Thermoplasticity prediction model and method, electronic equipment or storage medium

Abstract

The invention provides a thermoplasticity prediction model and method, electronic equipment or a storage medium, and belongs to the technical field of metal material continuous casting, and the thermoplasticity prediction model and method comprises the steps that the low-alloy high-strength steel component content, the gamma grain average size and the austenite complete forming temperature are determined, and an A value is calculated based on the parameters; the strain rate is determined, and Zmin, TZmin, TZ50 and TDB are determined according to temperature fluctuation delta T; the area reduction threshold value of the low-alloy high-strength steel is determined, and the first temperature corresponding to the threshold value is calculated based on the Zmin, the TZmin, the TZ50 and the TDB; and the continuous casting process is adjusted, so that the temperature of an outlet of the straightening section is higher than the first temperature and smaller than TDB. The third brittleness upper limit temperature zone model when austenite grains on the surface layer of the casting blank are coarse is obtained through experimental analysis of separation and redissolution behaviors of carbonitride particles, growth behaviors of austenite grains and a thermoplastic curve of the low-alloy high-strength steel casting blank at different temperatures.

Description

Technical Field

[0001] The invention belongs to the technical field of continuous casting of metal materials, and in particular relates to a thermoplasticity prediction model, method, electronic equipment or storage medium. Background Art

[0002] With the increasing demand for both strength and toughness of hull materials in modern shipbuilding, high-strength ship plate steel is widely used in key structural parts of shipbuilding due to its excellent mechanical properties and good welding performance. Usually, as a low-carbon microalloyed steel, this type of steel often has a carbon content near the sub-peritectic or peritectic point. Due to its shrinkage behavior during solidification and phase transformation, the billet shell tends to be concave or the austenite is fully formed at a high temperature. The austenite grains on the surface of the continuous casting billet are often very coarse. When it contains microalloying elements such as Nb, Ti, and Al, the precipitation behavior of grain boundary carbonitrides during continuous casting is also particularly prominent, which is more likely to induce transverse crack defects on the surface or corners of the continuous casting billet, which is the most prominent quality problem in the continuous casting process of microalloyed steel. At the same time, this type of steel is also very prone to cracks in the heating furnace and rough rolling process using the traditional hot charging and hot delivery process, which seriously reduces the homogeneity of the product. It is still one of the technical difficulties in the production of steel enterprises.

[0003] In the range of 600℃-900℃, the precipitation size of carbonitrides and the third brittle zone caused by the precipitation of pre-eutectoid film ferrite along the austenite grain boundary during the γ→α transformation of microalloyed steel are the root causes of such defects. The critical precipitation size of carbonitrides is 40nm, and the smaller the size of the precipitation phase, the higher the crack rate; at the same time, the fine carbonitrides in the matrix will enhance the grain boundary stress, which is conducive to grain boundary sliding, and it is easier to expand intergranular cracks when precipitating at the grain boundary. The smaller the size of the precipitation phase, the stronger the pinning force, and the higher the crack sensitivity.

[0004] At present, the common method for hot plasticity experiments of ingots is to obtain an austenite grain size similar to that of the actual ingot surface by high-temperature hot stretching (solution treatment at 1350℃ for 10 minutes), so as to determine the third brittle temperature zone; however, previous studies have shown that the austenite grain size is often difficult to reach 1mm when solution treatment is performed at 1350℃ for 10 minutes, which greatly reduces the reliability of the results and makes it impossible to accurately determine the "cold" or "hot" process scheme for continuous casting. At the same time, under specific equipment conditions and current continuous casting processes, the precipitation type and sequence of carbonitrides (containing Ti, Nb and Al) at different temperatures for low-alloy high-strength steels, as well as the particle size and its specific influence mechanism on the tendency of surface cracks are still unclear. Summary of the invention

