A method for predicting and controlling strength and toughness of a pipeline steel

Abstract

The present application belongs to the technical field of intelligent processing of metal materials, and specifically discloses a method for predicting and controlling the strength and toughness of pipeline steel. The method first acquires the physical parameters of the pipeline steel, and establishes a microstructure regulation model including the fusion zone and the heat-affected zone of the weld. Based on the service environment, the boundary conditions of the welding and heat treatment process are set, and after the microstructure simulation, the grain size distribution and the phase content are analyzed. Further, the Hall-Petch formula coupled with the grain size distribution correction coefficient and the grain size related power law hardening model are established to predict the yield strength and toughness. Finally, the process parameters are iteratively adjusted to achieve precise regulation of the strength and toughness. The present application solves the problem that the existing strength and toughness prediction technology does not fully consider the influence of microstructure, and significantly improves the performance reliability of the pipeline steel under service conditions.

Description

Technical Field

[0001] This invention belongs to the field of intelligent processing technology of metal materials, and specifically discloses a method for predicting and controlling the strength and toughness of pipeline steel. Background Technology

[0002] Pipeline transportation is a crucial method for transporting carbon dioxide in current carbon capture, utilization, and storage (CFS) technologies. During carbon dioxide pipeline transportation, numerous complex conditions such as low temperature and high pressure place extremely high demands on pipeline quality. On one hand, to achieve room-temperature liquid-phase (dense-phase) carbon dioxide transportation, the pressure inside the pipe needs to be maintained at a high pressure of 10-12 MPa, requiring the transportation pipeline to possess sufficient strength to prevent pipeline rupture and leakage. On the other hand, at special locations such as wellheads, carbon dioxide rapidly vaporizes from the liquid phase to the gas phase, and the thermal stress generated by the rapid temperature change repeatedly acts on the pipeline, easily causing microstructural damage and leading to pipeline failure. Therefore, the requirements for strength and toughness of pipeline steel, as the material used in transportation pipelines, are extremely stringent. The main method for controlling the strength and toughness of pipeline steel is to modify the heat treatment process.

[0003] Currently, the prediction of strength and toughness of pipeline steel mainly relies on traditional empirical formulas and simplified assumptions in mechanical models. Furthermore, it requires experimental adjustments to control strength and toughness, failing to fully consider microstructural interactions, resulting in low prediction accuracy and limited applicability. While modern numerical simulation and machine learning methods have improved prediction accuracy to some extent, they suffer from strong data dependence and poor interpretability. Current prediction methods also have shortcomings in considering the dynamic impact of microstructural evolution on strength and toughness.

[0004] This patent proposes a method for predicting and controlling the strength and toughness of pipeline steel. This method establishes a microstructure model of the welding and heat treatment processes. By analyzing the correspondence between the microstructure and the strength and toughness of pipeline steel, a strength and toughness prediction model for welded joints is established. Through process optimization, changes in microstructure and toughness are achieved, thereby enabling the regulation of the strength and toughness of pipeline steel materials. This method can reduce the cost of experimental regulation in traditional methods, improve the efficiency of adjusting the strength and toughness of pipeline steel welded joints, and thus achieve better and more accurate regulation of the strength and toughness of pipeline steel. Summary of the Invention

[0005] To address the aforementioned problems, this invention discloses a method for predicting and controlling the strength and toughness of pipeline steel. A prediction model is established by dynamically coupling the evolution of microstructure and process parameters, and a closed-loop optimization mechanism is constructed to achieve precise control of strength and toughness.

[0006] To achieve the above objectives, the present invention includes the following technical solutions:

[0007] A method for predicting and controlling the strength and toughness of pipeline steel, comprising the following steps:

[0008] (1) Obtain the physical property parameters of pipeline steel, including density, elastic modulus, Poisson's ratio, phase transformation start temperature, phase transformation end temperature, initial carbon content, thermal expansion coefficient, thermal conductivity, latent heat, hardness, heat capacity, thermal radiation coefficient and diffusion coefficient.

[0009] (2) Establish a micro-organizational regulation model:

[0010] Part 1: The weld fusion zone model was constructed using Procast software, which was completed through geometric modeling, mesh generation, material database settings, and nucleation parameter settings.

[0011] Part Two: The heat-affected zone model was constructed using Deform software by importing physical property parameters and setting up a dislocation density model and a recrystallization model.

