Online high-precision rod and wire structure property prediction and production method

Abstract

An on-line high-precision rod and wire structure performance prediction and production method comprises the steps of selecting historical production data matched with a current rod and wire production process, determining an optimal prediction model based on the matched historical production data, performing simulation calculation on a produced rod and wire based on the prediction model, and obtaining a production result of the rod and wire. Simulating a temperature field, a structure and performance parameters; judging whether the errors of the simulated temperature field, the structure and the performance parameters and the measured values of the production rods and wires are within an allowable range or not, if so, backing up and storing corresponding production data and a prediction model, and if not, performing model parameter optimization by combining the current production data until the errors are within the allowable range; and on the basis of the optimized prediction model, the production data of the current rod and wire are simulated, a temperature field, a structure and performance parameters are simulated, whether the simulated structure performance meets the national production standard and the expected value of a factory or not is judged, and if yes, rolling is conducted.

Description

Technical Field

[0001] The invention relates to the technical field of hot rolling, and in particular to an online high-precision rod and wire structure performance prediction and production method. Background Art

[0002] In the current global manufacturing industry, the steel industry is the core of the basic industry, and its production technology and quality control level directly affect the development of the entire manufacturing industry and the country's industrial safety. With the continuous changes and improvement of market demand, the performance standards for steel products are becoming more and more stringent, especially the organizational properties of bars and wires, which are directly related to the final mechanical properties and application range of the products. Traditional bar and wire production methods often rely on experience adjustment and post-testing, which is not only inefficient, but also difficult to cope with rapidly changing market demands and strict quality standards.

[0003] In recent years, the rapid development of information technology and intelligent manufacturing has brought new opportunities for change in steel production, and provided new ideas and methods for improving production efficiency and product quality. Through these technologies, real-time data monitoring and analysis can be achieved in the production process, so as to predict and adjust production parameters and optimize product performance. In this context, it is particularly important and urgent to develop an online high-precision prediction technology for the microstructure and properties of rods and wires and its production process. Based on the production data collected in real time and using advanced prediction models, this technology can accurately predict the microstructure and properties of rods and wires under different production data. This prediction can not only improve the transparency of the production process, but also foresee potential quality problems in the early stages of production, so that adjustments can be made in advance to ensure product quality.

[0004] Currently, the commonly used microstructure and performance prediction technologies are mainly based on physical metallurgical models or big data models, and are mainly offline prediction systems. There is no online prediction technology that simultaneously considers the ingot state (ingot out of the heating furnace or direct rolling), bar and wire rolling. Summary of the invention

[0005] In view of the technical defects and technical drawbacks in the prior art, the embodiments of the present invention provide an online high-precision rod and wire structure performance prediction and production method that overcomes the above problems or at least partially solves the above problems. The specific scheme is as follows:

[0006] An online high-precision rod and wire structure property prediction and production method, the method comprising:

[0007] Step 1, selecting historical production data that has a high degree of matching with the production process of rods and wires, determining the optimal prediction model based on the historical production data, performing simulation calculations based on the prediction model, and simulating the temperature field, organization, and performance parameters;

[0008] Step 2: Determine whether the error between the simulated temperature field, organization and performance parameters and the actual measured values ​​of production is within the allowable range. If so, the prediction model is considered to be a well-matched model, and the corresponding production data and the prediction model are backed up into the database. If not, the model parameters are optimized in combination with the current production data until the error is within the allowable range.

[0009] Step 3: Based on the optimized prediction model, simulate the current production data of bars and wires, simulate the temperature field, organization and performance parameters, and judge whether the simulated organization performance meets the national production standards and the factory's expectations. If so, roll.

[0010] Furthermore, the optimal prediction model is determined by the historical production data in the following way: an adjustment factor of the prediction model is obtained, and when the adjustment factor of the prediction model reaches a preset matching degree with the historical production data, the corresponding prediction model is considered to be the optimal prediction model.