[0005] In order to solve the above problems, the present invention provides a thermoplasticity prediction model, method, electronic device or storage medium. The present invention experimentally analyzes the precipitation and dissolution behavior of carbonitride particles, the austenite grain growth behavior and the thermoplasticity curves under different austenite grain sizes of low-alloy high-strength steel ingots at different temperatures, and obtains a reliable calculation model that takes into account the third brittle upper temperature limit zone when the austenite grains on the surface of the actual ingot are coarse. Based on this, Procast calculates and verifies the corner temperature of low-alloy high-strength steel under the current continuous casting conditions, in order to provide a theoretical basis and technical support for the implementation of the "hot running" process for continuous casting of this type of steel and then reduce the tendency of continuous casting surface cracks.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0007] On the one hand, the present invention provides a thermoplasticity prediction model, which is used for low-alloy high-strength steel, in which, by mass percentage, C: 0.05%-0.16%, Nb: 0.01%-0.03%, Ti: 0.010%-0.015%, Al: 0.01%-0.05%, N: 0.004%-0.006%; the thermoplasticity prediction model is constructed by the following method, including: obtaining training data, and constructing a relationship between the amount of TiN precipitation and the nitrogen content, titanium content and actual temperature of the low-alloy high-strength steel based on the training data; calculating the A value based on the average size of γ grains and the amount of TiN precipitation; constructing a minimum value Z of cross-sectional shrinkage based on the training data. min , the temperature T corresponding to the minimum cross-sectional shrinkage Zmin , the temperature T corresponding to the section shrinkage rate drops to 50% Z50 , Thermoplasticity curve extrapolation starting point T DB With the A value, strain rate , Austenite complete formation temperature T γ , the relationship between the component content of low-alloy high-strength steel and the temperature fluctuation ΔT; the component content of low-alloy high-strength steel includes the percentage of aluminum in the steel, the percentage of nitrogen in the steel, the percentage of titanium in the steel and the percentage of niobium in the steel.

[0008] On the other hand, the present invention provides a method for reducing the tendency of cracks to form on the surface of the ingot based on the above-mentioned thermoplasticity prediction model, comprising: determining the content of low alloy high strength steel components, the average size of γ grains , calculate the A value; determine the strain rate , austenite complete formation temperature T γ , the temperature fluctuation ΔT determines Z min , T Zmin , T Z50 and T DB; Determine the cross-sectional reduction rate threshold of the low alloy high strength steel, and based on the Z min , T Zmin , T Z50 and T DB Calculate the first temperature corresponding to the threshold; adjust the continuous casting process so that the temperature at the outlet of the straightening section is higher than the first temperature and lower than T DB That's it.

[0009] Furthermore, the average size of the γ grains is It is determined based on the surface grain size of the continuous casting slab of the low alloy high strength steel.

[0010] Furthermore, the austenite complete formation temperature T γ Determined by DSC test.

[0011] Furthermore, the threshold is 35%-45%.

[0012] Furthermore, the threshold is 40%, and the calculation formula of the first temperature T0 is: ; ; Where CR is the cooling rate.

[0013] Furthermore, the temperature T1 at the outlet of the straightening section is: .

[0014] Furthermore, the continuous casting process includes secondary cooling intensity and / or continuous casting speed.

[0015] The present invention also provides an electronic device, comprising: a control center, the control center including one or more processors; a memory, on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned thermoplasticity prediction model or the above-mentioned method.

[0016] The present invention also provides a storage medium storing one or more programs. When the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned thermoplasticity prediction model or the above-mentioned method.

[0017] The beneficial effects of the technical solution provided by the embodiment of the present invention include: the present invention establishes a thermoplastic model for low-alloy high-strength steel, that is, in terms of mass percentage, C: 0.05%-0.16%, Nb: 0.01%-0.03%, Ti: 0.010%-0.015%, Al: 0.01%-0.05%, N: 0.004%-0.006%, and the Z can be reliably calculated by the model.min , T Zmin , T Z50 , T DB In order to calculate the upper limit temperature of the brittle zone corresponding to the threshold value of the cross-sectional shrinkage rate, it is helpful to guide the actual production in the continuous casting process and avoid defects such as cracks in the continuous casting billet. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0019] Figure 1 The temperature variation of the corners of AH36 under different continuous casting conditions provided in Example 2 of the present invention; Figure 2 The SEM images of samples taken at different positions in the thickness direction of the continuous casting slab prepared under the current continuous casting conditions of AH36 steel provided in Example 2 of the present invention, wherein a, b, c and d are four different sampling positions; Figure 3 These are SEM images of samples taken at different positions in the thickness direction of the continuously cast ingot prepared under the improved continuous casting conditions of AH36 steel provided in Example 2 of the present invention, wherein a and b adopt the first process, and c and d adopt the second process. DETAILED DESCRIPTION

[0020] The present invention is further described in detail below by specific embodiments. However, it will be appreciated by those skilled in the art that the following examples are only used to illustrate the present invention and should not be considered as limiting the scope of the present invention. If specific techniques or conditions are not specified in the examples, they are carried out according to the techniques or conditions described in the literature in this area or according to the product instructions. If the manufacturer of the reagents or instruments used is not specified, they are all conventional products that can be obtained commercially.

