A high-precision prediction method for the microstructure and properties of deformed steel bars with split ribs

Abstract

The present invention relates to a method for predicting the high-precision microstructure and properties of split ribbed bars, belonging to the field of iron and steel smelting. The present invention relates to the temperature evolution, microstructure evolution, and property evolution processes throughout the rolling process. By using heat transfer models during rolling, water cooling, air cooling, and cooling bed processes, grain growth models in reheating furnaces and on the rolling line, recrystallization models and residual strain models on the rolling line, as well as phase transformation models and force-energy models on the cooling bed, etc., the parameters of each model are corrected based on the measured values of temperature, microstructure, and properties. The corrected sub-models are integrated, and various complex and multi-dimensional parameters such as chemical composition, rolling schedule, equipment parameters, and ambient temperature are coupled to build a B / S architecture program for predicting the finished microstructure and properties of split ribbed bars. Finally, the function of online temperature-microstructure property prediction can be realized. The present invention realizes simulated steel rolling, reduces the contingency of results caused by inspection technology defects, and is used to optimize the rolling and water cooling production processes of split ribbed bars.

Description

Technical Field

[0001] The present invention belongs to the field of steel smelting, and relates to a method for predicting the high-precision microstructure and properties of split bar of deformed steel bars. Background Art

[0002] The process of split bar of deformed steel bars is extremely complex, specifically including processes such as billet heating, billet soaking, descaling, rough rolling, medium rolling, pre-finishing rolling, splitting, finishing rolling, sizing and reducing, post-rolling water cooling, and air cooling on the cooling bed. The key to improving the properties and quality of split bar of deformed steel bars lies in formulating and adopting a reasonable controlled rolling and controlled cooling production process.

[0003] The rolling speed of bars is fast, and steel mills are profit-making enterprises. Usually, production cannot be interrupted. It is also very difficult to measure the temperature, microstructure, and properties of materials during the production process, and it is impossible to determine the specific factors affecting the microstructure and properties of bars. This also makes it difficult to formulate the process parameters of controlled rolling and controlled cooling.

[0004] The traditional method for formulating the controlled rolling and controlled cooling process is mainly to study the microstructure evolution law of a certain steel grade through thermal simulation experiments, and then test the mechanical properties. When meeting the expectations and standards, set the process flow according to the experimental parameters, conduct industrial trial rolling, and then return to modify the rolling process parameters and water cooling parameters according to the on-site trial rolling data, so as to determine the final rolling schedule and cooling regime. It can be seen that this method requires a large amount of financial and human resources, and the entire experimental and data analysis process also takes a lot of time, with low efficiency.

[0005] With the rapid development of the civil engineering industry, users have higher and higher requirements for the microstructure and properties of deformed steel bars. In the existing inspection means, usually only 4-5 bars are inspected for one heat of steel. This makes the results representing the microstructure and properties of the finished products of this heat of rolling have contingency and limitations, and it is impossible to confirm whether each billet is qualified.

[0006] Most of the existing microstructure and property prediction software for deformed steel bars is an offline system based on a neural network model, and can only predict for the existing process, or is an offline microstructure and property prediction system based on a physical metallurgy model. However, no system that can simultaneously consider the heated billet and the directly rolled billet and realize the online real-time prediction of temperature, microstructure, and properties throughout the rolling process has been seen. Summary of the Invention

[0007] In view of this, the purpose of the present invention is to provide a method for predicting the high-precision microstructure and properties of split bar of deformed steel bars, which can simulate the entire rolling and cooling process (from the continuous casting outlet / furnace outlet to the finished product off-line) of the head and tail of the billet, and is used to optimize the hot rolling and cooling processes of split bar of deformed steel bars, improve the qualification rate of product properties; connect with industrial production, realize simulated rolling, and can online predict the microstructure and properties of each billet, reducing the contingency of results caused by inspection technology defects.