[0012] (3) Set process boundary conditions based on the service environment, including welding temperature profile and heat treatment process parameters;

[0013] (4) Set the simulation step size and storage interval, and perform microstructure simulation;

[0014] (5) Analyze the content distribution, grain size and morphology of each phase in the simulation results, analyze the grain size, and mark the abnormal abrupt change areas;

[0015] (6) The grain size distribution is coupled to the finite element model, and the yield strength and toughness are predicted by the modified Hall-Petch formula and the power-law hardening model.

[0016] (7) Compare the simulated toughness with the expected value. If the target is not met, return to step (3) to adjust the process until the target is met.

[0017] Furthermore, in the above method, the physical property parameters in step (1) are generated by material property calculation software and saved as KEY format files.

[0018] Furthermore, in the first part of step (2) of the above method:

[0019] The mesh size is 1 / 3 of the recommended value in the Mesh module of the Procast software;

[0020] Nucleation parameters include volumetric nucleation density, surface nucleation density, and their corresponding mean and standard deviation of supercooling;

[0021] The dislocation density model uses the following formula:

[0022] .

[0023] In the formula: ρ is the dislocation density, r is the dynamic recovery softening coefficient, r0 is the softening constant, ε is the strain rate, ε0 is the strain rate correction coefficient, m is the strain rate sensitivity index, h is the work hardening rate coefficient, h0 is the hardening constant, Q is the activation energy, T is the Kelvin temperature, and R is the gas constant.

[0024] Furthermore, in the second part of step (2) of the above method:

[0025] The recrystallization model is static recrystallization, and the nucleation equation is:

[0026] .

[0027] In the formula: c is the nucleation parameter, H min V is the minimum distortion energy at the onset of static recrystallization. t Let Q be the probability that the material can continue to generate recrystallization nuclei at time t. act Nucleation activation energy, T is the Kelvin temperature, and R is the gas constant;

[0028] H is the current distortion energy, derived from the formula...

[0029] .

[0030] In the formula: C o Where μ is a constant, b is the shear modulus, and V is the Burgers vector. t Let t be the probability that the material can continue to generate recrystallization nuclei at time t, and ρ be the dislocation density.

[0031] Furthermore, in the above method, step (3):

[0032] The process boundary conditions include:

[0033] The molten zone model is set by initial temperature, heat transfer surface, and heat transfer coefficient;

[0034] The heat-affected zone model is achieved by scheduling heating rate, holding time, cooling rate, and ambient temperature.

[0035] Furthermore, in the above method, step (5) includes: grain size analysis, which involves statistically analyzing the maximum grain diameter. Minimum grain diameter, minimum grain diameter And calculate the grain size distribution correction factor f, the formula for calculating f is...

[0036] .

[0037] Furthermore, in the above method, step (6):

[0038] The modified Hall-Petch formula is as follows:

[0039] , where f is the grain size distribution correction factor;

[0040] The power-law hardening model is as follows:

[0041]

[0042] In the formula: True stress, For plastic strain, A is the strength coefficient; n is the strain hardening exponent.

[0043] Furthermore, in the above method, step (6):

[0044] Grain parameters are embedded into the finite element model using the UMAT subroutine. Tensile simulation is performed using C3D8R elements, and the resulting engineering stress-strain curves and P-CMOD curves are output.

[0045] Furthermore, in the above method, the finite element model includes:

[0046] Tensile specimen models are used to predict yield strength and tensile strength;

[0047] Notched specimen models are used to predict fracture toughness.

[0048] Furthermore, in the above method, the process adjustment rules in step (7) are as follows:

[0049] If the proportion of local abnormal grain size regions exceeds the threshold, the heat treatment temperature profile should be optimized.

[0050] If the strength and toughness are insufficient, increase the cooling rate or adjust the phase change temperature range.