[0021] As used herein, the words "comprises," "including," "having," or any other variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that includes listed elements is not necessarily limited to those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. The singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise.

[0022] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meanings as those generally understood by those skilled in the art. In addition to the specific methods, equipment, and materials used in the embodiments, any methods, equipment, and materials of the prior art that are similar or equivalent to the methods, equipment, and materials in the embodiments of the present invention may be used to implement the present invention, based on the prior art mastery of those skilled in the art and the description of the present invention.

[0023] Example 1

[0024] An embodiment of the present invention provides a thermoplasticity prediction model, which is used for low-alloy high-strength steel. In the low-alloy high-strength steel, by mass percentage, C: 0.05%-0.16%, Nb: 0.01%-0.03%, Ti: 0.010%-0.015%, Al: 0.01%-0.05%, N: 0.004%-0.006%; the thermoplasticity prediction model is constructed by the following method, including: obtaining training data, and constructing a relationship between the amount of TiN precipitation and the nitrogen content, titanium content and actual temperature of the low-alloy high-strength steel based on the training data; calculating the A value based on the average size of γ grains and the amount of TiN precipitation; constructing a minimum value Z of cross-sectional shrinkage based on the training data. min , the temperature T corresponding to the minimum cross-sectional shrinkage Zmin , the temperature T corresponding to the section shrinkage rate drops to 50% Z50 , Thermoplasticity curve extrapolation starting point T DB With the A value, strain rate , Austenite complete formation temperature T γ , the relationship between the component content of low-alloy high-strength steel and the temperature fluctuation ΔT; the component content of low-alloy high-strength steel includes the percentage of aluminum in the steel, the percentage of nitrogen in the steel, the percentage of titanium in the steel and the percentage of niobium in the steel.

[0025] Specifically, the calculation method of the A value is: ; in is the average size of γ grains, µm; P Ti is the amount of TiN precipitation, %.

[0026] The thermoplastic prediction model constructed using the above construction method is: ; ; ; ; ; ; Among them, Z min is the minimum value of section shrinkage, %; is the strain rate, s -1 ; is the percentage of aluminum in steel, %; is the percentage of nitrogen in steel, %; is the percentage of titanium in steel, %; is the percentage of niobium in steel, %; ΔT is the temperature fluctuation, ℃; T Zmin is the temperature corresponding to the minimum section shrinkage, ℃; T Z50 T is the temperature when the cross-sectional shrinkage rate drops to 50%, ℃; DB is the starting point of thermoplasticity curve extrapolation, ℃; is the average size of γ grains, µm; P Ti is the amount of TiN precipitation, %; T γ is the temperature at which austenite is completely formed, ℃; T is the actual temperature, ℃.

[0027] The present invention establishes a thermoplastic model for low alloy high strength steel, that is, in terms of mass percentage, C: 0.05%-0.16%, Nb: 0.01%-0.03%, Ti: 0.010%-0.015%, Al: 0.01%-0.05%, N: 0.004%-0.006%, through which Z can be reliably calculated. min , T Zmin , T Z50 , T DB In order to calculate the upper limit temperature of the brittle zone corresponding to the threshold value of the cross-sectional shrinkage rate, it is helpful to guide the actual production in the continuous casting process and avoid defects such as cracks in the continuous casting billet.

[0028] This application aims at a specific low-alloy high-strength steel, combines the grain growth model with the cross-sectional shrinkage model, and develops a thermoplasticity prediction model under the condition of coarse austenite grain size. The technical solution proposed in the present invention is suitable for austenite grain size of 800μm-1200μm.

[0029] Specifically, the section reduction rate RA is calculated by the following formula: ; Where S0 and S are the initial area and fracture area of ​​the specimen, respectively.