[0011] Among them, the adjustment factors of the prediction model include the chemical composition of the material, rolling specifications, rolling schedule, rolling process temperature, cooling water tank parameters, air cooling line parameters, etc.; the production data also include chemical composition, rolling specifications, rolling schedule, rolling process temperature, cooling water tank parameters, air cooling line parameters, etc. For example, if the chemical composition error is within ±3%, the rolling specifications are the same, the rolling schedule hole system error is within ±3%, the rolling process temperature error is within ±5°C, the cooling water tank parameter error is within ±3%, and the air cooling line parameter error is within ±5%, then it is considered that the adjustment factors of the prediction model have reached a preset matching degree with the historical production data.

[0012] Furthermore, the prediction model is developed based on a physical metallurgical model.

[0013] Furthermore, the prediction model includes a temperature field model, a tissue evolution model, and a tissue and mechanical property relationship model.

[0014] Furthermore, simulation calculation is performed based on the temperature field model to simulate the temperature field parameters, including:

[0015] When simulating the temperature field of the entire process, the temperature field model first models the billet and divides it into two-dimensional grids. The rolling direction, number of grids, radial direction and number of grids are set by default. The grid division is adjusted according to the actual iteration situation before simulation to improve the efficiency of the simulation calculation. During the simulation, the appropriate time step is determined by calculation to ensure the convergence of the calculation, and the corresponding material thermophysical properties are imported from the database. The current heat exchange model, initial conditions and boundary conditions are selected based on the process position of the incremental step, and the temperature field from the heating furnace or continuous casting outlet to the finished product collection area is calculated. After completion, the temperature field simulation results are saved and output.

[0016] Furthermore, simulation calculations are performed based on the tissue evolution model to simulate tissue parameters, including:

[0017] The temperature field parameters obtained by simulation are used as initial conditions, and the rolling process parameters are imported. According to the rolling process parameters, the strain ε and strain rate of each pass are calculated. Rolling temperature T, action time t of each stage and initial size d0 of austenite before rolling. In a certain rolling pass, the microstructure evolution model first calculates the critical strain value ε for dynamic recrystallization to occur. c and the current accumulated strain value ε * , when ε * ≥ε c When dynamic recrystallization occurs, the dynamic recrystallization grain size d is calculated. d and the recrystallization percentage X d , when ε * ≤ε c When static recrystallization occurs, calculate the static crystal grain size d s and the recrystallization percentage X s , during the gap between passes, the microstructure evolution model first determines the percentage of recrystallization that occurs X d or X s Is it ≥0.95? If so, calculate the static recrystallization grain growth d after all interpass recrystallization is completed. SG and metadynamic recrystallization grain growth d MG If not, calculate the interpass recrystallization without complete static recrystallization grain growth d SG and metadynamic recrystallization grain growth d MG ,After the grain growth calculation is completed, the organizational evolution model calculates the average grain size, outputs and saves various types of recrystallization occurrence;

[0018] The organizational evolution model will cyclically calculate the first pass to the last stand rolling mill pass. After the cycle is completed, the average grain size, temperature field and residual strain of austenite before cooling phase transformation will be output and saved as the initial conditions of phase transformation.

[0019] Furthermore, the method also includes: in the cooling stage after rolling is completed, if the average temperature of the intermediate billet is higher than the phase equilibrium temperature Ae3, the austenite grains will continue to grow. At this time, the organizational evolution model first uses the superposition method to calculate the growth of the grain size based on the temperature field of the whole process. After the average temperature of the intermediate billet reaches the Ae3 temperature, the austenite isothermal decomposition begins, and the phase transformation incubation period model is used to determine the starting temperature of each phase. The volume fraction of each phase transformation is calculated by the continuous isothermal superposition method based on the phase transformation kinetic equation. When the volume fraction of the transformation reaches 98% of the theoretical value of the calculation model of the maximum transformation amount of each phase, the phase transformation is considered to be completed, and the final grain size is calculated.

