Low-temperature-resistant flat steel with refined grain structure and preparation method of low-temperature-resistant flat steel

Abstract

The invention relates to the technical field of material processing, in particular to a preparation method of low-temperature-resistant flat steel with a refined grain structure, which comprises the following steps: firstly, acquiring alloy components of a steel billet, specification parameters of the steel billet and corresponding process parameters and material properties; constructing a process optimization model according to the data; then target material performance is determined according to process requirements, the model automatically analyzes stored empirical data according to the target material performance, and optimal alloy components, specification parameters and corresponding process parameters are recommended; then, respectively weighing all alloy elements according to a design ratio recommended by the model, smelting, refining, and continuously casting to form a steel billet; finally, the steel billet is cut into corresponding specifications, technological parameters are adjusted according to the specifications of the steel billet, and corresponding deformation quantity and temperature are controlled for multiple times of rolling; and after finish rolling is completed, laminar cooling is conducted, heat preservation is conducted when the flat steel is cooled to the set temperature, the flat steel is gradually converted into ferrite and pearlite, then the flat steel is cooled to the room temperature, and the grain-refined low-temperature-resistant flat steel is obtained.

Description

Technical Field

[0001] The invention relates to the technical field of material processing, and in particular to a low-temperature resistant flat steel with a refined grain structure and a preparation method thereof. Background Art

[0002] Low temperature resistant flat steel is widely used in key engineering structures in low temperature environments, such as ships and marine engineering equipment. Since it needs to withstand impact loads and maintain high strength and good toughness in extremely low temperature environments (usually below -40°C), the performance requirements of the material are extremely strict. Typical requirements include high strength to withstand static and impact loads, excellent low temperature toughness to avoid brittle fracture, and good welding performance to ensure safety and reliability in complex structures.

[0003] However, the existing low-temperature resistant flat steel production technology still faces multiple challenges in practical application. First, the traditional composition design often relies on high contents of precious alloying elements such as Ni, Cr, and Cu to improve low-temperature toughness and strength, which significantly increases production costs and is not conducive to large-scale industrial production.

[0004] Secondly, the preparation of low-temperature resistant flat steel involves many parameters such as alloy component ratio, smelting temperature, rolling pass, cooling rate, etc. There is a complex nonlinear coupling relationship between these parameters, and has a significant impact on the final performance. In actual production, factors such as equipment performance fluctuations, material batch differences, and environmental changes may make it difficult to accurately reproduce parameters. Even if there are products with excellent specifications in historical data, it is difficult to stably achieve them in multiple batches of production. In addition, grain refinement, as an important means to improve the balance between strength and toughness of materials, is difficult to control during the smelting and rolling process due to the difficulty in controlling the uniformity of the organization, and problems such as grain growth or uneven organization often occur, which directly affects the final performance. In addition, the rolling and heat treatment parameter requirements for billets of different specifications are different. In traditional processes, parameter setting relies more on experience, resulting in large performance fluctuations during the production process.

[0005] At the same time, the existing process optimization methods mostly rely on experiments and experience accumulation, which are inefficient and difficult to quickly find the optimal process path under complex multi-parameter conditions. The lack of intelligent process optimization tools further limits the performance improvement and production efficiency of low-temperature resistant flat steel.

[0006] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0007] The technical problem to be solved by the present invention is to provide a low-temperature resistant flat steel with refined grain structure and a preparation method thereof, which is used in the field of shipbuilding and marine engineering equipment, and significantly improves material performance, production efficiency and economy through intelligent control and full-process optimization.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is:

[0009] A method for preparing low-temperature resistant flat steel with refined grain structure comprises the following steps:

[0010] S01: Obtain the alloy composition of the steel billet, the specification parameters of the steel billet, the corresponding process parameters and material properties; and build a process optimization model based on the above data;

[0011] Preferably, in step S01, the process parameters include rolling process parameters and heat treatment process parameters, and the material properties include billet deformation, stress-strain distribution and grain size.

[0012] Preferably, in step S01, the alloy composition and specification parameters of different steel billets are collected and stored, and for steel billets of different specifications, historical data of process parameters and historical data of material properties are collected, a process database is established, a process optimization model is established, and the data in the process database is input into the process optimization model to train the model.

