Method for predicting and controlling obdurability of pipeline steel

Abstract

The invention belongs to the technical field of intelligent processing of metal materials, and particularly discloses a method for predicting and controlling the obdurability of pipeline steel. The method comprises the steps that firstly, physical property parameters of the pipeline steel are obtained, and a microstructure regulation and control model containing a weld joint melting zone and a heat affected zone is established; setting welding and heat treatment process boundary conditions based on a service environment, executing microstructure simulation, and analyzing grain size distribution and each phase content; further establishing a Hall-Petch formula coupled with a grain size distribution correction coefficient and a grain size related power-law hardening model to predict yield strength and toughness; and finally, precise regulation and control of the obdurability are realized by iteratively adjusting process parameters. The method solves the problem that an existing obdurability prediction technology does not fully consider the influence of the microstructure, and the performance reliability of the pipeline steel under the service working condition is remarkably improved.

Description

TECHNICAL FIELD

[0001] The 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. BACKGROUND

[0002] Pipeline transportation is an important way of carbon dioxide transportation in the current carbon capture, utilization and storage technology. In the process of carbon dioxide pipeline transportation, many complex working conditions such as low temperature and high pressure put high requirements on the quality of the pipeline. On the one hand, in order to realize the normal temperature liquid phase (dense phase) transportation of carbon dioxide, the pressure in the pipe needs to be maintained at a high pressure of 10-12 MPa, which requires the transportation pipeline to have sufficient strength to prevent pipeline rupture and leakage. On the other hand, at special positions such as wellheads, carbon dioxide will rapidly vaporize from liquid phase to gas phase, and the thermal stress generated by the rapid change of temperature repeatedly acts on the pipeline, which easily causes microstructure damage and leads to pipeline damage. Therefore, the pipeline steel as the material of the transportation pipeline has extremely strict requirements on the strength and toughness, and the method for regulating and controlling the strength and toughness of the pipeline steel is mainly to realize the regulation and control of the strength and toughness by changing the heat treatment process of the pipeline steel.

[0003] At present, the prediction of the strength and toughness of the pipeline steel mainly relies on the traditional empirical formula and the mechanical model relying on simplified assumptions, and in addition, the test needs to be combined to regulate and control the strength and toughness, which cannot fully consider the interaction of the microstructure, has low prediction accuracy and limited application range. Although modern numerical simulation and machine learning methods improve the prediction accuracy to some extent, they have the problems of strong data dependence and poor interpretability. The current prediction method also has deficiencies in considering the dynamic influence of microstructure evolution on the strength and toughness.

[0004] The patent proposes a method for predicting and controlling the strength and toughness of pipeline steel, which establishes a microstructure model of the welding and heat treatment process, analyzes the corresponding relationship between the microstructure and the strength and toughness of the pipeline steel, establishes a strength and toughness prediction model of the welded joint, and realizes the change from the microstructure to the strength and toughness through the optimization of the process, so as to realize the regulation and control of the strength and toughness of the pipeline steel. This method can reduce the cost of test regulation in the traditional method, improve the regulation efficiency of the strength and toughness of the pipeline steel welded joint, and better and more accurately realize the better regulation of the strength and toughness of the pipeline steel. SUMMARY

[0005] In view of the above problems, the application discloses a method for predicting and controlling the strength and toughness of pipeline steel, which establishes a prediction model by dynamically coupling microstructure evolution and process parameters, and realizes accurate regulation and control of the strength and toughness through a closed-loop optimization mechanism.

[0006] In order to achieve the above purpose, the application includes the following technical solutions:

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

[0008] (1) Obtain the physical parameters of the pipeline steel, including density, elastic modulus, Poisson's ratio, phase transition start temperature, phase transition 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 microstructure control model:

[0010] The first part: using Procast software to build the weld fusion zone model, through geometric modeling, mesh division, material database setting, nucleation parameter setting is completed;

[0011] The second part: using Deform software to build the heat affected zone model, by importing physical parameters, setting dislocation density model and recrystallization model is completed;

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

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

[0014] (5) Analyze the phase content distribution, grain size and morphology in the simulation results, analyze the grain size, and mark the abnormal mutation area;

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

[0016] (7) Compare the simulated strength and toughness with the expected value, and return to step (3) to adjust the process until the standard is met.

[0017] Further, in the above method, the physical parameters in step (1) are generated by a material performance calculation software and saved as a KEY format file.

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

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

[0020] The nucleation parameters include bulk nucleation density, surface nucleation density, and their corresponding average undercooling and standard deviation;

[0021] The dislocation density model uses the formula:

[0022] dρ i =(h-rρ i )d ε

[0023]

[0024] wherein p is the dislocation density, R is the dynamic recovery softening coefficient, r0 is the softening constant, e is the strain rate, e0 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 temperature in Kelvin, and r is the reaction rate index.