[0020] Furthermore, simulation calculations are performed based on the tissue mechanical properties relationship model to simulate performance parameters, including:

[0021] The chemical composition, average grain size of each phase and transformation volume fraction of each phase of the produced rods and wires are imported into the microstructure-mechanical-property relationship model. The microstructure-mechanical-property relationship model predicts the yield strength, tensile strength, elongation and total elongation at maximum force of the produced rods and wires based on the imported chemical composition, average grain size of each phase and transformation volume fraction of each phase.

[0022] Furthermore, in step 3, if the simulated microstructure performance does not meet the national production standards and the factory's expectations, the controlled cooling process is adjusted and the simulation is continued until the simulated microstructure performance meets the national production standards and the factory's expectations, wherein the adjustment of the controlled cooling process includes the starting rolling temperature, water cooling parameters, and air cooling parameters.

[0023] The present invention has the following beneficial effects:

[0024] 1. High-precision and real-time prediction model: The present invention establishes an online high-precision rod and wire structure performance prediction technology by integrating physical metallurgical models with data-driven methods. This technology can match the optimal simulation model parameters according to the data received on the production line in real time, and predict the structure evolution of the rod and wire and its final performance in real time. During the actual application of the prediction model, the system can self-adjust and optimize according to the error between the actual measured value and the predicted value of the current production. Through continuous iterative calculation and parameter adjustment, the system keeps the prediction model in the optimal state at all times, ensuring the prediction accuracy and reliability. This real-time prediction and self-optimization mechanism greatly reduces the need for human intervention, allowing production adjustments to be faster and more accurate to cope with various deviations and changes in production.

[0025] 2. Improved production quality control: By real-time monitoring and predicting the microstructure and properties of bars and wires, the present invention can foresee and identify potential quality problems in the early stages of production. By timely adjusting production parameters, such as controlled cooling process, rolling temperature, etc., it can ensure that the final product meets or exceeds quality standards and meets factory expectations, significantly reducing scrap rates and improving the overall efficiency and economic benefits of the production line.

[0026] 3. Strong flexibility and adaptability: The technical solution of the present invention takes into account different production conditions and requirements, and can adapt to various production lines and material characteristics. Whether it is billeting from a heating furnace or direct rolling, whether it is bar or wire rolling, the system can provide accurate temperature field and organizational performance prediction, thereby adapting to diverse production needs. This flexibility makes this technology BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1A flow chart of an online high-precision rod and wire structure property prediction and production method provided by an embodiment of the present invention;

[0028] Figure 2 A flow chart of the temperature field model operation of the whole process provided by the embodiment of the present invention;

[0029] Figure 3 A flowchart of the operation of the organizational evolution model of the whole process provided by the embodiment of the present invention;

[0030] Figure 4 Schematic diagram of the relationship model between organization and mechanical properties provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Embodiment 1:

[0033] See also Figure 1 The embodiment of the present invention provides an online high-precision rod and wire structure performance prediction and production method, which mainly includes two parts: model optimization and process adjustment.

[0034] The specific implementation steps of model optimization are as follows:

[0035] Select historical production data with a high degree of matching and predictive model for simulation calculation to simulate the temperature field, organization and performance parameters;

[0036] Determine whether the error between the simulated temperature field, organization and performance and the actual measured value of current production is within the allowable range. If so, back up the current production data and the prediction model into the database. If not, optimize the model parameters in combination with the current production data until the error is within the allowable range.

[0037] Module 2: Process adjustment. The specific implementation steps are:

[0038] Based on the optimized high-precision prediction model, determine whether the simulated microstructure performance meets the national production standards and the factory's expectations. If so, rolling is performed; if not, the controlled cooling process (hereinafter referred to as the controlled cooling process) is adjusted to continue the simulation until the microstructure performance meets the standards and expectations.

