A thermoplastic prediction model, method, electronic device, or storage medium

Abstract

The present invention provides a thermoplasticity prediction model, method, electronic device or storage medium, belonging to the technical field of continuous casting of metal materials, including: determining the component content of low-alloy high-strength steel, the average size of γ grains, the complete austenite formation temperature, and calculating the A value based on the above parameters; determining the strain rate, the temperature fluctuation ΔT to determine Z min , T Zmin , T Z50 and T DB ; determining the reduction of area 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 less than T DB That's it. Through experimental analysis of the precipitation and re-solution behavior of carbonitride particles, the austenite grain growth behavior and the thermoplasticity curve of low-alloy high-strength steel billets at different temperatures, a model for the upper limit temperature range of the third brittleness when the austenite grains on the surface of the billet are coarse is obtained.

Description

Technical Field

[0001] The present invention belongs to the technical field of continuous casting of metal materials, and particularly relates to a hot plasticity prediction model, method, electronic device or storage medium. Background Art

[0002] With the increasing demand for the 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. Generally, such steel grades are low-carbon microalloyed steels, and the carbon content is often near the hypoeutectic or eutectic point. Due to the shrinkage behavior during the solidification and phase transformation processes, the tendency of the billet shell to sag is large or the complete formation temperature of austenite is high, and the austenite grains on the surface layer of the continuous casting billet are often very coarse; when containing microalloying elements such as Nb, Ti, Al, etc., the precipitation behavior of grain boundary carbonitrides during the continuous casting process is particularly prominent, and it is more likely to induce surface or corner transverse crack defects of the continuous casting billet, which is the most prominent quality problem in the continuous casting process of microalloyed steels. At the same time, when such steel is processed by the traditional hot charging and hot delivery process, cracks are also very likely to occur in the heating furnace and rough rolling process, seriously reducing the product homogeneity, and it is still one of the technical problems in the production of iron and steel enterprises so far.

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

[0004] Currently, for the hot plasticity experiment of continuous casting billets, the common method is to obtain an austenite grain size similar to that of the actual billet surface layer through high-temperature hot tensile test (solution at 1350°C for 10 min) to determine the third brittle temperature range; however, previous studies have shown that the austenite grain size is often difficult to reach 1mm when solution at 1350°C for 10 min, resulting in a significant reduction in the reliability of the results and inability to accurately determine the "cold operation" or "hot operation" process plan for continuous casting. At the same time, under specific equipment conditions and the current continuous casting process, the precipitation types and sequences, particle sizes of carbonitrides (containing Ti, Nb, and Al) in low-alloy high-strength steel grades at different temperatures and their specific influence mechanisms on surface crack tendency are still unclear. Summary of the Invention

[0005] To solve the above problems, the present invention provides a hot plasticity prediction model, method, electronic device or storage medium. The present invention conducts experimental analysis on the precipitation and dissolution behavior of carbonitride particles, the austenite grain growth behavior of low-alloy high-strength steel slabs at different temperatures, and the hot plasticity curves at different austenite grain sizes, and obtains a reliable calculation model considering the upper limit temperature range of the third brittleness when the austenite grains on the surface of the actual slab are coarse. Based on this, the corner temperature under the current continuous casting conditions of low-alloy high-strength steel grades is calculated and verified by Procast, so as to provide a theoretical basis and technical support for the implementation of the "hot operation" process of continuous casting of this type of steel grade and then reduce the tendency of continuous casting surface cracks.