[0008] To achieve the above object, the present invention provides the following technical solutions:

[0009] A high-precision microstructure and property prediction method for split ribbed steel bars, the method comprising the following steps:

[0010] 1) The high-precision microstructure and property prediction system for split ribbed steel bars is a B / S architecture system, which collects temperature, microstructure and property data on site and corrects the model structure and parameters in the calculation program;

[0011] 2) Upload a template with mill unit, stand number, stand elongation, equipment location, roll ring diameter, water tank length, water pressure, water volume, billet size parameters, cooling bed cooling time, finishing rolling speed, ambient temperature, split stand, number of splits, chemical composition, and heating regime at the front end of the interface;

[0012] 3) The processing backend receives the request from the front-end interface and forwards the calculation request to the calculation program;

[0013] 4) After receiving the request, the calculation program parses the template data and uses the finite difference and heat transfer model to simulate the temperature evolution process of the entire rolling and cooling process, specifically including descaling heat transfer model, air cooling heat transfer model, roll heat transfer model, rolling temperature rise model, water cooling heat transfer model, and cooling bed heat transfer model;

[0014] 5) The calculation program uses the austenite microstructure evolution model of the rolling line to simulate the recrystallization and grain growth processes during and after rolling, specifically including critical strain model, dynamic recrystallization model, meta-dynamic recrystallization model, and static recrystallization model;

[0015] 6) The calculation program uses the austenite phase transformation model to simulate the microstructure evolution process of the rolled piece on the cooling bed, specifically including incubation period model of each phase, CCT curve, maximum transformation amount model of ferrite and pearlite, volume fraction model of each phase, pearlite lamellar spacing model, and ferrite grain size model;

[0016] 7) The calculation program uses the force and energy model to calculate the mechanical properties of the rolled piece when it exits the cooling bed, specifically including yield strength model, tensile strength model, elongation model, and total elongation at maximum force model;

[0017] 8) The calculation program passes the calculation results into the database according to the agreed data structure, and then the processing backend retrieves the data and passes it to the front end for display on the result display interface.

[0018] Optionally, the heat transfer model is as follows:

[0019] 1) Descaling heat transfer model:

[0020] H d = f(V, r, R, T) (1)

[0021] Where: H d is the descaling heat transfer coefficient, W / (m 2 ·℃); V is the water flow rate, m 3 / h; r is the water pressure influence coefficient; R is the equivalent radius of the billet, m; T is the surface temperature of the rolled piece, ℃;

[0022] 2) Air-cooling heat transfer model

[0023] H a = f(T, T f ) (2)

[0024] Where: H a is the air-cooling heat transfer coefficient, W / (m 2 ·℃); T is the surface temperature of the rolled piece, ℃; T f is the ambient temperature, ℃;

[0025] 3) Roll heat transfer model:

[0026] H r = f(λ, a, t, W) (3)

[0027] Where: H r is the roll heat transfer coefficient, W / (m 2 ·℃); λ is the thermal conductivity of the rolled piece, W / (m·K); a is the thermal diffusivity of the rolled piece, m 2 / s; t is the contact time, s; W is the coefficient related to the roll cooling water;

[0028] 4) Rolling temperature rise model

[0029]

[0030] Where: q v is the rolling internal heat source intensity, W / m 3 ; K m is the metal deformation resistance, Pa; is the equivalent height of the rolled piece before deformation, mm; is the equivalent height of the rolled piece after deformation, mm; is the equivalent width of the rolled piece before deformation, mm; S is the cross-sectional area of the rolled piece, mm 2 ; η is the work-heat conversion coefficient;

[0031] 5) Water-cooling heat transfer model

[0032]

[0033] Where: H w is the water-cooling heat transfer coefficient, W / (m 2 ·℃); Q is the water volume, m3 / h; R is the radius of the wire rod, m; A, B5, B6, B7, B8, η, θ are the cooling tube parameters selected according to the radius of the rolled piece; v is the speed of the rolled piece, m / s;

[0034] 6) Cooling bed heat transfer model

[0035] H CB = f(λ, R, C p , μ, ρ, u) (6)

[0036] In the formula: H CB is the heat transfer coefficient of the cooling bed, W / (m 2 ·°C); λ is the thermal conductivity of the rolled piece, W / (m·°C); R is the radius of the rolled piece, m; C p is the specific heat of the rolled piece, J / (kg·°C); μ is the air viscosity, Pa·s; ρ is the density of the rolled piece, kg / m 3 ; u is the air flow velocity, m / s.