[0030] The austenite is completely formed at temperature T γ, the effect of the intermediate value A and the TiN particles precipitated near the solidification temperature on the grain growth (i.e., the grain size), combined with the intermediate value A, the steel grade component content, the strain rate, and the temperature fluctuation, a quantitative calculation method for the characteristic value of the thermoplastic curve in the third brittle zone of the low-alloy high-strength steel ingot of this application is constructed to calculate Z min , T Zmin , T Z50 , T DB , in order to provide guidance for subsequent actual continuous casting process.

[0031] Specifically, a confocal laser scanning microscope was used to simulate the austenite grain growth behavior during the continuous casting of microalloyed steel, and the effects of the intermediate value A, the starting temperature of austenite grain growth and the steel composition on the behavior were analyzed. An austenite grain size prediction model was proposed, which is suitable for the cooling rate of 0.2℃ / s-5.0℃ / s and the specific composition and content range of low-alloy steel proposed for this application. At the same time, combined with the above-mentioned austenite grain size prediction model, the tensile test section shrinkage function (RA) was embedded, and the critical strain was derived using the RA value, and compared and analyzed with the dynamic strain calculated by the thermomechanical coupling model of the ingot surface. The transient conditions were processed by the integral method, and the ratio of the dynamic strain to the corresponding critical strain within the time increment was weighted and accumulated. When the integral value reached the threshold, it was determined that a crack had occurred.

[0032] The embodiment of the present invention further provides a method for reducing the tendency of surface crack generation based on the above-mentioned thermoplasticity prediction model, comprising: S1 determines the composition content of low alloy high strength steel and the average size of γ grains , calculate the A value based on the above parameters; S2 determines the strain rate , austenite complete formation temperature T γ， Temperature fluctuation ΔT determines Z min , T Zmin , T Z50 and T DB ; S3 determines the threshold value of the cross-sectional reduction rate of the low alloy high strength steel, and based on the Z min , T Zmin , T Z50 and T DB Calculating a first temperature corresponding to the threshold; S4 adjusts the continuous casting process so that the temperature at the outlet of the straightening section is higher than the first temperature and lower than T DB That's it.

[0033] Specifically, in step S1, the composition of the low alloy high strength steel can be obtained based on actual measurement, and the average size of the γ grains Based on the surface grains of the continuous casting slab, the surface layer is the average grain size within 15mm along the thickness direction of the continuous casting slab, and the austenite complete formation temperature T γ Determined based on DSC test.

[0034] Strain rate in step S2 Combined with the calculation based on the equivalent strain rate during continuous casting, the temperature fluctuation value is set based on the on-site working conditions. By bringing the above parameters into the thermoplastic model of this application, Z min , T Zmin , T Z50 and T DB .

[0035] In step S3, the cross-sectional shrinkage threshold of the low-alloy high-strength steel is determined. The cross-sectional shrinkage threshold is measured by a Gleeble 3800 thermal simulation tester using a high-temperature tensile test or is set based on an empirical value. When the thermal simulation tester is used for measurement, the thermoplasticity curves at different solution temperatures are obtained, and the cross-sectional shrinkage threshold is obtained based on the thermoplasticity curves, and the calculated Z is used to determine the cross-sectional shrinkage threshold. min , T Zmin , T Z50 and T DB A first temperature T0 corresponding to the threshold is calculated.

[0036] For the low alloy high strength steel proposed in this application, the threshold value is 35%-45%, preferably 40%.

[0037] When the threshold is 40%, the calculation formula of the first temperature T0 is: ; ; Where CR is the cooling rate.

[0038] The first temperature T0 when the threshold is 40% can be accurately calculated by the above calculation formula.

[0039] It should be pointed out that the generation of cracks is relative, not absolute, that is, the probability of crack generation can be greatly reduced at a temperature higher than a certain first temperature.

[0040] In order to facilitate calculation, the present invention also proposes another method for calculating the first temperature T0, specifically: ; Where Z is the threshold value of cross-sectional shrinkage; is the correction factor, the value is 2.24-2.25; It means the section shrinkage is 50%; Indicates the minimum section shrinkage.