[0006] To achieve the above object, the technical solutions adopted by the present invention are as follows:

[0007] On the one hand, the present invention provides a hot plasticity 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 hot plasticity prediction model is constructed by the following method, including: obtaining training data, and based on the training data, constructing the relationship between the precipitation amount of TiN and the nitrogen content, titanium content and actual temperature of the low-alloy high-strength steel; calculating the value of A based on the average size of γ grains and the precipitation amount of TiN; based on the training data, constructing the minimum value Z of the reduction of area min , the temperature T corresponding to the minimum value of the reduction of area Zmin , the temperature T corresponding to the reduction of area dropping to 50% Z50 , the starting point T of the extrapolation of the hot plasticity curve DB and the relationship between the value of A, the strain rate , the complete austenite formation temperature T γ , the component content of the low-alloy high-strength steel and the temperature fluctuation ΔT; the component content of the low-alloy high-strength steel includes the percentage content of aluminum in the steel, the percentage content of nitrogen in the steel, the percentage content of titanium in the steel, and the percentage content of niobium in the steel.

[0008] On the other hand, the present invention provides a method for reducing the tendency of slab surface crack generation based on the above hot plasticity prediction model, including: determining the component content of the low-alloy high-strength steel, the average size of γ grains , calculating the value of A; determining the strain rate , the complete austenite formation temperature T γ , determining Z with the temperature fluctuation ΔT min , T Zmin , T Z50 and T DB; Determine the reduction of area 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 exit of the straightening section is higher than the first temperature and less than T DB That's it.

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

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

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

[0012] Furthermore, the threshold is 40%, and the calculation formula for the first temperature T0 is:

[0013] ;

[0014] ;

[0015] where CR is the cooling rate.

[0016] Furthermore, the temperature T1 at the exit of the straightening section is: .

[0017] Furthermore, the continuous casting process includes secondary cooling intensity and / or continuous casting drawing speed.

[0018] The present invention also provides an electronic device, including: a control center, the control center includes one or more processors; a memory, on which one or more programs are stored, when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the above-mentioned thermoplastic prediction model or the above-mentioned method.

[0019] The present invention also provides a storage medium, the storage medium stores one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the above-mentioned thermoplastic prediction model or the above-mentioned method.

[0020] The beneficial effects brought by the technical solution provided by the embodiment of the present invention include: For low-alloy high-strength steel, that is, 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%, a hot plasticity model is established. Through this model, Z min , T Zmin , T Z50 , T DB can be reliably calculated, so as to calculate the upper limit temperature of the brittle zone corresponding to the cross-sectional shrinkage rate threshold, which is beneficial to guiding the actual production in the continuous casting process and avoiding defects such as cracks in the continuous casting billet. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It shows the corner temperature change of AH36 under different continuous casting conditions provided in Embodiment 2 of the present invention;

[0023] Figure 2 It is the SEM diagram of samples taken at different positions in the thickness direction of the continuous casting billet prepared under the current continuous casting conditions of AH36 steel provided in Embodiment 2 of the present invention, where a, b, c, and d are four different sampling positions;

[0024] Figure 3 It is the SEM diagram of samples taken at different positions in the thickness direction of the continuous casting billet prepared under the improved continuous casting conditions of AH36 steel provided in Embodiment 2 of the present invention, where a and b adopt the first process, and c and d adopt the second process. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0025] The present invention will be further described in detail below through specific embodiments. However, those skilled in the art will understand that the following embodiments are only used to illustrate the present invention and should not be regarded as limiting the scope of the present invention. For those not specified in the embodiments regarding specific technologies or conditions, they shall be carried out according to the technologies or conditions described in the literature in the field or according to the product specifications. For reagents or instruments not specified for the manufacturer, they are all conventional products that can be obtained through commercial procurement.

[0026] As used herein, the terms "comprising", "including", "having" or any other variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, article or apparatus that comprises a list of 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. Unless the context clearly dictates otherwise, the singular forms "a / an" and "the" include plural referents.

[0027] Unless otherwise defined, all technical and scientific terms used in the present invention have the same meaning as commonly understood by those skilled in the art. In addition to the specific methods, devices, and materials used in the examples, any methods, devices, and materials of the prior art similar to or equivalent to those described in the embodiments of the present invention can also be used to implement the present invention according to the knowledge of those skilled in the art in the prior art and the description of the present invention.