[0037] Optionally, the austenite microstructure evolution model of the rolling line is as follows:

[0038] 1) Austenite grain growth model during heating and holding:

[0039] The temperature of the steel billet in the heating furnace is generally higher than and belongs to the austenite microstructure. After heating and holding, the austenite will grow, and the grain size model after growth is as follows:

[0040] d = f(d0, t, T, C, Mn, Si) (7)

[0041] In the formula: d0 is the initial grain size, μm; t is the heating time, s; Q is the activation energy for grain boundary migration, J / mol; T is the temperature, °C; C, Mn, Si are the carbon content, manganese content, and silicon content of the steel billet, %.

[0042] 2) Dynamic recrystallization model

[0043] The critical strain is the condition for dynamic recrystallization to occur during hot deformation. The critical strain model is as follows:

[0044]

[0045] In the formula: is the strain rate, s -1 ; T is the temperature of the rolled piece, °C;

[0046] The dynamic recrystallization volume fraction model is as follows:

[0047]

[0048] Where: X drx is the volume fraction of dynamic recrystallization; ε is the actual strain; ε c is the critical strain; ε 0.5 is the strain when the volume fraction of dynamic recrystallization is 50%; is the strain rate, s -1 ; T is the temperature of the rolled piece, °C;

[0049] The dynamic recrystallized grain size model is as follows:

[0050]

[0051] Where: d drx is the dynamic recrystallized grain size, μm; t is the deformation time, s; d is the initial grain size of dynamic recrystallization, μm; is the strain rate, s -1 ; T is the deformation temperature, °C;

[0052] 3) Meta-dynamic recrystallization model

[0053] When dynamic recrystallization occurs during rolling, meta-dynamic recrystallization occurs in the rolling gap. The meta-dynamic recrystallization volume fraction model is as follows:

[0054]

[0055] Where: x mrx is the meta-dynamic recrystallization volume fraction; t is the rolling gap time, s; t 0.5 is the time when the meta-dynamic recrystallization volume fraction reaches 50%, s; Z is the Zener-Hollomon parameter; is the strain rate, s -1 ; T is the average temperature of the rolled piece, °C;

[0056] The meta-dynamic recrystallized grain size model is as follows:

[0057] d mrx = f(Z) (12)

[0058] Where: d mrx is the meta-dynamic recrystallized grain size, μm; Z is the Zener-Hollomon parameter;

[0059] After the meta-dynamic recrystallization is complete, the grains will grow. The meta-dynamic recrystallized grain growth model is as follows:

[0060] d MG = f(d mrx , t, t 0.5 , T) (13)

[0061] Where: d MGis the grain size after the growth of the sub-dynamic recrystallization grains, μm; d mrx is the sub-dynamic recrystallization grain size, μm; t is the rolling interval time, s; t 0.5 is the time when the sub-dynamic recrystallization volume fraction reaches 50%, s; T is the average temperature of the rolled piece, °C;

[0062] 4) Static recrystallization model

[0063] If dynamic recrystallization does not occur during rolling, static recrystallization occurs during the rolling interval. The relevant model of the static recrystallization volume fraction is as follows:

[0064]

[0065] In the formula: X srx is the static recrystallization volume fraction; t is the pass interval time, s; t 0.5 is the time when the static recrystallization volume fraction reaches 50%; d0 is the initial recrystallization grain size, μm; is the strain rate, s -1 ; ε is the strain; T is the deformation temperature, °C;

[0066] The static recrystallization grain size model is as follows:

[0067] d srx = f(ε, d0, T) (15)

[0068] In the formula: d srx is the static recrystallization grain size, μm; ε is the strain; d0 is the initial recrystallization grain size, μm; T is the deformation temperature, °C;

[0069] After the static recrystallization occurs completely, the grains will grow. The sub-dynamic recrystallization grain growth model is as follows:

[0070] d SG = f(d srx , t, t 0.5 , T) (16)

[0071] In the formula: d SG is the grain size after the growth of the static recrystallization grains, μm; d srx is the static recrystallization grain size, μm; t is the rolling interval time, s; t 0.5 is the time when the static recrystallization volume fraction reaches 50%, s; T is the average temperature of the rolled piece, °C.