[0013] S02: Determine the target material properties according to the process requirements. The model automatically analyzes the stored empirical data based on the target material properties and recommends the best alloy composition, specification parameters and corresponding process parameters.

[0014] Preferably, in step S02, the user inputs the target material properties, and the model automatically analyzes the stored empirical data based on the input target material properties, and screens out multiple data groups within the allowable deviation range of the target material properties. Among the multiple data groups, they are sorted from small to large according to the priority of the deviation value from the target material properties, grain size, total cost of alloy elements, and deviation value between the parameters and the equipment capability range, and the top-ranked solution is output as the best recommendation.

[0015] Preferably, the present invention sets the priority rules and sorts the data groups according to different factors. The following is a specific method of assigning and sorting priorities:

[0016] The principle of priority assignment is: the deviation value from the target performance has the highest priority, followed by grain size, alloy cost, and deviation value from the equipment capability range. The present invention ensures that the scheme has the best mechanical properties and low-temperature toughness by giving priority to the deviation value and grain size from the target performance. On the premise that the performance meets the requirements, low-cost alloy composition design schemes are given priority to improve economic benefits. Then, data groups that match the equipment capability range are given priority to reduce the complexity of production adjustments and adapt to continuous industrial production. The present invention provides scientific support for process optimization and recommended schemes through comprehensive sorting of multiple indicators combined with the intelligent analysis capabilities of the model, while effectively solving the decision-making problems under complex multi-parameter conditions.

[0017] Preferably, the deviation value from the target material performance is:

[0018]

[0019] Among them, X is the deviation value, A i is the actual deformation, A0 is the target deformation, B i is the actual stress-strain value, B0 is the target stress-strain value, C i is the actual grain size, C0 is the target grain size, ω1, ω2, ω3 are weight factors, which are adjusted according to specific performance requirements. After calculating the deviation value from the target material performance, the grain size is sorted from small to large as a separate condition to ensure that the produced flat steel has a smaller grain structure. The deviation value between the parameter and the equipment capacity range refers to the difference between the process parameter and the production setting value of the company's existing equipment.

[0020] On this basis, the specific steps of priority sorting are:

[0021] Set the allowable deviation range according to the target performance and filter out the data groups that meet the conditions. For example, if the allowable deviation of the target performance is 5%, filter out the data groups with deviation values ​​X≤5%;

[0022] The data groups are sorted in ascending order of deviation values ​​from the target material properties to obtain a preliminary sorting result;

[0023] When the deviation values ​​are the same, they are further sorted in order of grain size from small to large;

[0024] When the deviation value and grain size are the same, the total cost of alloying elements is ranked in ascending order;

[0025] When the ranking results of the first three items are the same, they are sorted in ascending order of deviation value;

[0026] According to the sorting results, the top-ranked solution is output as the final recommendation.

[0027] Preferably, in step S02, if the deviation value between the outputted top-ranked solution and the target material performance is greater than 5%, a sensitivity analysis is performed on each recipe and parameter to identify the variables that have the greatest impact on the target performance, and a numerical optimization algorithm is used to find a better solution in the existing parameter space, and the optimized parameter combination is output as a recommendation.

[0028] S03: Weigh each alloy element according to the design ratio recommended by the model, smelt and refine, and then continuously cast into steel billets;

[0029] Preferably, step S03 includes the following steps:

[0030] S31 Accurately weigh each alloy element according to the recommended design ratio;

[0031] S32 first maintains the molten iron temperature at 1600-1650°C for smelting;

[0032] S33. Add alloy elements in order from high to low melting points, and stir to make the alloy elements fully diffused and evenly distributed in the molten iron;

[0033] S34 then refines the molten steel at 1550-1650°C;

[0034] S35 adds desulfurizer, dephosphorizer and deoxidizer in sequence to reduce the content of non-metallic inclusions in the molten steel;

[0035] S36 uses a continuous casting machine to cast the refined molten steel into steel billets to obtain steel billets with shapes and sizes that meet the requirements.

[0036] S04: Cut the steel billet into corresponding specifications, adjust the parameters according to the specifications of the steel billet and heat it until it is completely austenitized, then control the corresponding deformation and temperature, and perform multiple rolling; after the final rolling is completed, laminar cooling is performed, and when it is cooled to the set temperature, it is kept warm to gradually transform it into ferrite and pearlite, and then cooled to room temperature to obtain a low-temperature resistant flat steel with refined grains.