[0028] Example 1

[0029] An embodiment of the present invention provides a hot plasticity prediction model for a 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 hot plasticity prediction model is constructed by the following method, including: obtaining training data, and based on the training data, constructing the relationship between the TiN precipitation amount and the nitrogen content, titanium content, and actual temperature of the low-alloy high-strength steel; calculating the value of A based on the average γ grain size and the TiN precipitation amount; based on the training data, constructing the minimum value Z of the reduction of area min , the temperature T corresponding to the minimum value of the reduction of area Zmin , the temperature T corresponding to the reduction of area dropping to 50% Z50 , the starting point T of the extrapolation of the hot plasticity curve DB and the relationship between the value of A, the strain rate , the temperature T at which austenite is completely formed γ , the component content of the low-alloy high-strength steel, and the temperature fluctuation ΔT; the component content of the low-alloy high-strength steel includes the percentage content of aluminum in the steel, the percentage content of nitrogen in the steel, the percentage content of titanium in the steel, and the percentage content of niobium in the steel.

[0030] Specifically, the calculation method of the value of A is:

[0031] ;

[0032] where is the average γ grain size, µm; P Ti is the TiN precipitation amount, %.

[0033] The thermoplastic prediction model constructed by the above construction method is as follows:

[0034] ;

[0035] ;

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] where Z min is the minimum value of the reduction of area, %; is the strain rate, s -1 ; is the percentage content of aluminum in steel, %; is the percentage content of nitrogen in steel, %; is the percentage content of titanium in steel, %; is the percentage content of niobium in steel, %; ΔT is the temperature fluctuation, °C; T Zmin is the temperature corresponding to the minimum value of the reduction of area, °C; T Z50 is the temperature corresponding to the reduction of area dropping to 50%, °C; T DB is the starting point of the extrapolation of the thermoplastic curve, °C; is the average grain size of γ grains, µm; P Ti is the precipitation amount of TiN, %; T γ is the temperature at which austenite is completely formed, °C; T is the actual temperature, °C.

[0041] The present invention establishes a thermoplastic model for low-alloy high-strength steel, that is, 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%. Through this model, Z min , T Zmin , T Z50 , T DB can be reliably calculated so as to calculate the upper limit temperature of the brittle zone corresponding to the reduction of area threshold, which is beneficial to guiding the actual production in the continuous casting process and avoiding defects such as cracks in the continuous casting billet.

[0042] This application is directed to a specific low-alloy high-strength steel. By combining a grain growth model and an area reduction rate model, a hot plasticity prediction model under the condition of a coarse austenite grain size has been developed. The technical solution proposed in the present invention is applicable to an austenite grain size of 800 μm - 1200 μm.

[0043] Specifically, the area reduction rate R.A. is calculated by the following formula:

[0044] ;

[0045] In the formula, S0 and S are the initial area of the specimen and the fracture area, respectively.

[0046] By means of the effect of the complete austenite formation temperature T γ , 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 composition content, the strain rate, and the temperature fluctuation, a quantitative calculation method for the characteristic value of the hot plasticity curve in the third brittle zone of the continuous casting billet of the low-alloy high-strength steel of the present application is constructed to calculate Z min , T Zmin , T Z50 , T DB , in order to guide the subsequent actual continuous casting process.

[0047] Specifically, a confocal laser scanning microscope is used to simulate the austenite grain growth behavior during the continuous casting process of microalloyed steel, and the effects of the intermediate value A, the austenite grain growth starting temperature, and the steel composition on this behavior are analyzed. An austenite grain size prediction model is proposed, which is applicable to a cooling rate of 0.2 °C / s - 5.0 °C / s and for the specific composition and content range of the low-alloy steel proposed in the present application. At the same time, in combination with the above austenite grain size prediction model, the area reduction rate function (RA) of the tensile test is embedded, the critical strain is deduced using the RA value, and it is compared and analyzed with the dynamic strain calculated by the thermo-mechanical coupling model of the continuous casting billet surface. The integral method is used to process the transient conditions, and the weighted sum of the ratio of the dynamic strain to the corresponding critical strain within the time increment is carried out. When the integral value reaches the threshold, it is determined that cracks occur.