[0039] In the embodiment of the present invention, the entire simulation is calculated using a model formula. In order to ensure the reliability of the entire calculation accuracy, the initial model parameters are determined by fitting based on the experimental data of typical low-alloy steel and the actual production situation. However, due to the differences in the environment and other production conditions of each steel plant, the model parameters need to be appropriately adjusted to maintain the accuracy of the simulation. Therefore, when calculating the temperature field, each heat exchange model, the grain size model when calculating the organizational evolution, and the performance prediction model will have an adjustment factor. For a new application case or one without historical data as a reference, the adjustment factor will be iteratively optimized according to the default value until the simulation accuracy meets expectations, and then this set of optimized adjustment factors and their production data (chemical composition of materials, rolling specifications, rolling schedule, rolling process temperature, cooling water tank parameters, air cooling line parameters, etc.) will be recorded as empirical knowledge. When a large amount of empirical data is stored in the database, if the new simulated production data is similar to the empirical data (the judgment rule in the system is that the chemical composition error is within ±3%, the rolling specifications are the same, the rolling program table hole system error is within ±3%, the rolling process temperature error is within ±5°C, the cooling water tank parameter error is within ±3%, and the air cooling line parameter error is within ±5%, then the two production data are considered to be basically consistent), the adjustment factor is directly used to improve the calculation efficiency. The optimal prediction model is that the adjustment factor matches the current production data and can simulate the results that are not much different from the actual production. The expected deviation value can be adjusted according to demand. The system defaults to ±10°C for the temperature field, ±8% for the grain size, and 10MPa for the performance prediction. Among them, historical production data is used to confirm the adjustment factors of the prediction model, that is, to confirm the final prediction model. Unless the production data is exactly the same, the results of the historical data can be directly used as the simulation results of this time, but the actual production data will always be different, so re-simulation is required. Although there will be a lot of historical data, they have all been classified and can be retrieved very quickly, and the memory of the data stored each time is very small and does not take up space.

[0040] Among them, the prediction model is based on the physical metallurgical model, including the temperature field model of the whole process, the tissue evolution model and the tissue mechanical property relationship model. The model parameter optimization specifically refers to the optimization of the temperature field heat exchange model parameters, the tissue evolution model parameters and the tissue and mechanical property relationship model parameters.

[0041] The adjustment of the controlled cooling process is divided into two situations. For the bar production line, the controlled cooling process includes the adjustment of the rolling temperature and water cooling parameters. For the wire production line, the controlled cooling process is the Stelmor controlled cooling process, including the adjustment of the rolling temperature, water cooling parameters and air cooling parameters.

[0042] Among them, the temperature field model of the whole process of the prediction model is refined according to the actual physical process of the rolled piece, and is specifically divided into: conveying or inter-pass heat exchange model, descaling water tank heat exchange model, temperature rise model during rolling, roller contact heat conduction model, water cooling heat exchange model, cooling bed heat exchange model and wire rod Stelmor heat exchange model. The model takes into account the following rolling process parameters: material material and its thermophysical performance parameters, rolling specifications, equipment process layout parameters, pre-rolling status (furnace temperature or continuous casting outlet temperature, whether the conveyor roller insulation cover is enabled), rolling schedule, room temperature, descaling water tank status, cooling water temperature and pressure, water tank nozzle status, Stelmor air cooling line fan on / off status and air volume, the speed of each roller of the air cooling line, and whether the air cooling line is enabled with an insulation cover. The temperature field is calculated using the finite difference method, in which the one-dimensional unsteady-state heat conduction equation is used for rods, and the one-dimensional unsteady-state heat conduction equation (from before rolling to the spinning machine) and the two-dimensional unsteady-state heat conduction equation (air cooling after the spinning machine) are combined for wires to improve the calculation efficiency.

[0043] Among them, the whole process organization evolution model of the prediction model is subdivided into the grain growth model before rolling, the recrystallization model during rolling and the phase transformation model of the collection area. The grain growth model before rolling is further divided into the austenite grain growth model during the heating process and the insulation process. The recrystallization model during the rolling process is further divided into dynamic recrystallization, sub-dynamic recrystallization, static recrystallization, grain growth after recrystallization, incomplete recrystallization and residual strain models. The phase transformation model of the collection area is refined into grain growth before phase transformation, isothermal decomposition of austenite, phase transformation start temperature / phase transformation incubation period, phase transformation superposition, maximum transformation amount of phase transformation, volume fraction of each phase and grain size calculation model of each phase. The whole process organization evolution model is developed based on the calculated whole process temperature field data, using the same incremental step as the temperature field model.

