initialized particle swarms and the number of iterations;

[0042] S344, with the goal of maximizing the minimum coating thickness, automatically search for the best number of hidden layers and nodes.

[0043] In a specific implementation, in step S344, the fitness value of each particle is the minimum coating thickness predicted by the BP neural network corresponding to the particle.

[0044] In a specific implementation, in S2, in the rough rolling stage, each node of the composite surface is defined as a friction constraint, and in the intermediate rolling stage, each node of the composite surface is defined as a binding constraint.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] Compared with the traditional finite element model, the present invention improves the accuracy of simulation results, shortens the process optimization cycle, and reduces the optimization cost.

[0047] The present invention effectively solves the problem of calculation termination caused by mesh distortion by cutting off the head and tail and re-dividing the mesh.

[0048] The present invention effectively connects the entire rolling process, that is, the rough rolling result is used as the intermediate rolling input, and the intermediate rolling result is used as the finishing rolling input. Compared with the traditional finite element model, the simulation error of this method is reduced from 22.22% (traditional model) to 5.71%.

[0049] The invention effectively realizes the prediction of the minimum coating thickness of the coated steel bar and obtains the optimal rolling parameters of the coated steel bar, reduces the cost of experimental optimization, shortens the optimization cycle, and industrial experiments verify that the coating thickness prediction value differs from the actual value by only 2%.

[0050] In addition to the above-described objects, features and advantages, the present invention has other objects, features and advantages. The present invention is further described in detail below. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0052] Figure 1 It is a step-by-step simulation calculation flow chart of an embodiment of the present invention;

[0053] Figure 2 is a schematic cross-sectional view of a covered steel bar according to an embodiment of the present invention;

[0054] Figure 3 It is a schematic diagram of the mesh division of the crescent groove of a simulation model according to an embodiment of the present invention;

[0055] Figure 4 is a schematic diagram of simulation results of an embodiment of the present invention;

[0056] Figure 5 is a schematic diagram of actual rolling results of an embodiment of the present invention;

[0057] Figure 6 It is a traditional simulation model rolling schematic diagram;

[0058] Figure 7 It is a schematic diagram of an optimization process of an embodiment of the present invention;

[0059] Among them, 1. Base layer; 2. Covering layer. DETAILED DESCRIPTION

[0060] The embodiments of the present invention are described in detail below. The specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0061] A method for optimizing a rolling process of a coated steel bar based on finite element analysis of the present invention comprises the following steps:

[0062] S1. Using thermal simulation experiments, the stress-strain curves of the base and coating materials at different temperatures were obtained. The material parameters of the base layer 1 and the coating 2 were obtained using Jmatpro software, and a finite element model was established.

[0063] The specific steps of S1 include:

[0064] S11. Process the covered steel bars into thermal simulation specimens of specified shapes.

[0065] S12, clamp the thermal simulation samples, and then heat them to 900°C, 950°C, 1000°C, 1050°C, and 1100°C respectively.

[0066] S13, after heating, keep warm for 5 minutes, then heat for 1 second -1The compression is carried out at a strain rate of 80%, and the stress-strain curve is derived after the compression is completed.

[0067] S14. Use a spectrometer to measure the content of each element in the covered steel bars.

[0068] S15. Then input the content of each element into Jmatpro software.

[0069] S16. Use Jmatpro software to export the material parameters of the base layer and the covering layer.

[0070] The material parameters include thermal conductivity, specific heat, and Poisson's ratio.

[0071] S2. The rolling of the coated steel bar is divided into rough, medium and finish rolling. After the rough rolling is completed in the established finite element model, the head and tail of the billet are cut off and the mesh is redivided. Then, the medium rolling is carried out in the established finite element model. After the medium rolling is completed, the head and tail of the billet are cut off again, the mesh is redivided, and the finish rolling is carried out again until the rolling is completed.

[0072] The specific steps of S2 include:

[0073] S21. Use ABAQUS software to establish a simulation model for rough rolling of coated steel bars and submit the calculation;

[0074] S22. After the rough rolling simulation model is calculated, the rough rolling simulation results are imported into the HYPERMESH software;

[0075] S23, using a cutting plane to cut off the head and tail of the rough rolling simulation result, and re-dividing the grid;

[0076] S24, importing the rough rolling simulation results after mesh re-division into ABAQUS, and establishing a medium rolling simulation model based on the results;

[0077] S25, after the calculation of the intermediate rolling simulation model is completed, the intermediate rolling simulation results are imported into the HYPERMESH software;

[0078] S26, using a cutting plane to cut off the head and tail of the rolling simulation result, and re-dividing the grid;

[0079] S27, importing the intermediate rolling simulation results after mesh re-division into ABAQUS, and establishing a finishing rolling simulation model based on the results;

[0080] S28. After the calculation of the finishing rolling simulation model is completed, the finished coated steel bars are rolled out.