[0048] The embodiment of the present invention also provides a method for reducing the tendency of surface crack generation based on the above hot plasticity prediction model, including:

[0049] S1 Determine the composition content of the low-alloy high-strength steel, the average size of γ grains , and calculate the value of A based on the above parameters;

[0050] S2 Determine the strain rate , the complete austenite formation temperature T γ， The temperature fluctuation ΔT determines Z min , T Zmin, T Z50 and T DB ;

[0051] S3 determines the reduction of area 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;

[0052] S4 adjusts the continuous casting process so that the temperature at the exit of the straightening section is higher than the first temperature and lower than T DB That's it.

[0053] Specifically, in step S1, the composition of the low-alloy high-strength steel can be obtained based on actual measurement, and the average γ grain size is determined based on the surface layer grains of the continuous casting slab. The surface layer is the average grain size within 15 mm along the thickness direction of the continuous casting billet. The austenite complete formation temperature T γ is determined based on DSC test.

[0054] The strain rate in step S2 is calculated in combination with the equivalent strain rate during the continuous casting process. The temperature fluctuation value is set based on the on-site working conditions. By substituting the above parameters into the thermoplasticity model of the present application, Z min , T Zmin , T Z50 and T DB .

[0055] In step S3, determine the reduction of area threshold of the low-alloy high-strength steel. The threshold of the reduction of area is measured by a Gleeble3800 thermal simulation testing machine using a high-temperature tensile test or set based on empirical values. When measured using a thermal simulation testing machine, obtain the thermoplasticity curves at different solution temperatures, obtain the reduction of area threshold based on the thermoplasticity curves, and based on the calculated Z min , T Zmin , T Z50 and T DB calculate the first temperature T0 corresponding to the threshold.

[0056] For the low-alloy high-strength steel proposed in the present application, the threshold is 35% - 45%, preferably 40%.

[0057] When the threshold is selected as 40%, the calculation formula for the first temperature T0 is:

[0058] ;

[0059] ;

[0060] Where CR is the cooling rate.

[0061] Through the above calculation formula, the first temperature T0 when the threshold is 40% can be accurately calculated.

[0062] It should be noted that the generation of cracks is relative, not absolute, that is, the probability of crack generation can be significantly reduced at temperatures above a certain first temperature.

[0063] For the convenience of calculation, the present invention also proposes another calculation method for the first temperature T0. Specifically:

[0064] ;

[0065] Where Z is the threshold value of the reduction of area; is a correction coefficient, with a value of 2.24 - 2.25; represents that the reduction of area is 50%; represents the minimum reduction of area.

[0066] Through the above two calculation methods, the probability of crack generation in the continuous casting process of low-alloy high-strength steel can be reduced, so that the crack generation probability is less than 5 per 10 meters, and the size of the cracks is not more than 5 mm; another calculation method for the first temperature T0 proposed by the present invention can calculate the temperature corresponding to other reductions of area, and the calculation result is more simple and fast, with a wide application range.

[0067] In step S4, adjust the continuous casting process by adjusting the secondary cooling intensity and / or the continuous casting drawing speed, so that the temperature at the exit of the straightening section is higher than the first temperature and less than T DB That's it. The temperature T1 at the exit of the straightening section is: .

[0068] The embodiment of the present application also provides an electronic device, such as a 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 control method.

[0069] The electronic device in the present application may include one or more of the following components: a memory, a processor, and one or more application programs, where one or more application programs may be stored in the memory and configured to be executed by one or more processors, and one or more programs are configured to execute the method described in the foregoing method embodiments.

[0070] The memory may include Random Access Memory (RAM) and may also include Read-Only Memory (ROM). The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory may include a program storage area and a data storage area. Among them, the program storage area can store instructions for implementing the operating system, instructions for implementing at least one function (such as histogram equalization function, etc.), instructions for implementing the above various method embodiments, etc. The data storage area can also store data created during the use of the electronic device (such as image matrix data, etc.).