[0072] Optionally, the austenite phase transformation model is as follows:

[0073] 1) Calculate the phase transformation start temperature during continuous cooling transformation using the Scheil's rule:

[0074]

[0075] Where: dt is the time step, s; t is the incubation period of each phase corresponding to this temperature, s;

[0076] 2) The phase transformation incubation period model for continuous cooling transformation is as follows:

[0077] k F = f(C, Mn, T) (18)

[0078] t F = f(k F ,T) (19)

[0079] k P = f(C, Mn, T) (20)

[0080] t P = f(k P ,T) (21)

[0081] k B = f(C, Mn, T) (22)

[0082] t B = f(k B ,T) (23)

[0083] Where: k F 、t F are the coefficient and incubation period during the transformation from austenite to ferrite, s; k P 、t P are the coefficient and incubation period during the transformation from austenite to pearlite, s; k B 、t B are the coefficient and incubation period during the transformation from austenite to bainite, s; C is the carbon content, %; Mn is the manganese content, %; T is the average cross-section temperature, °C;

[0084] 3) Calculate the maximum phase transformation amounts of ferrite and pearlite to constrain the phase transformation amount range. The specific model is as follows:

[0085]

[0086]

[0087] Where: are the maximum transformation amounts of ferrite and pearlite, %; C is the carbon content, %; T is the average cross-section temperature, °C;

[0088] 4) Calculate the volume fractions of each phase according to the CCT curve. The specific model is as follows:

[0089] XF , X P , X B , X M = f(T F , T P , t F , t P ) (26)

[0090] In the formula: X F , X P , X B , X M are the volume fractions of ferrite, pearlite, bainite, and martensite, respectively, %; T F is the starting transformation temperature of ferrite, °C; T P is the starting transformation temperature of pearlite, °C; t F is the starting transformation time of ferrite, s; t P is the starting transformation time of pearlite, s;

[0091] 5) The calculation model of pearlite lamellar spacing is as follows:

[0092] d P = f(T F , T P , t F , t P ) (27)

[0093] In the formula: d P is the pearlite lamellar spacing, nm; T F is the starting transformation temperature of ferrite, °C; T P is the starting transformation temperature of pearlite, °C; t F is the starting transformation time of ferrite, s; t P is the starting transformation time of pearlite, s;

[0094] 6) The calculation model of ferrite grain size is as follows:

[0095] d F = f(d r , r s , x F , V F , T) (28)

[0096] In the formula: d F is the final ferrite grain size, μm; d r is the austenite grain size before phase transformation, μm; r s is the residual strain; X F is the ferrite volume fraction, %; V F is the cooling rate, m / s; T is the temperature of the cooling bed or coiling temperature, °C.

[0097] Optionally, the strength model is as follows:

[0098] 1) Yield strength model

[0099] σ s = f(σ jt , σ jl , σ gr , σ wc , σ xb , σ xc ) (29)

[0100] In the formula: σ jt , σ jl , σ gr , σ wc , σ xb , σ xc are the contribution values of matrix strengthening, grain refinement strengthening, solid solution strengthening, dislocation strengthening, phase transformation strengthening, and precipitation strengthening, respectively, in MPa;

[0101] 2) Tensile strength model:

[0102] σ b = f(X F , X P , X B , X M , d F , C, Mn, Si) (30)

[0103] In the formula: X F , X P , X B , X M are the volume fractions of ferrite, pearlite, bainite, and martensite, respectively; d F is the ferrite grain size, in μm; C, Mn, Si, and P are the carbon content, manganese content, silicon content, and phosphorus content in the rolled piece, respectively, in %;

[0104] 3) Elongation model:

[0105] δ = f(d F , X F , C, Mn, Si) (31)

[0106] In the formula: δ is the elongation of the rolled piece, in %; d F is the ferrite grain size, in μm; X F is the ferrite volume fraction; C, Mn, and Si are the carbon content, manganese content, and silicon content in the rolled piece, respectively, in %;

[0107] 4) Total elongation at maximum force model:

[0108] Agt = f(XF , X P , X E , X M , d F ) (32)

[0109] Where: Agt is the total elongation at maximum force, %; X F 、X P 、X E 、X M are the volume fractions of ferrite, pearlite, bainite and martensite respectively; d F is the ferrite grain size in μm.

[0110] Optionally, the temperature range of the cooling bed on the rod is 700°C to 950°C.