[0081] In S2, in the rough rolling stage, each node of the composite surface is defined as a friction constraint, and in the intermediate rolling stage, each node of the composite surface is defined as a binding constraint.

[0082] S3. Based on the finite element model, the hole type and rolling parameters are optimized.

[0083] The specific steps of S3 include:

[0084] S31. Determine the rolling process parameters of the coated steel bars according to the industrial site conditions;

[0085] S32. Preliminary screening of rolling process parameters based on limited industrial experiments on coated steel bars;

[0086] S33, performing secondary screening on the rolling process parameters after the preliminary screening by using orthogonal variance analysis to determine the rolling process parameters to be optimized;

[0087] S34, using the rolling process parameters to be optimized as variables, and using the roulette method to randomly generate 300 to 800 combinations;

[0088] Substituting 300 to 800 combinations into the finite element model, the minimum values ​​of the cladding corresponding to 300 to 800 combinations are obtained;

[0089] According to 300 to 800 combinations and the corresponding minimum coating values, a BP neural network algorithm is constructed to predict the minimum coating thickness.

[0090] In the step S34:

[0091] In the constructed BP neural network algorithm, the number of hidden layers and nodes is automatically optimized through the embedded particle swarm algorithm.

[0092] The specific steps of the embedded particle swarm algorithm are as follows:

[0093] S341, taking the number of hidden layers and the number of nodes as independent variables for particle swarm optimization;

[0094] S342, determining the range of the number of hidden layers and the number of nodes;

[0095] S343, determining the number of initialized particle swarms and the number of iterations;

[0096] S344, with the goal of maximizing the minimum coating thickness, automatically search for the best number of hidden layers and nodes.

[0097] In step S344, the fitness value of each particle is the minimum coating thickness predicted by the BP neural network corresponding to the particle.

[0098] S35, taking the BP neural network prediction value as the adaptability value, and optimizing the rolling parameters using the genetic algorithm, wherein the optimization goal is to maximize the adaptability value, that is, the larger the minimum value of the coating thickness, the better;

[0099] S36. Output the optimized result.

[0100] Example 1

[0101] First, the covered steel bar was processed into a φ8×10mm cylinder using a machining center, and then clamped at both ends of the thermal simulator, and then heated, and then -1 The material is compressed by 80% at a rate of 1.5 % and the stress-strain curve is obtained.

[0102] The element contents of C, Si, Mn, P, S, Cr, Ni, Ti and V in the base layer and the coating were measured by a spectrometer, and then the above element contents were input into the Jmatpro software to derive the material parameters of the base layer and the coating, including thermal conductivity, specific heat and Poisson's ratio.

[0103] According to the above material parameters, the finite element model of the covered steel bar is established. The entire rolling process is divided into rough rolling, intermediate rolling and finishing rolling, of which rough rolling and intermediate rolling are six passes each, and finishing rolling is two passes. Model establishment includes: material property definition, analysis step definition, boundary condition definition and mesh division.

[0104] (1) Definition of material properties.

[0105] The cladding material corresponding to each simulation model is 316L, and the base material is 20MnSiV. The thermal stress-strain curves of the above two materials have been obtained through thermal simulation measurement, and the material properties are simulated by software Jmatpro. It should be noted that the rollers are all rigid bodies and do not need to define material properties.

[0106] (2) Analysis step definition.

[0107] The analysis step types corresponding to each simulation model are dynamic, temperature-displacement, and display analysis steps. The analysis step duration is 10 seconds. In order to speed up the calculation speed and improve the simulation efficiency, the mass of the rolled product is scaled, and the scaling factor is set to 10000.

[0108] (3) Definition of boundary conditions.

[0109] During the rolling process, the steel bar is mainly subjected to the combined effects of the force field and the temperature field. Therefore, the boundary condition definition of 20MnSiV / 316L steel bar includes load, temperature and contact definition. First, the load definition. The initial speed of the rolled piece corresponding to each simulation model is 600mm / s. Then the temperature definition. According to the actual measurement of the temperature measuring gun, the rolled piece temperatures of rough rolling, intermediate rolling and finishing rolling are 1050℃, 1040℃ and 1020℃ respectively, the surface temperature of each roller is 120℃, and the ambient temperature is 30℃. In addition, the rolled piece will naturally dissipate heat in the air by heat convection and heat radiation, and the heat dissipation coefficient is set to 0.17. Finally, the contact definition. Each simulation model has two groups of contacts to be defined. The first group is the roller and the rolled piece surface, and the second group is the cladding stainless steel tube and the base core rod surface. The contact definition results of each simulation model are shown in Table 1. Among them, in the friction constraint, the friction coefficient is related to the sliding speed, compressive stress and rolling temperature.