[0044] Among them, the organization and mechanical property relationship model of the prediction model is specifically divided into yield strength, tensile strength and elongation models according to the following indicators in the present invention. The above model imports the chemical composition, rolling specifications and other parameters of the material, and combines the grain size and volume fraction of each phase obtained by the previous simulation to calculate, so as to ensure that the mechanical properties of the simulated material conform to the laws of physical metallurgy.

[0045] Embodiment 2:

[0046] When performing temperature field parameter simulation calculation, the prediction model is the temperature field model of the whole process, see Figure 2, which is a flow chart of the operation of the temperature field model of the whole process provided by the embodiment of the present invention. When simulating the temperature field of the whole process, the prediction model first models the billet and divides the two-dimensional grid. The default number of grids in the rolling direction (X axis) is 20, and the number of grids in the radial direction (Y axis) is 4. The grid division is adjusted according to the actual iteration situation before the simulation to improve the simulation calculation efficiency. During the simulation, the system determines the appropriate time step by calculation to ensure the convergence of the calculation, and imports the corresponding material thermophysical property parameters from the database. Based on the process position of the incremental step, the current heat exchange model, initial conditions and boundary conditions are selected, and the temperature field from the heating furnace or continuous casting outlet to the finished product collection area is calculated. After completion, the system saves and outputs the temperature field simulation results. The prediction model then simulates the whole process organization evolution based on the whole process temperature field obtained by simulation.

[0047] Embodiment 3:

[0048] When performing tissue parameter simulation calculation, the prediction model is a whole-process tissue evolution model, see Figure 3 , which is a flow chart of the operation of the whole process organization evolution model provided by the embodiment of the present invention, is based on the temperature field simulation results, and further simulates the whole process organization evolution. This model uses the simulated temperature field as the initial condition and imports the rolling process parameters, such as steel type, rolling specifications, billet size information, rolling program table, controlled cooling parameters, etc. Based on these parameters, the system calculates the strain ε and strain rate of each pass. Rolling temperature T, action time t of each stage, initial size of austenite before rolling d0. When calculating the initial size of austenite before rolling d0, the model takes into account both heating and insulation, and the effect of the temperature drop process of the conveyor roller on the grain size is negligible. During a certain rolling pass, the model first calculates the critical strain value ε for dynamic recrystallization to occur c and the current accumulated strain value ε * , when ε * ≥ε c When dynamic recrystallization occurs, the dynamic recrystallization grain size d is calculated. d and the recrystallization percentage X d , when ε * ≤ε c When static recrystallization occurs, calculate the static crystal grain size d s and the recrystallization percentage X s This model assumes that once a grain undergoes dynamic recrystallization, most of its deformation is released, and the remaining deformation energy is insufficient to drive the grain to undergo static recrystallization. That is, the grain growth process of the grain that has undergone dynamic recrystallization is calculated as sub-dynamic recrystallization. According to this idea, during the gap between passes, the model first determines the recrystallization fraction X d or X sIs it ≥0.95? If so, calculate the static recrystallization grain growth d after all interpass recrystallization is completed. SG and metadynamic recrystallization grain growth d MG If not, calculate the interpass recrystallization without complete static recrystallization grain growth d SG and metadynamic recrystallization grain growth d MG The two cases differ in the time of grain growth. The former is equal to the gap time of the current pass minus the time required for recrystallization to complete, and the latter is equal to the gap time of the current pass minus the time required for the fraction of recrystallization that has occurred. After the grain growth calculation is completed, the model will calculate the average grain size and output and save various types of recrystallization. The model will cyclically calculate the first pass to the last stand mill pass. After the cycle is completed, the average grain, temperature field and residual strain of austenite before cooling phase transformation will be output and saved as the initial conditions of phase transformation.