[0111] Optionally, the billet type is a heated billet or a directly rolled billet.

[0112] Optionally, the calculation results are stored in an SQL database and historical records are consulted.

[0113] The beneficial effects of the present invention are:

[0114] 1) It provides an auxiliary tool for steel rolling engineers to use computers to study rolling processes online, which can be used to: optimize the hot rolling and cooling processes of steel being produced and improve the product performance qualification rate;

[0115] 2) Determine the range of hot rolling process parameters and cooling process parameters for new steel grades. Reduce the number of industrial experiments required when developing new grades;

[0116] 3) The performance prediction system of this organization has a friendly human-computer interface, the upload template is clear and simple, and the display interface is obvious and easy to understand, which is suitable for use by steel rolling engineers and rolling mill operators;

[0117] 4) Connected with industrial production, it can simulate steel rolling and predict the structure and properties of each steel billet online, reducing the randomness of results caused by defects in inspection technology;

[0118] 5) Calculation data is stored in the SQL database, making it easy for users to view historical calculation data, compare results, and identify problems;

[0119] 6) This system has a data export function to facilitate further data analysis;

[0120] 7) This system can realize batch calculation.

[0121] Other advantages, objects and features of the present invention will be set forth in part in the following description, and in part will be obvious to those skilled in the art upon examination of the following, or may be learned from the practice of the present invention. The objects and other advantages of the present invention may be realized and obtained by the following description of the specification. Description of the Drawings

[0122] In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be described in detail preferably with reference to the accompanying drawings, where:

[0123] Figure 1 is the overall block diagram of the present invention;

[0124] Figure 2 is the block diagram of the temperature evolution calculation process;

[0125] Figure 3 is the block diagram of the austenite evolution and phase transformation calculation process;

[0126] Figure 4 is the block diagram of the mechanical property calculation process;

[0127] Figure 5 is the bar template uploaded by the present invention. Detailed Embodiments

[0128] The following specific examples illustrate the embodiments of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments. Various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments only illustrate the basic concept of the present invention schematically. Without conflict, the following embodiments and the features in the embodiments can be combined with each other.

[0129] Among them, the drawings are only for illustrative purposes, showing only schematic diagrams, not physical diagrams, and should not be construed as a limitation on the present invention; in order to better illustrate the embodiments of the present invention, some components in the drawings will be omitted, enlarged or reduced, which do not represent the dimensions of the actual product; for those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0130] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms such as "upper", "lower", "left", "right", "front", "rear", etc. indicating the orientation or positional relationship, they are based on the orientation or positional relationship shown in the accompanying drawings. This is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation. Therefore, the terms describing the positional relationship in the accompanying drawings are only for illustrative purposes and should not be construed as a limitation of the present invention. For those of ordinary skill in the art, the specific meanings of the above terms can be understood according to specific circumstances.

[0131] Please refer to Figure 1 , a high-precision microstructure and property prediction method for deformed steel bars. A template with parameters such as unit, stand number, stand elongation, equipment position, roll ring diameter, water tank length, water pressure, water volume, billet size, cooling time on cooling bed, finishing rolling speed, ambient temperature, split stand, split number, chemical composition, and heating regime is uploaded to the system. Through a calculation program including a temperature heat transfer model, an austenite microstructure evolution model, an austenite phase transformation model, and a force and energy model, the temperature history and final microstructure and mechanical properties of the rolled piece are predicted, and the results are displayed.

[0132] The method flow of the present invention is as shown in Figure 2 , Figure 3 , Figure 4 shown.

[0133] The template uploaded by the present invention is as shown in Figure 5 shown.

[0134] Example 1

[0135] Taking a domestic steel plant using this system to predict the final microstructure and properties of HRB400E as an example, the specific steps are as follows:

[0136] 1. Measure the temperature of the rolled piece entering the finishing mill and the cooling bed on site, measure the final microstructure and mechanical properties of the rolled piece, and correct the structure and parameters of the temperature heat transfer model, austenite microstructure evolution model, austenite phase transformation model, and force and energy model;

[0137] 2. Before rolling, use the signals with billet chemical composition, billet size parameters, rolling schedule parameters, equipment parameters, and environmental parameters transmitted by the data platform of the steel plant to replace the uploaded template;

[0138] 3. The system backend receives the signals and transmits the signals to the calculation program;

[0139] 4. After the calculation program analyzes the data, it adopts the methods as shown in Figure 2 , Figure 3 , Figure 4The method flow shown calculates the temperature evolution process, microstructure and mechanical properties of the rolled piece;

[0140] 5. Store the calculation results in the database;

[0141] 6. The system backend retrieves the data from the database, transfers it to the front end, and displays the calculation results, as shown in Table 1.