[0041] The above two calculation methods can reduce the probability of crack generation during the continuous casting of low-alloy high-strength steel, so that the probability of crack generation is less than 5 / 10 meters, and the size of the crack is not greater than 5 mm; another first temperature T0 calculation method proposed in the present invention can calculate the temperature corresponding to other cross-sectional shrinkage rates, and the calculation result is simpler and faster, and has a wide range of applications.

[0042] In step S4, the continuous casting process is adjusted by adjusting the secondary cooling intensity and / or the continuous casting speed so that the temperature at the outlet of the straightening section is higher than the first temperature and lower than T DB That is, the temperature T1 at the outlet of the straightening section is: .

[0043] An embodiment of the present application also provides an electronic device, such as a control center, comprising one or more processors; a memory on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the above-mentioned control method.

[0044] The electronic device in the present application may include one or more of the following components: a memory, a processor, and one or more applications, wherein the one or more applications may be stored in the memory and configured to be executed by one or more processors, and the one or more programs are configured to execute the method described in the aforementioned method embodiment.

[0045] The memory may include a random access memory (RAM) or a read-only memory (ROM). The memory may be used to store instructions, programs, codes, code sets or instruction sets. The memory may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for implementing at least one function (such as a histogram equalization function, etc.), instructions for implementing the above-mentioned various method embodiments, etc. The data storage area may also store data created by the electronic device during use (such as image matrix data, etc.).

[0046] The processor may include one or more processing cores. The processor uses various interfaces and lines to connect the various parts of the entire electronic device, and executes various functions of the electronic device and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory, and calling data stored in the memory. Optionally, the processor can be implemented in at least one hardware form of digital signal processing (DSP), field-programmable gate array (FPGA), and programmable logic array (PLA). The processor can integrate one or a combination of a central processing unit (CPU) and a modem. Among them, the CPU mainly processes the operating system and application programs; the modem is used to process wireless communications. It is understandable that the above-mentioned modem may not be integrated into the processor, but may be implemented separately through a communication chip.

[0047] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0048] Example 2

[0049] The embodiment of the present invention is directed to the continuous casting process of AH36 steel, and combined with the thermoplasticity prediction model proposed by the present invention, the tendency of cracks to form on the surface of the casting is reduced, including: S1 determines the composition content of low alloy high strength steel and the average size of γ grains , calculate the A value.

[0050] The specific composition of the AH36 steel is shown in Table 1.

[0051] Table 1 Composition of AH36 steel

[0052] Average γ grain size is 1000µm, and the A value is calculated based on the above parameters; S2 strain rate Take 3×10 -4 s-1 , austenite complete formation temperature T γ is 1448℃, the temperature fluctuation ΔT is 50℃, and Z is calculated min =23.01%, T Zmin =761.92℃, T Z50 =988℃, T DB =1006℃; S3 determines that the threshold value of the cross-sectional reduction rate of the low alloy high strength steel is 40%, and based on the Z min , T Zmin , T Z50 and T DB A first temperature corresponding to the threshold is calculated.

[0053] When using Formula, calculate T Z40 It is 970℃.

[0054] When using Calculation shows that T0 is 970°C.

[0055] S4 adjusts the continuous casting process so that the temperature at the outlet of the straightening section is higher than the first temperature and lower than T DB This can be achieved by setting the temperature at the outlet of the straightening section to be higher than 970°C.

[0056] In order to characterize whether the prepared slab has cracks, samples are taken from cross sections at multiple different longitudinal positions of the continuous casting slab and analyzed. The longitudinal direction of the continuous casting slab is the moving direction of the continuous casting slab.

[0057] The temperature field of AH36 steel under the current continuous casting condition was calculated and verified by Procast software. The calculated working condition parameters and the secondary cooling intensity (SCI) distribution are shown in Table 3 and Table 4 respectively. The temperature changes of the corners at different distances from the meniscus are shown in Table 3. Figure 1 As shown. Figure 1 It can be seen that the corner temperature of the slab at the exit of the straightening section (11.63m from the meniscus) is about 956℃ under the current cooling intensity. There are cracks in the thickness direction of the produced slab, such as Figure 2 As shown, the depth exceeds 10mm.