[0110] Table 1 Contact definition results of rough, medium and finish rolling models

[0111]

[0112] Through testing, it was found that after the rough rolling of the covered steel bars, the bonding strength of the composite surface reached the national calibration value. Therefore, in the rough rolling model, the composite surface was defined as a friction constraint, and in the intermediate rolling and finishing rolling models, the composite surface was defined as a binding constraint.

[0113] (4) Network division.

[0114] Meshing is the most important part in the establishment of finite element models. The purpose of meshing is to discretize the continuum into small units, so as to simulate the real physical system. The mesh geometry, size and density will affect the calculation accuracy of the finite element model to a certain extent. In ABAQUS software, the mesh shapes mainly include tetrahedron, hexahedron and wedge. Compared with tetrahedron and wedge, hexahedron mesh has higher quality and the simulation results are easier to converge. Therefore, when meshing, hexahedron mesh is preferred. In addition, the mesh needs to be refined in areas where stress is more concentrated or curvature changes greatly, such as fillets and sharp corners, while the mesh can be appropriately diluted in areas where curvature changes more gently to improve calculation efficiency.

[0115] According to the above principles, the mesh size, shape and type of each simulation model are shown in Table 2.

[0116] Table 2 Grid shape, size and type of rough, medium and finishing rolling models

[0117]

[0118] In order to effectively simulate the metal flow trend during rolling, the mesh of the roll fillet and the crescent groove is refined. The mesh size of the above position is 0.5mm. The mesh division result of the crescent groove is as follows Figure 3 shown.

[0119] In addition, in order to prevent mesh distortion during the rolling process, a step-by-step simulation method is used. This method divides the rolling process into rough rolling, intermediate rolling, and finishing rolling. After the rough rolling is completed, the head and tail of the mesh distortion are cut off and the mesh is re-divided. Finally, the mesh re-divided model is imported into the ABAQUS software as the input of the intermediate rolling model. The schematic diagram of the step-by-step simulation is shown in the figure. Figure 1 As shown, the specific steps are as follows:

[0120] (1) According to the steps described above, the rough rolling simulation model of the coated steel bar was established using ABAQUS software and submitted for calculation;

[0121] (2) After the model calculation is completed, the rough rolling simulation results are imported into the HYPERMESH software;

[0122] (3) Use physical cutting to remove the head and tail of the rolled piece and re-divide the grid. The specific steps are as follows:

[0123] 1) Use the "Mesh-Surface" command to convert the workpiece after rough rolling into a surface.

[0124] 2) Use the "Surface - Solid" command to convert the surface into a solid.

[0125] 3) Delete the original mesh after rough rolling.

[0126] 4) Use the "Solid Cut" command to cut off the head and tail parts with larger deformation.

[0127] 5) Use the "Mesh Creation" command to divide the cut-off entity into hexahedral meshes.

[0128] 6) Import the meshed model into ABAQUS as the input of the intermediate rolling model for simulation.

[0129] (4) After the intermediate rolling simulation model is calculated, the intermediate rolling simulation results are imported into the HYPERMESH software; the head and tail of the intermediate rolling simulation results are cut off using the cutting plane, and the mesh is re-divided; the intermediate rolling simulation results after mesh re-division are imported into ABAQUS, and a finishing rolling simulation model is established based on the results; after the finishing rolling simulation model is calculated, the finished coated steel bars are rolled. The step-by-step simulation results are shown in Figure 1. Figure 4 The error statistics of the step-by-step simulation results are shown in Table 3.

[0130] Table 3 Error statistics of step-by-step simulation results

[0131]

[0132] In order to evaluate the advantages and disadvantages of the step-by-step simulation, a traditional finishing rolling model of coated steel bars was established. Unlike the step-by-step simulation, the traditional finishing rolling simulation directly sets the rolled piece as a cylinder with uniformly distributed coating. Figure 6 The simulation error of the traditional finishing rolling model is shown in Table 4.

[0133] Table 4 Statistics of simulation errors of traditional finishing rolling model

[0134]

[0135] It can be seen from Tables 3 and 4 that the step-by-step simulation results are very close to the actual ones, with the maximum simulation error being only 5.71%, while the traditional simulation result is 20%.