[0142] Table 1 shows the comparison results of the predicted values and the measured values in Example 1.

[0143]

[0144]

[0145] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the present technical solution, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A high-precision microstructure and property prediction method for split ribbed bars, characterized in that: The method includes the following steps: 1) The high-precision microstructure and property prediction system for deformed steel bars is a B / S architecture system, which collects temperature, microstructure and property data on site and corrects the model structure and parameters in the calculation program; 2) Upload a template with mill group, stand number, stand elongation, equipment position, roll ring diameter, water tank length, water pressure, water volume, billet size parameters, cooling bed cooling time, finishing rolling speed, ambient temperature, splitting stand, splitting number, chemical composition and heating regime at the front end of the interface; 3) The processing backend receives the request from the front-end interface and forwards the calculation request to the calculation program; 4) After receiving the request, the calculation program parses the template data and uses the finite difference and heat transfer model to simulate the temperature evolution process of the entire rolling and cooling process, specifically including descaling heat transfer model, air cooling heat transfer model, roll heat transfer model, rolling temperature rise model, water cooling heat transfer model and cooling bed heat transfer model; 5) The calculation program uses the austenite microstructure evolution model on the rolling line to simulate the recrystallization and grain growth processes during and after rolling, specifically including critical strain model, dynamic recrystallization model, meta-dynamic recrystallization model and static recrystallization model; 6) The calculation program uses the austenite phase transformation model to simulate the microstructure evolution process of the rolled piece on the cooling bed, specifically including incubation period model of each phase, CCT curve, maximum transformation amount model of ferrite and pearlite, volume fraction model of each phase, pearlite lamellar spacing model and ferrite grain size model; 7) The calculation program uses the force and energy model to calculate the mechanical properties of the rolled piece when it leaves the cooling bed, specifically including yield strength model, tensile strength model, elongation model and total elongation at maximum force model; 8) The calculation program passes the calculation results into the database according to the agreed data structure, and then the processing backend retrieves the data and passes it to the front end for display on the result display interface; The blank type of the billet is heating blank or direct rolling blank.

2. A high-precision microstructure and property prediction method for split ribbed steel bars according to claim 1, characterized in that: The heat transfer model is as follows: 1) Descaling heat transfer model: H d = f(V, r, R, T b ) (1) Where: H d is the descaling heat transfer coefficient, with the unit of W / (m 2 ·°C); V is the water flow rate, with the unit of m 3 / h; r is the water pressure influence coefficient; R is the equivalent radius of the billet, with the unit of m; T b is the surface temperature of the rolled piece, with the unit of °C; 2) Air cooling heat transfer model H a = f(T b , T f ) (2) Where: H a is the air-cooled heat transfer coefficient, with the unit of W / (m 2 ·°C); T b is the surface temperature of the rolled piece, with the unit of °C; T f is the ambient temperature, with the unit of °C. 3) Roll heat transfer model: H r = f(λ, a, t, W) (3) Where: H r is the heat transfer coefficient of the roll, with the unit of W / (m 2 ·°C); λ is the thermal conductivity of the rolled piece, with the unit of W / (m·K); a is the thermal diffusivity of the rolled piece, with the unit of m 2 / s; t is the contact time, with the unit of s; W is the coefficient related to roll cooling water; 4) Rolling temperature rise model where: q v is the intensity of the internal heat source during rolling, with the unit of W / m 3 ; K m is the metal deformation resistance, with the unit of Pa; is the equivalent height of the rolled piece before deformation, with the unit of mm; is the equivalent height of the rolled piece after deformation, with the unit of mm; is the equivalent width of the rolled piece before deformation, with the unit of mm; S is the cross-sectional area of the rolled piece, with the unit of mm 2 ; η is the work-heat conversion coefficient; 5) Water cooling heat transfer model Where: H w is the water-cooling heat transfer coefficient, with the unit of W / (m 2 ·°C); Q is the water volume, with the unit of m 3 / h; R X is the wire radius, with the unit of m; A, B5, B6, B7, B8, η, θ are the cooling tube parameters selected according to the rolled piece radius; v is the rolled piece speed, with the unit of m / s; 6) Cooling bed heat transfer model H CB = f(λ, R Z , C P , μ, ρ, u) (6) Where: H CB is the heat transfer coefficient of the cooling bed, with the unit of W / (m 2 ·°C); λ is the thermal conductivity of the rolled piece, with the unit of W / (m·°C); R Z is the radius of the rolled piece, with the unit of m; C p is the specific heat of the rolled piece, with the unit of J / (kg·°C); μ is the air viscosity, with the unit of Pa·s; ρ is the density of the rolled piece, with the unit of kg / m 3 ; u is the air flow velocity, with the unit of m / s.