[0037] Preferably, step S04 includes the following steps:

[0038] Cut the steel billet into corresponding specifications, adjust the heating time and heating temperature according to the specifications of the steel billet, and make the steel billet completely austenitized;

[0039] The steel billet is rolled multiple times in sequence according to the process parameters corresponding to the model, and the thickness, temperature and grain uniformity of the steel billet are monitored in real time after each pass;

[0040] Immediately after final rolling, the steel is cooled using a laminar cooling process; when cooled to the set temperature, the steel is kept warm, and after the end of the warming process, it is cooled to room temperature to ensure the uniformity of the material structure.

[0041] The second object of the present invention is to provide a low-temperature resistant flat steel with a refined grain structure obtained by the above-mentioned preparation method. The structure of the low-temperature resistant flat steel consists of ferrite and pearlite, and the grain size reaches above grade 8.0, and can be used in the field of ships and marine engineering equipment.

[0042] In order to achieve the above object, the technical solution adopted by the present invention is:

[0043] The invention discloses a low-temperature resistant flat steel with a refined grain structure, wherein the mass composition of the low-temperature resistant flat steel is as follows: C≤0.18%; Si≤0.50%; Mn: 0.90-1.60%; P≤0.030%; S≤0.030%; Cr≤0.20%; Ni≤0.40%; Cu≤0.35%; Al: 0.020%-0.050%; V: 0.02%-0.10%; the rest is Fe; and Ceq≤0.40 is satisfied at the same time.

[0044] The beneficial effects of the present invention are:

[0045] The present invention proposes a method for preparing low-temperature resistant flat steel based on a process optimization model. Through intelligent control and full-process optimization, the material performance, production efficiency and economy are significantly improved. In terms of composition design, microalloying means are used to introduce trace amounts of alloying elements such as Nb, V, and Ti with lower Ni, Cr, and Cu contents to form dispersed and precipitated fine carbides or nitrides, inhibit grain growth, and achieve a balance between low cost and high performance. In terms of process optimization, a process optimization model based on a machine learning algorithm is established. Using technologies such as random forests, support vector machines, and deep neural networks, combined with a historical process database, the nonlinear relationship between process parameters and material properties is analyzed, and the optimal parameter combination is intelligently recommended. Traditional metallurgical theories such as grain growth and phase transformation dynamics are also incorporated into the model to form a hybrid optimization model that combines data and theory, which improves the reliability and guidance of process prediction.

[0046] In terms of grain refinement and organizational control, this application uses multiple rolling processes of dynamic recrystallization and deformation energy storage release, combined with laminar cooling and heat preservation treatment, to achieve uniform distribution of ferrite and pearlite, and a grain size of 8.0 or above. In addition, when the recommended parameter scheme deviates from the target performance by more than 5%, the model adjusts the parameters through sensitivity analysis and numerical optimization algorithms to output the optimization scheme with the smallest deviation. Even if there is no scheme in the model that fully meets the target performance, the optimization strategy can be used to select a formula and process close to the target to ensure the stability of flat steel performance and improve process applicability.

[0047] The low-temperature resistant flat steel preparation method of the present application has excellent comprehensive performance through precise smelting control, dynamic recrystallization process of multiple rolling and optimized heat treatment process: fine grain, uniform structure, high strength and high toughness, and can adapt to extreme low temperature environment. At the same time, the process optimizes the equipment capacity range, reduces the transformation cost, and is suitable for large-scale industrial application of existing production lines. Compared with the existing technology, the present application not only achieves a higher level in performance, but also significantly reduces the production cost, and has significant technical advantages and broad application prospects. DETAILED DESCRIPTION

[0048] The technical solutions of the embodiments of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0049] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0050] The invention discloses a low-temperature resistant flat steel with a refined grain structure. The structure of the low-temperature resistant flat steel consists of ferrite and pearlite, and the grain size reaches above grade 8.0. The low-temperature resistant flat steel can be used in the field of shipbuilding and marine engineering equipment. The mass composition of the low-temperature resistant flat steel is as follows: C≤0.18%; Si≤0.50%; Mn: 0.90-1.60%; P≤0.030%; S≤0.030%; Cr≤0.20%; Ni≤0.40%; Cu≤0.35%; Al: 0.020%-0.050%; V: 0.02%-0.10%; the rest is Fe; and Ceq≤0.40 is satisfied at the same time.