[0051] Compared with the prior art, the present invention has the following outstanding advantages:

[0052] This invention proposes a method for predicting and controlling the strength and toughness of pipeline steel. This method innovates upon previous approaches to adjusting the strength and toughness of steel pipelines. It primarily involves simulating the microstructure by applying boundary conditions to the material, continuously modifying these boundary conditions to regulate the microstructure, and analyzing the correlation between the microstructure and the strength and toughness of the pipeline steel. This method not only reduces the cost of experimental control in traditional methods but also integrates the advantages of traditional empirical formulas, mechanical models, and machine learning, thus achieving better and more accurate regulation of the strength and toughness of pipeline steel. Attached Figure Description

[0053] Figure 1 This is a flowchart of the prediction and control method disclosed in this invention;

[0054] Figure 2This is a schematic diagram of the microscopic simulation model in the embodiment;

[0055] Figure 3 This also serves as a schematic diagram of the microscopic simulation model in the embodiments;

[0056] Figure 4 This is a schematic diagram illustrating the setting of boundary conditions and processing techniques for the model in this embodiment.

[0057] Figure 5 The examples show the microscopic simulation results, where a is the Procast microscopic simulation result and b is the Deform 3D microscopic simulation result.

[0058] Figure 6 In the example, ABAQUS finite element coupling model is shown, where a is the strength model and b is the toughness model.

[0059] Figure 7 This is a schematic diagram of stress and strain changes during the loading process in the embodiment;

[0060] Figure 8 This is a schematic diagram of the loading and fracture process in the embodiment. Detailed Implementation

[0061] The specific process for establishing a method for predicting and controlling the strength and toughness of pipeline steel based on microstructure is as follows: Figure 1 As shown:

[0062] (1) The first step is to determine the physical properties of the pipeline steel. This includes the density, elastic modulus, Poisson's ratio, onset temperature of each phase transformation, end temperature of each phase transformation, initial carbon content, coefficient of thermal expansion, thermal conductivity, latent heat, hardness, heat capacity, thermal radiation coefficient, and diffusion coefficient of the pipeline steel. These parameters can be calculated using Jmatpro (Procast software can also be used for calculation) and saved as a KEY file. The calculation process requires determining the material's composition parameters.

[0063] (2) The second step is to establish a micro-organizational regulation model. The model is built in two parts. The first part is the molten zone of the weld, which is implemented using Procast software. The specific steps are as follows: First, create a weld model in SolidWorks, save it as an IGS file, and then import it into the Mesh module of Procast for mesh generation. The mesh size is one-third of the software's recommended value. Next, enter the Cast module, open the manager, input the elemental composition parameters of the material to be simulated, establish the Material database, and set the initial material temperature. Then, set the heat coefficient based on the contact between the base metal and the weld and the actual heat transfer coefficient. Next, set the volume (volume core density), surface mucleation, and their respective mean undercooling and standard deviation. Specific values ​​are determined based on literature searches for the material. Finally, set the simulation parameters, setting Cells in a block and Cell Size-microns meters to appropriate values, determined based on specific material literature. Click Check to verify the data and confirm its accuracy.

[0064] The second part uses Deform 3D to model the heat-affected zone and subsequent overall heat treatment (this part first builds and processes the model of the heat-affected zone, without designing the simulation of heat treatment). First, enter the HeatTreatment module of Deform 3D to build the geometric model; then perform mesh generation (Mesh); the number of meshes is determined based on the size of the model; import the material KEY file calculated using Jmatpro, and check if the values ​​of material density, elastic modulus, Poisson's ratio, start temperature of each phase transition, end temperature of each phase transition, coefficient of thermal expansion, thermal conductivity, latent heat, hardness, heat capacity, thermal radiation coefficient, and diffusion coefficient are normal; the next step is to set the heating medium (Media). For the heat treatment process, it is necessary to set the heating furnace and cooling furnace, and define the heat transfer coefficient and radiation coefficient for each medium; the next step is to build the microstructure model. Enter the Microstructure module, open the model file created for Heat Treatment, select Add project, define (Define), and set the dislocation density:

[0065] The dislocation density is set by the formula

[0066] .

[0067] In the formula: ρ is the dislocation density, r is the dynamic recovery softening coefficient, r0 is the softening constant, ε is the strain rate, ε0 is the strain rate correction coefficient, m is the strain rate sensitivity index, h is the work hardening rate coefficient, and h0 is the hardening constant. Q is the activation energy, R is the gas constant, and T is the Kelvin temperature.

[0068] The recrystallization model for strength and toughness regulation is a static recrystallization model, and its nucleation model equation is...

[0069] .

[0070] In the formula: c is the nucleation parameter, H min V is the minimum distortion energy at the onset of static recrystallization. t Let Q be the probability that the material can continue to generate recrystallization nuclei at time t. act Nucleation activation energy, R is the gas constant, and T is the Kelvin temperature.