3. A high-precision microstructure and property prediction method for split ribbed steel bars according to claim 1, characterized in that: The austenite microstructure evolution model on the rolling line is as follows: 1) Austenite grain growth model during heating and holding: The temperature of the steel billet in the heating furnace is generally higher than It belongs to the austenite structure. After heating and heat preservation treatment, the austenite will grow. The grain size model after growth is as follows: d = f(d0, t1, T, C, Mn, Si) (7) In the formula: d0 is the initial grain size, with the unit of μm; t1 is the heating time, with the unit of s; T is the temperature of the rolled piece, with the unit of °C; C, Mn, Si are the carbon content, manganese content and silicon content of the steel billet, with the unit of %; 2) Dynamic recrystallization model The critical strain is the condition for dynamic recrystallization to occur during hot deformation. The critical strain model is as follows: In the formula: is the strain rate, with the unit of s -1 ; Dynamic recrystallization volume fraction model is as follows: where: X drx is the volume fraction of dynamic recrystallization; ε is the actual strain; ε c is the critical strain; ε 0.5 is the strain when the volume fraction of dynamic recrystallization is 50%; Dynamic recrystallization grain size model is as follows: where: d drx is the dynamic recrystallization grain size in μm; t2 is the deformation time in s; d is the initial dynamic recrystallization grain size in μm; T' is the deformation temperature in °C; 3) Meta-dynamic recrystallization model If dynamic recrystallization occurs during rolling, meta-dynamic recrystallization occurs during the rolling gap. The meta-dynamic recrystallization volume fraction model is as follows: Where: X mrx is the volume fraction of sub-dynamic recrystallization; t3 is the rolling interval time, in s; t 0.5 is the time when the volume fraction of sub-dynamic recrystallization reaches 50%, in s; Z is the Zener-Hollomon parameter; is the average temperature of the rolled piece, in °C; Meta-dynamic recrystallization grain size model is as follows: d mrx = f(Z) (12) where: d mrx is the sub-dynamic recrystallized grain size, with the unit of μm; Z is the Zener-Hollomon parameter; After meta-dynamic recrystallization occurs completely, the grains will grow. The meta-dynamic recrystallization grain growth model is as follows: where: d MG is the grain size after the growth of the sub-dynamically recrystallized grains, with the unit of μm; d mrx is the sub-dynamically recrystallized grain size, with the unit of μm; 4) Static recrystallization model When dynamic recrystallization does not occur during rolling, static recrystallization occurs in the rolling gap. The relevant model for the volume fraction of static recrystallization is as follows: where: X srx is the volume fraction of static recrystallization; t4 is the inter-pass interval time in s; d0 is the initial grain size of recrystallization in μm; ε is the strain; The model for the grain size of static recrystallization is as follows: d srx = f(ε, d0, T') (15) where: d srx is the static recrystallized grain size in μm; After static recrystallization is complete, grain growth occurs. The model for the grain growth of meta-dynamic recrystallization is as follows: where: d SG is the grain size after the static recrystallized grains grow up, with the unit of μm; d srx is the static recrystallized grain size, with the unit of μm; t3 is the rolling interval time, with the unit of s.