[0058] Table 3 Current continuous casting process parameters of AH36

[0059] Table 4 Current cooling intensity distribution of the secondary cooling zone

[0060] It can be seen that the outlet temperature of the straightening section of the current process is lower than the upper limit temperature of the third brittle temperature of 970°C. At the same time, temperature measurements were carried out at different positions of the billet crystallizer outlet and the bending section, which is consistent with the model calculation.

[0061] The embodiment of the present invention increases the temperature at the exit of the straightening section through two adjustment processes: The first process: based on the current 1.7m / min pulling speed and secondary cooling strength (SCI), by reducing the secondary cooling strength by 30%;

[0062] The second process: increase the pulling speed to 2.0m / min.

[0063] from Figure 1 It can be seen that the temperature of the ingot at the outlet of the straightening section reaches 977°C or 979°C, both higher than the third brittle upper limit temperature of 970°C, which improves the thermoplasticity of the ingot at the corners of the bending and straightening section, thereby avoiding the generation of cracks at the corners.

[0064] Through testing, within the longitudinal length of 10m, 50 positions were uniformly sampled and cracks in the thickness direction were observed. No cracks were found. Figure 3 shown.

[0065] The above description is only an embodiment of the present application and is not intended to limit the protection scope of the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A thermoplasticity prediction model, the thermoplasticity prediction model is used for low alloy high strength steel, characterized in that: In the low alloy high strength steel, by mass percentage, C: 0.05%-0.16%, Nb: 0.01%-0.03%, Ti: 0.010%-0.015%, Al: 0.01%-0.05%, N: 0.004%-0.006%; The thermoplastic prediction model is constructed by the following method, including: Acquire training data, and construct a relationship between the amount of TiN precipitation and the nitrogen content, titanium content and actual temperature of the low alloy high strength steel based on the training data; The A value is calculated based on the average size of γ grains and the amount of TiN precipitation; Based on the training data, construct the minimum value Z of the cross-sectional shrinkage rate min , the temperature T corresponding to the minimum cross-sectional shrinkage Zmin , the temperature T corresponding to the section shrinkage rate drops to 50% Z50 , Thermoplasticity curve extrapolation starting point T DB With the A value, strain rate , Austenite complete formation temperature T γ , the relationship between the composition content of low alloy high strength steel and the temperature fluctuation ΔT; The component contents of the low alloy high strength steel include the percentage of aluminum in the steel, the percentage of nitrogen in the steel, the percentage of titanium in the steel and the percentage of niobium in the steel.

2. A method for reducing the tendency of crack formation on the surface of a casting billet based on the thermoplasticity prediction model according to claim 1, characterized in that: include: Determine the composition content of low alloy high strength steel and the average size of γ grains , calculate the A value; Determine the strain rate , austenite complete formation temperature T γ， Temperature fluctuation ΔT determines Z min , T Zmin , T Z50 and T DB ; Determine the threshold value of the cross-sectional reduction rate of the low alloy high strength steel, and based on the Z min , T Zmin , T Z50 and T DB Calculating a first temperature corresponding to the threshold; Adjust the continuous casting process so that the temperature at the outlet of the straightening section is higher than the first temperature and lower than T DB That's it.

3. The method according to claim 2, characterized in that The average size of the γ grains It is determined based on the surface grain size of the continuous casting slab of the low alloy high strength steel.

4. The method according to claim 2, characterized in that: The austenite complete formation temperature T γ Determined by DSC test.

5. The method according to claim 2, characterized in that: The threshold is 35%-45%.

6. The method according to claim 5, characterized in that The threshold is 40%; The calculation formula of the first temperature T0 is: ； ； Where CR is the cooling rate.

7. The method according to claim 6, characterized in that The temperature T1 at the outlet of the straightening section is: .

8. The method according to claim 2, characterized in that: The continuous casting process includes secondary cooling intensity and / or continuous casting speed.

9. An electronic device, characterized in that: include: A control center, the control center comprising one or more processors; A memory having one or more programs stored thereon, which, when the one or more programs are executed by the one or more processors, enables the one or more processors to implement the thermoplastic prediction model of claim 1 or any method of claims 2-8.

10. A storage medium, characterized in that: The storage medium stores one or more programs, and when the one or more programs are executed by the one or more processors, the one or more processors implement the thermoplastic prediction model of claim 1 or any method of claims 2-8.

Images