[0071] H is the current distortion energy, derived from the formula...

[0072] .

[0073] In the formula: C o Where μ is a constant, b is the shear modulus, and V is the Burgers vector. t This represents the molar volume of austenite.

[0074] The driving force for grain growth is given by the formula

[0075] .

[0076] In the formula: This represents the dislocation density difference.

[0077] The crystallization model is selected as static recrystallization; the nucleation condition is selected as the function of threshold dislocation density and probability, the nucleation probability is set according to the material, and the grain boundary migration velocity is set to a constant of 1; the natural flow stress of the material σ0, the elastic shear modulus, and the Burgers vector are set, while the other parameters A, a1, a2, and a3 are kept at their default values; the initial grain size and the initial dislocation density are set.

[0078] (3) The third step is to determine the processing technology and set the process boundary conditions of the model. The specific welding process is determined by the specific service environment of the material, and the temperature change curves of the welding and heat treatment process are obtained (which can be extracted by Abaqus). The obtained temperature curves are input into the model established in the second step in the form of thermal boundary conditions. The first part of the model is realized by setting the initial temperature, heat transfer surface and heat transfer coefficient. The second part of the model is realized by scheduling different media (including heating rate, heating time, holding time, cooling rate and ambient temperature).

[0079] (4) Fourth step: Set simulation parameters and perform microstructure simulation. Set appropriate simulation parameters for the established model, including the maximum and minimum step size, the number of steps to store each time (if the total number of steps is less than 3000, store once every five steps; if it exceeds 3000, store once every ten steps), the temperature change range for each step (the allowable temperature change for each step should not exceed 50℃), and the grain growth range. Submit the calculation. When submitting the calculation with Procast, check the CAFÉ calculation module. With Deform 3D, you can submit directly to obtain the microstructure results of the model under the set process.

[0080] (5) The fifth step is to analyze the content and distribution of each phase in the microstructure, as well as the size and morphology of the grains. When analyzing, it is important to distinguish between different phases. Different microstructures such as austenite, martensite, bainite, and pearlite correspond to different morphologies. It is necessary to accurately analyze the maximum and minimum size of the grains in each structure and mark the areas where the grain size suddenly increases or decreases.

[0081] (6) The sixth step is to couple the microstructure results to the finite element model to predict the strength and toughness of pipeline steel. The geometric model established in the second step is coupled using the UMAT subroutine of Abaqus. The obtained micrograin parameters are compiled into the subroutine, including the relationship between grain size and yield strength, the relationship between true stress and engineering stress, the relationship between true strain and engineering strain, and the relationship between the strength coefficient A, strain hardening exponent n, and grain size. These are determined by the following formulas:

[0082] The relationship between the yield strength and grain size of a material is given by the Hall-Petch formula. .

[0083] In the formula: It is the yield strength of the material; d is the lattice friction resistance generated by moving a single dislocation; K is the Hall-Petch constant; d is the average grain diameter.

[0084] When the grain size distribution is uneven, a grain size distribution correction factor f needs to be introduced. The formula for calculating f is...

[0085] .

[0086] In the formula: It is the maximum grain diameter. It is the minimum grain diameter

[0087] Therefore, the Hall-Petch formula is modified as follows:

[0088] .

[0089] The power-law hardening model is calibrated, and the power-law relationship between stress and plastic strain is established using the formula.

[0090] .

[0091] In the formula: True stress, For plastic strain, A is the strength coefficient; n is the strain hardening exponent.

[0092] The relationship between true stress and engineering stress is given by the formula

[0093] .

[0094] The relationship between true strain and engineering strain is given by the formula

[0095] .

[0096] The strength coefficient A and the strain hardening exponent n are related to the grain size distribution, and their relationship is given by the formula.

[0097] .

[0098] In the formula: , b and c are obtained from materials.

[0099] The power-law hardening formula considering grain size distribution is obtained by refining the formula.

[0100]

[0101] Based on the improved Hall-Petch formula for tensile analysis, a toughness analysis model based on the flexibility damage criterion was constructed. Simultaneously, the UMAT subroutine in ABAQUS software was used to assign the microscopic grain parameters obtained in the fifth step of the analysis to the corresponding regions for finite element simulation. Tensile and toughness specimen models were established in ABAQUS using a transitional mesh and C3D8R element type. For tensile simulation, the yield strength, tensile strength, and engineering stress-strain curves of the material were mainly obtained. For toughness simulation, the P-CMOD curve was mainly obtained. The strength and toughness of the material were obtained through the yield strength, tensile strength, engineering stress-strain curves, and P-CMOD curves.