[0049] In the cooling stage after rolling, if the average temperature of the intermediate billet is higher than the phase equilibrium temperature Ae3, the austenite grains will continue to grow. At this time, the model first uses the superposition method to calculate the growth of grain size based on the temperature field of the whole process. After reaching the Ae3 temperature, the isothermal decomposition of austenite begins. First, the phase transformation incubation period model is used to determine the starting temperature of each phase transformation. Then, the continuous isothermal superposition method is used based on the phase transformation kinetic equation to calculate the volume fraction of each phase transformation. When the volume fraction of the transformation reaches 98% of the theoretical value of the calculation model of the maximum transformation amount of each phase, the phase transformation is considered to be completed, and the final grain size is calculated, including the average grain size of ferrite and the lamellar distance of pearlite.

[0050] Embodiment 4:

[0051] When simulating and calculating performance parameters, the prediction model is a model of the relationship between tissue and mechanical properties, see Figure 3 , which is a flow chart of the operation of the organization and mechanical property relationship model provided in an embodiment of the present invention. The model comprehensively considers factors such as chemical composition, average grain size of each phase and transformation volume fraction of each phase, and predicts the yield strength, tensile strength, elongation and maximum force total elongation of the product. The influence of chemical composition takes into account the influence of multiple elements such as C, Mn, Si, V, N, etc., and the average grain size and transformation volume fraction of each phase take into account the influence of crystal organizations such as austenite, ferrite, pearlite, bainite, etc.

[0052] like Figure 1As shown, after completing the simulation, the system of the present invention will verify whether the error between the simulated temperature field-organization-performance data and the actual measured value is within the allowable range. If not, the relevant model parameters are adjusted until the error between the simulated temperature field-organization-performance data and the actual measured value is within the allowable range; if it is, the data is cleaned and archived together with the prediction model to enrich the historical data. Then, the system determines whether the simulated organizational performance meets the production standards and factory expectations. If so, rolling is carried out. If not, the controlled cooling process is adjusted and the simulation is continued until the requirements are met. The adjustment of the controlled cooling process includes the start rolling temperature, water cooling parameters, and air cooling parameters.

[0053] Since the implementation of this scheme, the prediction accuracy is relatively high. See Table 1-3 below for the comparison between the measured values ​​and the predicted values ​​of this scheme on different production lines:

[0054]

[0055] Table 1 Table 2

[0056]

[0057]

[0058] Table 3

[0059] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. An online high-precision rod and wire structure performance prediction and production method, characterized in that: The method comprises: Step 1, selecting historical production data that matches the production process of rods and wires, determining the optimal prediction model based on the matching historical production data, performing simulation calculations on the production rods and wires based on the prediction model, and simulating the temperature field, organization, and performance parameters; Step 2: determine whether the error between the simulated temperature field, organization and performance parameters and the measured values ​​of the produced rods and wires is within the allowable range. If yes, the corresponding production data and prediction model are backed up into the database. If not, the model parameters are optimized in combination with the current production data until the error is within the allowable range. Step 3: Based on the optimized prediction model, simulate the current production data of bars and wires, simulate the temperature field, organization and performance parameters, and judge whether the simulated organization performance meets the national production standards and the factory's expectations. If so, roll.

2. The method for online high-precision prediction and production of rod and wire structure properties according to claim 1, characterized in that: Determining the optimal prediction model based on historical production data specifically includes: obtaining an adjustment factor of the prediction model, and when the adjustment factor of the prediction model reaches a preset matching degree with the historical production data, the corresponding prediction model is considered to be the optimal prediction model.

3. The method for online high-precision prediction and production of rod and wire structure properties according to claim 1, characterized in that: The prediction model is developed based on a physical metallurgical model.

4. The method for online high-precision prediction and production of rod and wire structure properties according to claim 3 is characterized in that: The prediction model includes a temperature field model, a tissue evolution model and a tissue and mechanical property relationship model.