[0136] Process optimization steps:

[0137] First, according to the industrial site conditions, the rolling parameters of the covered steel bars are determined, including: intermediate rolling temperature, final rolling temperature, heating time, heating temperature, rough rolling hole type, intermediate rolling hole type, rough rolling reduction, final rolling reduction, round hole diameter (finished product front hole), flat elliptical groove bottom width, flat elliptical hole height, crescent groove chamfer radius, finished hole roller diameter, finished hole rolling speed, push tension and initial rolling temperature, a total of 16 parameters;

[0138] Then, using the data from 25 industrial experiments, non-standard regression analysis was performed on the above parameters. The analysis is shown in Table 5.

[0139] Table 5 Nonstandard regression analysis results

[0140]

[0141]

[0142] From Table 5, eight parameters with larger regression coefficients are selected as dependent variables for orthogonal variance analysis, namely, the diameter of the circular hole (finished product front hole), the width of the flat elliptical groove bottom, the height of the flat elliptical hole, the radius of the crescent groove chamfer, the diameter of the finished hole roller, the rolling speed of the finished hole, the push tension and the initial rolling temperature.

[0143] Then the above eight parameters were analyzed by orthogonal method, and the orthogonal table type finally selected nine factors and eight levels orthogonal experimental table L 64 (8 9 ), and the level values ​​of each factor are shown in Table 6.

[0144] Table 6 Level values ​​of each factor

[0145]

[0146] The results of orthogonal analysis are shown in Table 7.

[0147] Table 7 Statistical values ​​of each factor

[0148]

[0149] According to the results in Table 7, it is finally determined that the five parameters of circular hole diameter, flat elliptical groove bottom width, flat elliptical hole height, finished hole rolling speed and initial rolling temperature have a greater impact on the minimum value of the coating. The above five parameters are used as BP neural network input parameters.

[0150] Using the roulette method, 500 parameter combinations are randomly generated for the above five parameters, and the 500 combinations are substituted into the finite element model to obtain the minimum values ​​of the cladding corresponding to the 500 combinations;

[0151] According to 500 parameter combinations and the corresponding minimum values ​​of the covering layer, a BP neural network algorithm is constructed, of which 400 parameter combinations are training samples and 100 parameter combinations are prediction samples. In the BP neural network, the number of hidden layers and nodes is optimized by the particle swarm algorithm. The specific steps are as follows:

[0152] (1) Set the number of hidden layers to 1 to 10. Set the number of hidden layer nodes to 10 to 100.

[0153] (2) Set the number of initialized particle swarms to 20 and the number of iterations to 5000;

[0154] (3) Automatic optimization is performed to finally obtain the optimal number of hidden layers and nodes. It should be noted that the fitness value of each particle is the minimum value of the coating thickness predicted by the BP neural network corresponding to the particle.

[0155] Then, the optimized hidden layer number 4 and node number 13 are substituted into the BP neural network algorithm.

[0156] Finally, the genetic algorithm is used to optimize the rolling parameters. The specific steps are as follows:

[0157] Set the population size to 20 and the number of iterations to 2000;

[0158] The optimized BP neural network is used as the adaptive value of the genetic algorithm;

[0159] With the goal of maximizing the fitness value, the rolling parameters are optimized.

[0160] Finally, the optimized parameter combination is: round hole diameter 1.23d 钢筋 , flat elliptical groove bottom width 0.95d 钢筋 , flat elliptical hole height 0.69d 钢筋, the rolling speed of the finished hole is 48 rad / s, and the initial rolling temperature is 1100°C. At this time, the minimum coating thickness reaches 0.49 mm.

[0161] Applying the above parameters to actual production, the minimum coating thickness in actual production is 0.5mm. Figure 5 Comparison shows that the minimum coating thickness is significantly improved. In addition, a lateral comparison of the actual coating thickness (0.5 mm) and the predicted value (0.49 mm) shows that the error of the predicted coating thickness is only 2%.

[0162] The above contents are further detailed descriptions of the present invention in combination with specific preferred embodiments, and it cannot be determined that the specific implementation of the present invention is limited to these descriptions. For ordinary technicians in the technical field to which the present invention belongs, several simple deductions and substitutions can be made without departing from the concept of the present invention, which should be regarded as belonging to the protection scope of the present invention.