[0051] The method for preparing the low-temperature resistant flat steel with refined grain structure comprises the following steps:

[0052] S01: Obtain the alloy composition of the steel billet, the specification parameters of the steel billet, the corresponding process parameters and material properties; and build a process optimization model based on the above data;

[0053] Preferably, in step S01, the process parameters include rolling process parameters and heat treatment process parameters, and the material properties include billet deformation, stress-strain distribution, and grain size. Specifically, the alloy composition and specification parameters of different billets are collected and stored, and for billets of different specifications, the historical data of process parameters and material properties are collected, a process database is established, a process optimization model is established, and the data in the process database is input into the process optimization model to train the model. Among them, the database can be continuously updated through the real-time data acquisition system of the production line, and the accuracy and applicability of the model are gradually enhanced. Machine learning algorithms such as random forests, support vector machines, or deep neural networks are used to model and train big data to identify complex nonlinear relationships.

[0054] S02: Determine the target material properties according to the process requirements. The model automatically analyzes the stored empirical data based on the target material properties and recommends the best alloy composition, specification parameters and corresponding process parameters.

[0055] Preferably, in step S02, the user inputs the target material properties (for example: grain size above grade 8.0, upper yield strength ≥355MPa, tensile strength>490MPa, elongation after fracture ≥21%), and the model automatically analyzes the stored empirical data according to the input target material properties, and screens out multiple data groups within the allowable deviation range of the target material properties. Among the multiple data groups, they are sorted from small to large according to the priority of the deviation value from the target material properties, grain size, total cost of alloy elements, and deviation value of parameters from the equipment capacity range, and the first-ranked solution is output as the best recommendation.

[0056] Preferably, the present invention sets the priority rules and sorts the data groups according to different factors. The following is a specific method of assigning and sorting priorities:

[0057] The principle of priority assignment is: the deviation value from the target performance has the highest priority, followed by grain size, alloy cost, and deviation value from the equipment capability range. The present invention ensures that the scheme has the best mechanical properties and low-temperature toughness by giving priority to the deviation value and grain size from the target performance. On the premise that the performance meets the requirements, low-cost alloy composition design schemes are given priority to improve economic benefits. Then, data groups that match the equipment capability range are given priority to reduce the complexity of production adjustments and adapt to continuous industrial production. The present invention provides scientific support for process optimization and recommended schemes through comprehensive sorting of multiple indicators combined with the intelligent analysis capabilities of the model, while effectively solving the decision-making problems under complex multi-parameter conditions.

[0058] Among them, the deviation value from the target material performance is:

[0059]

[0060] Among them, X is the deviation value, A i is the actual deformation, A0 is the target deformation, B i is the actual stress-strain value, B0 is the target stress-strain value, C i is the actual grain size, C0 is the target grain size, ω1, ω2, ω3 are weight factors, which are adjusted according to specific performance requirements. After calculating the deviation value from the target material performance, the grain size is sorted from small to large as a separate condition to ensure that the produced flat steel has a smaller grain structure. The deviation value between the parameter and the equipment capacity range refers to the difference between the process parameter and the production setting value of the company's existing equipment.

[0061] The type and proportion of alloying elements are determined according to the performance target of the required low-temperature resistant flat steel. Common alloying elements include: Carbon (C): controlled in a low content range to improve toughness and promote grain refinement. Manganese (Mn): increases the strength and toughness of steel and prevents low-temperature brittleness. Silicon (Si): improves the deoxidation effect of steel and increases strength at the same time. Niobium (Nb) and vanadium (V): promote grain refinement and form dispersed precipitated carbides. Titanium (Ti): inhibits grain growth and improves low-temperature resistance. Each alloying element is accurately weighed during the process, and an electronic balance is used to ensure that the error does not exceed 0.01%.