[0102] (7) The seventh step is to compare the simulated material strength and toughness with the expected results and further adjust the process. If the material strength and toughness reach the expected results, it means that the process used is qualified; if the material strength and toughness do not reach the expected results, return to the third step to adjust the process, and then repeat steps three to seven until the material reaches the expected results and the strength and toughness are controlled.

[0103] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0104] Example 1

[0105] X80 pipeline steel was selected as the material for strength and toughness regulation. The processing technology and desired strength and toughness properties of the material were determined. The regulation model was established, and the specific steps are as follows:

[0106] (1). The density, elastic modulus, Poisson's ratio, start temperature of each phase transformation, end temperature of each phase transformation, initial carbon content of pipeline steel, coefficient of thermal expansion, thermal conductivity, latent heat, hardness, heat capacity, thermal radiation coefficient and diffusion coefficient of the material are calculated by inputting the material composition parameters through Jmatpro software. The obtained data is saved as .KEY format.

[0107] (2). Establish a microstructure control model. The first part is the molten zone of the weld, which is implemented by Procast software. The specific steps are as follows: Establish a weld model in SolidWorks. The actual weld shape is relatively complex. Here, a simplified square model is used to represent the weld area. Save it as IGS format and then import it into the Mesh module in Procast for mesh generation. The mesh size is one-third of the recommended value of the software. The recommended value of this model is 0.1, so 0.033 is used. Next, enter the Cast module, open the manager and input the elemental composition of the material to be simulated: Fe-98.18%, C-0.15%, Mn-1.33%, P-0.015%, S-0.0039%, Si-0.32%. Establish a Material database and name it 123. Set the initial material temperature to 2620℃. Set the heat coefficient to 55 W / (m·K). The volumetric parameters are ΔT. max =15k、ΔT σ =1k、n max =3.7×10 14 m -2 The surface kernel parameters are ΔT max =5k、ΔT σ =0.5k, n max =6×10 6 m -2 Set Cells in a block to 10 and Cell Size (microns meters) to X=5, Y=5, Z=5; click Check to verify the data and confirm it is correct. Figure 2

[0108] The second part uses Deform 3D to simulate the heat-affected zone and subsequent overall heat treatment. First, enter the Heat Treatment module of Deform 3D. A 10mm × 10mm × 10mm square is created to represent the heat-affected zone; the mesh size is 32000. Import the material KEY file calculated using Jmatpro and check if the values ​​of material density, elastic modulus, Poisson's ratio, start temperature of each phase transition, end temperature of each phase transition, coefficient of thermal expansion, thermal conductivity, latent heat, hardness, heat capacity, thermal radiation coefficient, and diffusion coefficient are normal. Next, set the heating medium. The heat-affected zone mainly includes heat conduction with the weld and heat exchange with the air. The heat conduction coefficient with the weld is set to 55 W / (m·K), and the heat exchange coefficient with the air is set to 5 W / (m·K). 2 ·K); The next step is to build a microscopic model. Enter the Microstructure module, open the model file created by Heat Treatment, select Add Project, define it, and set the dislocation density according to the following formula:

[0109] Dislocation density

[0110] .

[0111] Model equations

[0112] .

[0113] Current distortion energy

[0114] .

[0115] Driving force of grain growth

[0116] .

[0117] The dislocation density parameters are set as follows: ε0=1, r0=2000, Q=220000, K=0.005, h0=0.00075, m=0.2; nucleation probability is set to 0.02; grain boundary migration constant is set to 1; flow stress is set to temperature-dependent values ​​of 100℃-150000000 Pa and 800℃-100000000 Pa; elastic shear modulus is set to 5e10 Pa; Burgers vector is set to 3e-10; and the remaining parameters A, a1, a2, and a3 remain at their default values ​​of 1, 1, 0.1, and 0.1, respectively. The initial grain size is 20 μm, and the initial dislocation density is 5e12 cm⁻¹. -2 Setup complete. Figure 3 .