[0071] The processor may include one or more processing cores. The processor connects various parts within the entire electronic device using various interfaces and lines. By running or executing instructions, programs, code sets, or instruction sets stored in the memory, and by invoking data stored in the memory, it executes various functions of the electronic device and processes data. Optionally, the processor may be implemented in at least one of the hardware forms of Digital Signal Processing (DSP), Field-Programmable Gate Array (FPGA), and Programmable Logic Array (PLA). The processor may integrate one or several combinations of a Central Processing Unit (CPU) and a modem, etc. Among them, the CPU mainly processes the operating system and application programs, etc.; the modem is used to process wireless communication. It can be understood that the above modem may not be integrated into the processor and can be implemented separately through a communication chip.

[0072] Those skilled in the art can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described according to functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Skilled professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0073] Embodiment 2

[0074] The embodiment of the present invention aims at the continuous casting process of AH36 steel and combines the thermoplasticity prediction model proposed by the present invention to reduce the tendency of surface cracks in the billet, including:

[0075] S1 Determine the component content and average γ grain size of the high-strength low-alloy steel, and calculate the A value. , and calculate the A value.

[0076] The specific components of the AH36 steel are shown in Table 1.

[0077] Table 1 Components of AH36 Steel

[0078]

[0079] Average γ grain size is 1000 µm, and calculate the A value based on the above parameters;

[0080] S2 Strain rate Take 3×10 -4 s -1 , the complete austenite formation temperature T γ is 1448 °C, the temperature fluctuation ΔT is taken as 50 °C, and calculate to obtain Z min = 23.01%, T Zmin = 761.92 °C, T Z50 = 988 °C, T DB = 1006 °C;

[0081] S3 Determine that the cross-sectional shrinkage rate threshold of the high-strength low-alloy steel is 40%, and based on the Z min , T Zmin , T Z50 and T DB calculate the first temperature corresponding to the threshold.

[0082] When using the formula, calculate to obtain T Z40 is 970 °C.

[0083] When using to calculate, obtain T0 as 970 °C.

[0084] S4 Adjust the continuous casting process so that the temperature at the exit of the straightening section is higher than the first temperature and lower than T DB That's it. By setting the temperature at the exit of the straightening section to be higher than 970 °C.

[0085] In order to characterize whether there are cracks in the prepared slab, samples are taken and analyzed on the cross-sections at multiple different positions in the longitudinal direction of the continuous casting slab. The longitudinal direction of the continuous casting slab is the moving direction of the continuous casting slab.

[0086] The temperature field under the current continuous casting conditions of AH36 steel was calculated by Procast software and verified. The calculation condition parameters and the distribution of secondary cooling intensity (SCI) in the secondary cooling zone are shown in Table 3 and Table 4 respectively. The angular temperature changes at different distances from the meniscus are as Figure 1 shown. From Figure 1 it can be seen that at the current cooling intensity, the angular temperature of the slab at the exit of the straightening section (11.63 m from the meniscus) is about 956 °C. There are cracks in the thickness direction of the produced slab, as Figure 2 shown, with a depth exceeding 10 mm.

[0087] Table 3 Current continuous casting process parameters of AH36

[0088]

[0089] Table 4 Distribution of current secondary cooling intensity in the secondary cooling zone

[0090]

[0091] It can be seen that the temperature at the exit 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 at the exit of the mold and the bending section of the slab, which is consistent with the model calculation.

[0092] In the embodiments of the present invention, the temperature at the exit position of the straightening section is increased by two adjustment processes:

[0093] The first process: Based on the current casting speed of 1.7 m / min and secondary cooling intensity (SCI), the secondary cooling intensity is reduced by 30%;

[0094] The second process: The casting speed is increased to 2.0 m / min.