[0062] On this basis, the specific steps of priority sorting are:

[0063] Set the allowable deviation range according to the target performance and filter out the data groups that meet the conditions. For example, if the allowable deviation of the target performance is 5%, filter out the data groups with deviation values ​​X≤5%;

[0064] The data groups are sorted in ascending order of deviation values ​​from the target material properties to obtain a preliminary sorting result;

[0065] When the deviation values ​​are the same, they are further sorted in order of grain size from small to large;

[0066] When the deviation value and grain size are the same, the total cost of alloying elements is ranked in ascending order;

[0067] When the ranking results of the first three items are the same, they are sorted in ascending order of deviation value;

[0068] According to the sorting results, the top-ranked solution is output as the final recommendation.

[0069] For example: C≤0.13%; Si≤0.35%; Mn: 1.35%; P≤0.020%; S≤0.015%; Cr≤0.06%; Ni≤0.05%; Cu≤0.06%; Al: 0.003%; V: 0.035%; the rest is Fe; and Ceq≤0.40 is satisfied at the same time.

[0070] During the process, the present invention preferably selects a data group that is close to the target performance. When no set of formulas and parameters in the model can fully achieve the target performance, a data group with a smaller deviation value from the target material performance is selected. The smaller the deviation value, the better the solution, ensuring that the flat steel obtained has better applicability. Specifically, if the deviation value between the output first-ranked solution and the target material performance is greater than 5%, a sensitivity analysis is performed on each formula and parameter to quantify the degree of influence of each parameter on the target performance, identify the variables that have the greatest influence on the target performance, and use numerical optimization algorithms (such as genetic algorithms and particle swarm algorithms) to find a better solution in the existing parameter space, and output the optimized parameter combination as a recommendation. On this basis, when outputting a set of parameters with the smallest deviation, it can also be prompted that "the target performance is not fully met, but the deviation is within an acceptable range."

[0071] S03: Accurately weigh each alloying element according to the recommended design ratio; first, keep the molten iron temperature at 1600-1650℃ for smelting; ensure that the alloying elements are completely dissolved and will not increase the oxidation of the molten steel, add the alloying elements in order from high to low melting points, and stir to make the alloying elements fully diffuse and evenly distribute in the molten iron; then refine the smelted molten steel at 1550-1650℃; add desulfurizer (lime, fluorite), dephosphorizer, and deoxidizer (silicon manganese alloy or silicon iron alloy) in turn to reduce the content of non-metallic inclusions in the molten steel; use argon or argon-oxygen mixed gas for blowing to remove gases such as hydrogen, nitrogen and oxygen in the molten steel. The refined molten steel is cast into a billet through a continuous casting machine to obtain a billet with the required shape and size; during continuous casting, the cooling rate and casting speed are precisely controlled to avoid segregation, cracks or other defects.

[0072] S04: Cut the steel billet into corresponding specifications, such as the cross-sectional dimensions of the steel billet are 150 mm × 150 mm; 160 mm × 230 mm; 170 mm × 280 mm, and adjust the heating time and heating temperature according to the specifications of the steel billet to make the steel billet completely austenitized;

[0073]

[0074] Specifically: according to the volume and thermal conductivity of the steel billet, extend or shorten the heating time to ensure that the inside of the steel billet can be heated evenly and reach a state of complete austenitization. The temperature needs to be controlled within the austenitization temperature range of the steel grade. This can prevent insufficient heating from causing incomplete austenitization of the grains, and prevent excessive temperature from causing grain growth, affecting the final performance.

[0075] The steel billet is rolled multiple times in sequence according to the process parameters corresponding to the model, and the rolling temperature is controlled as follows:

[0076]

[0077] After each pass, the thickness, temperature and grain uniformity of the steel billet are monitored in real time; after final rolling, the steel is immediately cooled to 700-800℃ by laminar cooling process; when cooled to the set temperature, the heat preservation operation is carried out, and after 10-30 minutes, it is further cooled to room temperature to promote the transformation of austenite to ferrite and pearlite. The present invention obtains a fine-grained structure with a grain size of 8.0 or above through dynamic recrystallization, laminar cooling and heat preservation control, which significantly improves strength and toughness.