[0118] (3). Set boundary conditions and processing techniques for the established model. For the cube model, fix its bottom surface to match the actual scene during the simulation. Add the actual processing techniques. For the Procast model, set the top surface to exchange heat with the air, and the bottom surface and four sides to conduct heat with the base material. For the Deform 3D model, schedule the temperature curves of welding and heat treatment using the medium added in b. to simulate the temperature flow of welding and heat treatment. Figure 4 .

[0119] (4) Set the simulation parameters, determine the step size and save time interval during the simulation process. Check the CAFÉ (post) option in Procast, and keep the other settings as default; set the Deform 3D simulation parameters Temp.change per step to 5, Min / Max time per step to 0.01 / 10, and Step increment to save to 5. After checking that there are no errors, submit the calculation.

[0120] (5). After obtaining the calculation results, analyze the results, count the size of the grains, record the largest and smallest grains, and calibrate the regions where the grain size changes abruptly.

[0121] The obtained microscopic simulation results are as follows Figure 5

[0122] Figure 5 'a' represents the Procast microscopic simulation result. Figure 5 b represents the results of Deform 3D microscopic simulation. These results are analyzed and statistically processed using their respective post-processing tools to obtain the analysis results of grain size and distribution, which are then saved.

[0123] (6) Obtain the microscopic simulation analysis results, establish a finite element coupled model, and couple the microscopic simulation results into the finite element model. This process involves establishing tensile and toughness pattern models using ABAQUS (e.g., Figure 6 The obtained grain size and distribution data are compiled using the UMAT subroutine in ABAQUS to couple with the material's yield strength and toughness. The specific formula is as follows:

[0124] Hall-Petch correction formula

[0125] .

[0126] Power-law hardening formula considering grain size distribution

[0127] .

[0128] Figure 6 6a represents the strength model, and 6b represents the toughness model. After coupling the microscopic simulation results into the models, the two models are loaded and their strength and toughness are tested. Figure 7 For stress-strain changes during loading, Figure 8 For loading the fracture process.

[0129] (7). Analysis of the simulation results of the finite element coupled model shows that the strength of the material is improved in regions with finer and more uniform grain size. According to the Hall-Petch relationship, grain refinement can increase grain boundary density, making the crack propagation path more tortuous and the energy absorption rate higher, thereby hindering the movement of dislocations in the grains and improving the strength of the material. Moderate grain refinement can simultaneously improve toughness, because fine grains can delay crack propagation. Excessively fine grains (such as nanoscale) may lead to brittleness due to restricted dislocation activity. Local coarse grains or mixed grain structures are prone to stress concentration, becoming crack initiation sources and significantly reducing toughness. Through result analysis, it is determined whether the material's strength and toughness meet the actual requirements. If they meet the actual requirements, the determined process is put into use; if they do not meet the actual requirements, steps three to seven are repeated until they meet the requirements.

[0130] The simulated yield and tensile strengths were 480 MPa and 583 MPa, respectively. The crack initiated due to stress concentration at the machined notch and propagated along the stress concentration direction with increasing tensile load. The propagation direction may change due to the influence of grain structure, until the specimen's load-bearing capacity decreased and it fractured. The maximum stress in the cover weld specimen from the start of loading to fracture was 1082 MPa, indicating high strength. In summary, the material strength and toughness obtained through this simulation process meet practical requirements and can therefore be used in production.

[0131] The above are merely a few preferred embodiments of the present invention, described in a relatively specific and detailed manner, but they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention.