[0095] From Figure 1 it can be seen that the temperature of the slab at the exit of the straightening section reaches 977 °C or 979 °C, both of which are higher than the upper limit temperature of the third brittleness of 970 °C, improving the hot plasticity of the slab at the corner of the bending and straightening section, thus avoiding the generation of corner cracks.

[0096] Through detection, within a longitudinal length of 10 m, 50 positions were evenly sampled, and cracks in the thickness direction were observed. No cracks were found, as Figure 3 shown.

[0097] The above are only the embodiments of the present application and are not used to limit the protection scope of the present application. For those skilled in the art, various changes and modifications can be made to the present application. 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 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 thermoplasticity prediction model is constructed by the following method, including: Obtain training data, and based on the training data, construct the relationship between the precipitation amount of TiN and the nitrogen content, titanium content and actual temperature of the low-alloy high-strength steel; Calculate the value of A based on the average size of γ grains and the precipitation amount of TiN; construct the minimum value of the reduction of area Z based on the training data min , the temperature T corresponding to the minimum value of the reduction of area Zmin , the temperature T corresponding to the reduction of area dropping to 50% Z50 , the starting point T of the extrapolation of the thermoplasticity curve DB and the relationship between the value of A, the strain rate the temperature T at which austenite is completely formed γ , the content of low-alloy high-strength steel components and the temperature fluctuation ΔT The component content of the low-alloy high-strength steel includes the percentage content of aluminum in the steel, the percentage content of nitrogen in the steel, the percentage content of titanium in the steel, and the percentage content of niobium in the steel; Among them, Z min is the minimum value of the reduction of area, %; is the strain rate, s -1 ; w(Al) is the percentage content of aluminum in the steel, %; w(N) is the percentage content of nitrogen in the steel, %; w(Ti) is the percentage content of titanium in the steel, %; w(Nb) is the percentage content of niobium in the steel, %; ΔT is the temperature fluctuation, °C; T Zmin is the temperature corresponding to the minimum value of the reduction of area, °C; T Z50 is the temperature corresponding to the reduction of area dropping to 50%, °C; T DB is the starting point of the extrapolation of the hot plasticity curve, °C; is the average size of γ grains, μm; P Ti is the precipitation amount of TiN, %; T γ is the temperature at which austenite is completely formed, °C; T is the actual temperature, °C.

2. A method for reducing the tendency of surface cracks in continuous casting billets based on the thermoplasticity prediction model according to claim 1, characterized in that, Including: Determine the composition content of the low-alloy high-strength steel and the average size of the γ grains Calculate the value of A; Determine the strain rate The complete austenite formation temperature T γ , the temperature fluctuation ΔT, determine Z min , T Zmin , T Z50 and T DB ; Determine the reduction of area 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 less than T DB That's all.

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

4. The method according to claim 2, wherein The austenite complete formation temperature T γ is determined by DSC test.

5. The method according to claim 2, wherein The threshold is 35% - 45%.

6. The method according to claim 5, wherein The threshold is 40%; The calculation formula of the first temperature T0 is: where CR is the cooling rate, °C / s; P Ti is the precipitation amount of TiN, %; T γ is the temperature at which austenite is completely formed, °C, w(Al) is the percentage content of aluminum in the steel, %; w(N) is the percentage content of nitrogen in the steel, %; is the strain rate, s -1 ; ΔT is the temperature fluctuation, °C.

7. The method according to claim 6, wherein The temperature T1 at the outlet of the straightening section is: T1 = T0 + (0°C to 30°C).

8. The method according to claim 2, wherein The continuous casting process includes secondary cooling intensity and / or continuous casting drawing speed.

9. An electronic device, characterized in that, Including: A control center, the control center includes one or more processors; A memory, on which one or more programs are stored. When the one or more programs are executed by the one or more processors, the one or more processors implement the thermoplasticity prediction model according to claim 1 or the method according to any one of claims 2 - 8.

10. A storage medium, characterized in that, The storage medium stores 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 thermoplasticity prediction model according to claim 1 or the method according to any one of claims 2 - 8.

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