[0078] Those skilled in the art should understand that the present invention is not limited to the above embodiments, and the above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for preparing low-temperature resistant flat steel with refined grain structure, characterized in that: The steps include: S01: Obtain the alloy composition of the steel billet, the specification parameters of the steel billet, the corresponding process parameters and material properties; and build a process optimization model based on the above data; S02: Determine the target material properties according to the process requirements. The model automatically analyzes the stored empirical data based on the target material properties and recommends the best alloy composition, specification parameters and corresponding process parameters. S03: Weigh each alloy element according to the design ratio recommended by the model, smelt and refine, and then continuously cast into steel billets; S04: Cut the steel billet into corresponding specifications, adjust the parameters according to the specifications of the steel billet and heat it until it is completely austenitized, then control the corresponding deformation and temperature, and perform multiple rolling; after the final rolling is completed, laminar cooling is performed, and when it is cooled to the set temperature, it is kept warm to gradually transform it into ferrite and pearlite, and then cooled to room temperature to obtain a low-temperature resistant flat steel with refined grains.

2. The method for preparing a low-temperature resistant flat steel with a refined grain structure according to claim 1, characterized in that: In step S01, the process parameters include rolling process parameters and heat treatment process parameters, and the material properties include billet deformation, stress-strain distribution, and grain size.

3. The method for preparing a low-temperature resistant flat steel with a refined grain structure according to claim 2, characterized in that: In step S01, the alloy composition and specification parameters of different steel billets are collected and stored, and for steel billets of different specifications, historical data of process parameters and historical data of material properties are collected, a process database is established, a process optimization model is established, and the data in the process database is input into the process optimization model to train the model.

4. The method for preparing a low-temperature resistant flat steel with a refined grain structure according to claim 2, characterized in that: In step S02, the user inputs the target material properties, and the model automatically analyzes the stored empirical data based on the input target material properties, and screens out multiple data groups within the allowable deviation range of the target material properties. Among the multiple data groups, they are sorted from small to large according to the priority of the deviation value from the target material properties, grain size, total cost of alloy elements, and deviation value between parameters and equipment capacity range, and the top-ranked solution is output as the best recommendation.

5. The method for preparing low-temperature resistant flat steel with refined grain structure according to claim 4, characterized in that: In step S02, if the deviation between the output top-ranked solution and the target material performance is greater than 5%, a sensitivity analysis is performed on each recipe and parameter to identify the variables that have the greatest impact on the target performance, and a numerical optimization algorithm is used to find a better solution in the existing parameter space, and the optimized parameter combination is output as a recommendation.

6. The method for preparing low-temperature resistant flat steel with refined grain structure according to claim 5, characterized in that: The deviation values ​​from the target material properties are: Among them, X is the deviation value, A i is the actual deformation, A0 is the target deformation, B i is the actual stress-strain value, B0 is the target stress-strain value, C i is the actual grain size, C0 is the target grain size, ω1, ω2, and ω3 are weight factors, which are adjusted according to specific performance requirements.

7. The method for preparing low-temperature resistant flat steel with refined grain structure according to claim 5, characterized in that: The deviation value between the parameters and the equipment capacity range refers to the difference between the process parameters and the production setting values ​​of the company's existing equipment.

8. The method for preparing low-temperature resistant flat steel with refined grain structure according to claim 5, characterized in that: Step S03 includes the following steps: S31 Accurately weigh each alloy element according to the recommended design ratio; S32 first maintains the molten iron temperature at 1600-1650°C for smelting; S33. Add alloy elements in order from high to low melting points, and stir to make the alloy elements fully diffused and evenly distributed in the molten iron; S34 then refines the molten steel at 1550-1650°C; S35 adds desulfurizer, dephosphorizer and deoxidizer in sequence to reduce the content of non-metallic inclusions in the molten steel; S36 uses a continuous casting machine to cast the refined molten steel into steel billets to obtain steel billets with shapes and sizes that meet the requirements.

9. A low temperature resistant flat steel with refined grain structure according to any one of claims 1 to 8, characterized in that: The mass composition of the low temperature resistant flat steel is as follows: C≤0.18%; Si≤0.50%; Mn: 0.90-1.60%; P≤0.030%; S≤0.030%; Cr≤0.20%; Ni≤0.40%; Cu≤0.35%; Al: 0.020%~0.050%; V: 0.02% to 0.10%; the rest is Fe; and Ceq≤0.40 is satisfied.

10. The low temperature resistant flat steel with refined grain structure according to claim 9, characterized in that: The microstructure of the low-temperature resistant flat steel consists of ferrite and pearlite, and the grain size reaches above grade 8.0.

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