Claims

1. A method for predicting and controlling the strength and toughness of pipeline steel, characterized in that, Includes the following steps: (1) Obtain the physical property parameters of pipeline steel, including density, elastic modulus, Poisson's ratio, phase transformation start temperature, phase transformation end temperature, initial carbon content, thermal expansion coefficient, thermal conductivity, latent heat, hardness, heat capacity, thermal radiation coefficient and diffusion coefficient. (2) Establish a micro-organism prediction and coupling model: This model consists of two parts. It can simulate the microstructure of pipeline steel under set process conditions. Its main function is to visualize the changes in microstructure during material processing and to establish a molecular dynamics model that couples dislocation surface density and nucleation activation energy. It innovatively realizes the function of coupling microstructure with strength and toughness. Part 1: Establishing a Model of the Weld Melting Zone Create a weld geometry model in SolidWorks and import it into the Mesh module of Procast software for mesh generation; set the material database, initial temperature, heat transfer coefficient, volumetric core density, surface core density and their corresponding mean and standard deviation of supercooling in the Cast module; after setting the simulation parameters, check the CAFÉ calculation module to build the weld melting zone model; Part Two: Establishing a Model of the Heat-Affected Zone In the Heat Treatment module of Deform 3D, perform geometric modeling and mesh generation, import the physical property parameters from step (1), and set the heat exchange coefficient and radiation coefficient of the heating medium; enter the Microstructure module to set the dislocation density model and recrystallization model, and construct the heat-affected zone model. (3) Determine process parameters based on the service environment, and assign the determined process to the simulation model in the form of boundary conditions, including welding temperature curves and heat treatment process parameters. (4) Set the simulation step size and the number of stored interval steps to perform microstructure simulation; (5) Analyze the content distribution, grain size and morphology of each phase in the simulation results, and mark the regions of abrupt changes in grain size and morphology in the simulation results; (6) Couple the grain size distribution to the finite element model, establish a strength and toughness prediction model, and write the UMAT subroutine in ABAQUS based on the power law hardening model to predict the yield strength and toughness of the material. (7) Compare the simulated toughness with the expected value. If the target is not met, return to step (3) to adjust the process until the target is met.

2. The method according to claim 1, characterized in that, In step (1), the mechanical properties are obtained through tensile and hardness tests, and the high-temperature thermal properties are generated and saved as KEY format files using the material properties calculation software Jmatpro.

3. The method according to claim 1, characterized in that, In the first part of step (2): The mesh size is 1 / 3 of the recommended value in the Mesh module of the Procast software; Nucleation parameters include volumetric nucleation density, surface nucleation density, and their corresponding mean and standard deviation of supercooling; The dislocation density model uses the following formula: In the formula: ρ is the dislocation density, r is the dynamic recovery softening coefficient, r0 is the softening constant, ε is the strain rate, ε0 is the strain rate correction coefficient, m is the strain rate sensitivity index, h is the work hardening rate coefficient, h0 is the hardening constant, Q is the activation energy, R is the gas constant, and T is the Kelvin temperature.

4. The method according to claim 1, characterized in that, In step (2), part two: The recrystallization model is static recrystallization, and the nucleation equation is as follows: In the formula: c is the nucleation parameter, H min V is the minimum distortion energy at the onset of static recrystallization. t Let Q be the probability that the material can continue to generate recrystallization nuclei at time t. act Nucleation activation energy, R is the gas constant, T is the Kelvin temperature; H is the current distortion energy, derived from the formula... In the formula: C o Where μ is a constant, b is the shear modulus, and V is the Burgers vector. t ρ is the molar volume of austenite, and ρ is the dislocation density.

5. The method according to claim 1, characterized in that, In step (3): The process boundary conditions include: The molten zone model is set by initial temperature, heat transfer surface, and heat transfer coefficient; The heat-affected zone model is achieved by scheduling heating rate, holding time, cooling rate, and ambient temperature.

6. The method according to claim 1, characterized in that, In step (5): Grain size analysis includes statistically analyzing the maximum grain diameter d. max Minimum grain diameter d min And calculate the grain size correction factor f, the formula for calculating f is... d is the average grain diameter.

7. The method according to claim 1, characterized in that, In step (6): The revised Hall-Petch formula is as follows: Where f is the grain size correction factor and K is the Hall-Petch constant. It is the yield strength of the material; It is the lattice friction resistance generated by the movement of a single dislocation, and d is the average grain diameter; The power-law hardening model is as follows: In the formula: True stress, For plastic strain, A is the strength coefficient; n is the strain hardening exponent.

8. The method according to claim 1, characterized in that, In step (6): Grain parameters are embedded into the finite element model using the UMAT subroutine. Tensile simulation is performed using C3D8R elements, and the resulting engineering stress-strain curves and P-CMOD curves are output.

9. The method according to claim 8, characterized in that, The finite element model includes: Tensile specimen models are used to predict yield strength and tensile strength; Notched specimen models are used to predict fracture toughness.

10. The method according to claim 1, characterized in that, The process adjustment rules in step (7) are as follows: If the proportion of localized abnormal grain size regions exceeds the threshold, the welding process should be changed and the heat treatment temperature profile optimized. If the strength and toughness are insufficient, increase the cooling rate of the weld and the temperature profile of the heat treatment.

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