Claims

1. A method for optimizing the rolling process of coated steel bars based on finite element method, characterized in that: The following steps are involved: S1. Using thermal simulation experiments, the stress-strain curves of the base and cladding materials at different temperatures are obtained. The material parameters of the base and cladding are obtained using Jmatpro software, and a finite element model is established. S2, the rolling of the coated steel bar is divided into rough rolling, medium rolling and finishing rolling. After the rough rolling is completed in the established finite element model, the head and tail of the billet are cut off, and the grid is re-divided. Then, the medium rolling is performed in the established finite element model. After the medium rolling is completed, the head and tail of the billet are cut off again, and the grid is re-divided, and the finishing rolling is performed again until the rolling is completed; S3. Based on the finite element model, the hole type and rolling parameters are optimized.

2. The method for optimizing the rolling process of coated steel bars based on finite element method according to claim 1, characterized in that: The specific steps of S1 include: S11, processing the covered steel bar into a thermal simulation specimen of a specified shape; S12, clamping the thermal simulation sample, and then heating it to 900° C., 950° C., 1000° C., 1050° C., and 1100° C. respectively; S13, after heating, keep warm, then heat for 1s -1 After compression, the stress-strain curve is derived.

3. The method for optimizing the rolling process of coated steel bars based on finite element method according to claim 2, characterized in that: The specific step S1 also includes: S14, measuring the content of each element in the coated steel bar using a spectrometer; S15, then input the content of each element into Jmatpro software; S16. Use Jmatpro software to export the material parameters of the base layer and the covering layer.

4. The method for optimizing the rolling process of coated steel bars based on finite element method according to claim 3, characterized in that: The material parameters include thermal conductivity, specific heat, and Poisson's ratio.

5. The method for optimizing the rolling process of coated steel bars based on finite element method according to claim 1, characterized in that: The specific steps of S2 include: S21. Use ABAQUS software to establish a simulation model for rough rolling of coated steel bars and submit the calculation; S22. After the rough rolling simulation model is calculated, the rough rolling simulation results are imported into the HYPERMESH software; S23, using a cutting plane to cut off the head and tail of the rough rolling simulation result, and re-dividing the grid; S24, importing the rough rolling simulation results after mesh re-division into ABAQUS, and establishing a medium rolling simulation model based on the results; S25, after the calculation of the intermediate rolling simulation model is completed, the intermediate rolling simulation results are imported into the HYPERMESH software; S26, using a cutting plane to cut off the head and tail of the rolling simulation result, and re-dividing the grid; S27, importing the intermediate rolling simulation results after mesh re-division into ABAQUS, and establishing a finishing rolling simulation model based on the results; S28. After the calculation of the finishing rolling simulation model is completed, the finished coated steel bars are rolled out.

6. The method for optimizing the rolling process of coated steel bars based on finite element method according to claim 1, characterized in that: The specific steps of S3 include: S31. Determine the rolling process parameters of the coated steel bars according to the industrial site conditions; S32. Preliminary screening of rolling process parameters based on limited industrial experiments on coated steel bars; S33, performing secondary screening on the rolling process parameters after the preliminary screening by using orthogonal variance analysis to determine the rolling process parameters to be optimized; S34, taking the rolling process parameters to be optimized as variables, constructing a BP neural network algorithm to achieve prediction of the minimum coating thickness; S35, taking the BP neural network prediction value as the adaptability value, and optimizing the rolling parameters using the genetic algorithm, wherein the optimization goal is to maximize the adaptability value, that is, the larger the minimum value of the coating thickness, the better; S36. Output the optimized result.

7. The method for optimizing the rolling process of coated steel bars based on finite element method according to claim 6, characterized in that: In the step S34: In the constructed BP neural network algorithm, the number of hidden layers and nodes is automatically optimized through the embedded particle swarm algorithm.

8. The method for optimizing the rolling process of coated steel bars based on finite element method according to claim 7, characterized in that: The specific steps of the embedded particle swarm algorithm are as follows: S341, taking the number of hidden layers and the number of nodes as independent variables for particle swarm optimization; S342, determining the range of the number of hidden layers and the number of nodes; S343, determining the number of initialized particle swarms and the number of iterations; S344, with the goal of maximizing the minimum coating thickness, automatically search for the best number of hidden layers and nodes.

9. The method for optimizing the rolling process of coated steel bars based on finite element method according to claim 8, characterized in that: In step S344, the fitness value of each particle is the minimum coating thickness predicted by the BP neural network corresponding to the particle.

10. The method for optimizing the rolling process of coated steel bars based on finite element method according to claim 1, characterized in that: In S2, in the rough rolling stage, each node of the composite surface is defined as a friction constraint, and in the intermediate rolling stage, each node of the composite surface is defined as a binding constraint.

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