4. A high-precision microstructure and property prediction method for split ribbed steel bars according to claim 1, characterized in that: The austenite phase transformation model is as follows: 1) Use the Scheil's rule to calculate the starting transformation temperature during continuous cooling transformation: Where: dt is the time step, in s; t' is the incubation period of each phase corresponding to this temperature, in s; 2) The model for the incubation period of continuous cooling transformation is as follows: Where: k F , t F are respectively the coefficient and incubation period during the transformation of austenite to ferrite, with the unit of s; k P , k P are respectively the coefficient and incubation period during the transformation of austenite to pearlite, with the unit of s; k B , t B are respectively the coefficient and incubation period during the transformation of austenite to bainite, with the unit of s; C is the carbon content, with the unit of %; Mn is the manganese content, with the unit of %; is the cross-section average temperature, with the unit of °C; 3) Calculate the maximum phase change amounts of ferrite and pearlite to constrain the range of phase change amounts. The specific model is as follows: Wherein: are respectively the maximum transformation amounts of ferrite and pearlite, with the unit of %; C is the carbon content, with the unit of %; is the average cross-section temperature, with the unit of °C; 4) Calculate the volume fraction of each phase according to the CCT curve. The specific model is as follows: X F , X P , X B , X M = f(T F , T P , t F , t P ) (26) Where: X F , X P , X B , X M are the volume fractions of ferrite, pearlite, bainite, and martensite, respectively, with the unit of %; T F is the starting transformation temperature of ferrite, with the unit of °C; T P is the starting transformation temperature of pearlite, with the unit of °C; t F is the starting transformation time of ferrite, with the unit of s; t P is the starting transformation time of pearlite, with the unit of s; 5) The model for calculating the pearlite lamellar spacing is as follows: d P = f(T F , T P , t F , t P ) (27) where: d P is the pearlite lamellar spacing, in nm; T F is the ferrite start transformation temperature, in °C; T P is the pearlite start transformation temperature, in °C; t F is the ferrite start transformation time, in s; t P is the pearlite start transformation time, in s; 6) The model for calculating the ferrite grain size is as follows: d F = f(d r , r s , X F , V F , T X ) (28) where: d F is the final ferrite grain size, in μm; d r is the austenite grain size before phase transformation, in μm; r s is the residual strain; X F is the ferrite volume fraction, in %; V F is the cooling rate, in m / s; T X is the temperature of the cooling bed or coiling, in °C.

5. A high-precision microstructure and property prediction method for split ribbed bars according to claim 1, characterized in that: The force and energy model is as follows: 1) Yield strength model σ s = f(σ jt , σ jl , σ gr , σ wc , σ xb , σ xc ) (29) Where: σ jt , σ jl , σ gr , σ wc , σ xb , σ xc are the matrix strengthening contribution value, grain refinement contribution value, solid solution strengthening contribution value, dislocation strengthening contribution value, phase transformation strengthening contribution value, and precipitation strengthening contribution value, respectively, in MPa; 2) Tensile strength model: σ b = f(X F ,X P ,X B ,X M ,d F ,C, Mn, Si) (30) Where: X F , X P , X B , X M are the volume fractions of ferrite, pearlite, bainite and martensite respectively; d F is the ferrite grain size, with the unit of μm; C, Mn and Si are the carbon content, manganese content and silicon content in the rolled piece respectively, with the unit of %; 3) Elongation model: δ = f(d F , X F , C, Mn, Si) (31) where: δ is the elongation rate of the rolled piece, in %; d F is the ferrite grain size, in μm; X F is the ferrite volume fraction; C, Mn, and Si are the carbon content, manganese content, and silicon content in the rolled piece, respectively, in %; 4) Total elongation at maximum force model: Agt = f(X F , X P , X B , X M , d F )(32) Where: Agt is the total elongation at maximum force, in %; X F , X P , X B , X M are the volume fractions of ferrite, pearlite, bainite, and martensite, respectively; d F is the ferrite grain size, in μm.

6. A high-precision microstructure and property prediction method for split ribbed steel bars according to claim 1, characterized in that: The temperature range of the cooling bed on the bar is 700°C to 950°C.

7. A high-precision microstructure and property prediction method for split ribbed steel bars according to claim 1, characterized in that: The calculation results are stored in the SQL database to consult the historical records.

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