5. The method for online high-precision prediction and production of rod and wire structure properties according to claim 4, characterized in that: Based on the temperature field model, simulation calculation is performed to simulate the temperature field parameters, including: When simulating the temperature field of the entire process, the temperature field model first models the billet and divides it into two-dimensional grids. The rolling direction, number of grids, radial direction and number of grids are set by default. The grid division is adjusted according to the actual iteration situation before simulation to improve the efficiency of the simulation calculation. During the simulation, the appropriate time step is determined to ensure the convergence of the calculation, and the corresponding material thermal physical properties are imported from the database. The current heat exchange model, initial conditions and boundary conditions are selected based on the process position of the incremental step, and the temperature field from the heating furnace or continuous casting outlet to the finished product collection area is calculated. After completion, the temperature field simulation results are saved and output.

6. The method for online high-precision prediction and production of rod and wire structure properties according to claim 4, characterized in that: Based on the organizational evolution model, simulation calculations are performed to simulate organizational parameters, including: The temperature field parameters obtained by simulation are used as initial conditions, and the rolling process parameters are imported. According to the rolling process parameters, the strain ε and strain rate of each pass are calculated. Rolling temperature T, action time t of each stage and initial size d0 of austenite before rolling. In a certain rolling pass, the microstructure evolution model first calculates the critical strain value ε for dynamic recrystallization to occur. c and the current accumulated strain value ε * , when ε * ≥ε c When dynamic recrystallization occurs, the dynamic recrystallization grain size d is calculated. d and the recrystallization percentage X d , when ε * ≤ε c When static recrystallization occurs, calculate the static crystal grain size d s and the recrystallization percentage X s , during the gap between passes, the microstructure evolution model first determines the percentage of recrystallization that occurs X d or X s Is it ≥0.95? If so, calculate the static recrystallization grain growth d after all interpass recrystallization is completed. SG and metadynamic recrystallization grain growth d MG If not, calculate the interpass recrystallization without complete static recrystallization grain growth d SG and metadynamic recrystallization grain growth d MG ,After the grain growth calculation is completed, the organizational evolution model calculates the average grain size, outputs and saves various types of recrystallization occurrence; The organizational evolution model will cyclically calculate the first pass to the last stand rolling mill pass. After the cycle is completed, the average grain size, temperature field and residual strain of austenite before cooling phase transformation will be output and saved as the initial conditions of phase transformation.

7. The method for online high-precision prediction and production of rod and wire structure properties according to claim 6, characterized in that: The method also includes: in the cooling stage after rolling is completed, if the average temperature of the intermediate billet is higher than the phase equilibrium temperature Ae3, the austenite grains will continue to grow. At this time, the organizational evolution model first uses the superposition method to calculate the growth of the grain size based on the temperature field of the whole process. After the average temperature of the intermediate billet reaches the Ae3 temperature, the austenite isothermal decomposition begins, and the phase transformation incubation period model is used to determine the starting temperature of each phase. The volume fraction of each phase transformation is calculated by the continuous isothermal superposition method based on the phase transformation kinetic equation. When the volume fraction of the transformation reaches 98% of the theoretical value of the maximum transformation amount calculation model of each phase, the phase transformation is considered to be completed, and the final grain size is calculated.

8. The method for online high-precision prediction and production of rod and wire structure properties according to claim 4, characterized in that: Based on the relationship model of tissue mechanical properties, simulation calculations are performed to simulate performance parameters, including: The chemical composition, average grain size of each phase and transformation volume fraction of each phase of the produced rods and wires are imported into the microstructure-mechanical-property relationship model. The microstructure-mechanical-property relationship model predicts the yield strength, tensile strength, elongation and total elongation at maximum force of the produced rods and wires based on the imported chemical composition, average grain size of each phase and transformation volume fraction of each phase.

9. The method for online high-precision prediction and production of rod and wire structure properties according to claim 1, characterized in that: In step 3, if the simulated microstructure performance does not meet the national production standards and the factory's expectations, the controlled cooling process is adjusted and the simulation is continued until the simulated microstructure performance meets the national production standards and the factory's expectations, wherein the adjustment of the controlled cooling process includes the start rolling temperature, water cooling parameters, and air cooling parameters.

Images