[0025] Further, in the step (2), the second part of the above method:

[0026] The recrystallization model is static recrystallization, and the nucleation equation is:

[0027]

[0028] wherein c is the nucleation parameter, H min is the minimum distortion energy at the time when the static recrystallization starts, V t is the probability that the material can continue to generate recrystallization nuclei at t, Q act is the activation energy of nucleation, T is the temperature in Kelvin, and R is the gas constant;

[0029] H is the current distortion energy, which is calculated by the formula

[0030] H=C0·p·μ·b 2 ·V t

[0031] wherein C o is a constant, μ is the shear modulus value, b is the Burgers vector, V t is the probability that the material can continue to generate recrystallization nuclei at t, and p is the dislocation density.

[0032] Further, in the step (3) of the above method:

[0033] The process boundary conditions include:

[0034] The melting zone model is set by the initial temperature, the heat exchange surface, and the heat exchange coefficient;

[0035] The heat affected zone model is realized by scheduling the heating rate, the holding time length, the cooling rate, and the environment temperature.

[0036] Further, in the step (5) of the above method: the grain size analysis includes counting the maximum grain diameter d max is the minimum grain diameter, the minimum grain diameter d min , and calculating a grain size distribution correction coefficient f, the calculation formula of f is

[0037]

[0038] Further, in the step (6) of the above method:

[0039] The modified Hall-Petch formula is:

[0040] σ y = σ0 + Kfd -1 / 2 Wherein f is a grain size distribution correction coefficient;

[0041] The power law hardening model is:

[0042]

[0043] In the formula: σ z Is the true stress, epsilon p Is the plastic strain, A is the strength coefficient;n is the strain hardening index.

[0044] Further, in the above method, in step (6):

[0045] The grain parameters are embedded into the finite element model through the UMAT subroutine, the C3D8R unit is used for tensile simulation, and the engineering stress-strain curve and P-CMOD curve are output.

[0046] Further, in the above method, the finite element model comprises:

[0047] The tensile specimen model is used for predicting the yield strength and tensile strength;

[0048] The notched specimen model is used for predicting the fracture toughness.

[0049] Further, in the above method, in step (7), the process adjustment rule is:

[0050] If the proportion of the local grain size abnormal area exceeds the threshold value, the heat treatment temperature curve is optimized;

[0051] If the strength and toughness are insufficient, the cooling rate or the phase transformation temperature range is adjusted.

[0052] Compared with the prior art, the present application has the following outstanding beneficial effects:

[0053] The present application provides a kind of strength and toughness prediction and control method of pipeline steel, this method mainly is the strength and toughness adjustment mode of previous steel pipeline is innovated.Mainly by exerting boundary condition to material organization simulation, by constantly changing boundary condition to microstructure is regulated, by analyzing the corresponding relationship between microstructure and pipeline steel strength and toughness, to realize the regulation of pipeline steel material strength and toughness, this method not only can reduce the cost of test regulation in traditional method, can also comprehensively traditional experience formula and mechanics model and the advantage of machine learning, to better and more accurately realize the better adjustment of pipeline steel strength and toughness. BRIEF DESCRIPTION OF DRAWINGS

[0054] Figure 1 Flow chart of the prediction and control method disclosed in the present application;

[0055] Figure 2 Schematic diagram of the microstructure simulation model in the embodiment;

[0056] Figure 3 Schematic diagram of the microstructure simulation model in the embodiment;

[0057] Figure 4 Schematic diagram of setting boundary conditions and processing technology for the model in the embodiment;

[0058] Figure 5 Microstructure simulation results in the embodiment, a is the Procast microstructure simulation result, and b is the Deform 3D microstructure simulation result;

[0059] Figure 6 ABAQUS finite element coupling model in the embodiment, a is the strength model, and b is the toughness model;

[0060] Figure 7 Schematic diagram of stress-strain change in the loading process in the embodiment;

[0061] Figure 8 Schematic diagram of the loading fracture process in the embodiment. DETAILED DESCRIPTION

[0062] The specific flow of establishing the strength and toughness prediction and control method of the pipeline steel based on the microstructure is shown in Figure 1

[0063] (1) The first step is to determine the physical property parameters of the pipeline steel. The parameters include the density, elastic modulus, Poisson's ratio, start temperature of each phase change, end temperature of each phase change, initial carbon content of the pipeline steel, thermal expansion coefficient, thermal conductivity, latent heat, hardness, heat capacity, thermal radiation coefficient, and diffusion coefficient. These parameters can be calculated by Jmatpro (Procast software can also be used for calculation) and saved as a KEY file. The calculation process needs to determine the component parameters of the material.

[0064] ​(2) The second step is to establish the microstructure control model. The model is divided into two parts. The first part is the fusion zone of the weld, which is realized by Procast software. The specific steps are as follows: establish the weld model in SolidWorks, save it as IGS format, then import it into the Mesh module of Procast for meshing. The mesh size is one third of the recommended value of the software. Next, enter the Cast module, open the manager, input the element composition parameters of the simulated material, establish the Material database, set the initial material temperature, set the Heat coefficient, set the Volume, Surface mucleation, and their respective Mean undercooling and Standard deviation according to the contact between the weld and the base material and the actual heat transfer coefficient. The specific values are determined according to literature retrieval of the material. Set the Cells in ablock and Cell Size-microns meters to appropriate values, which are determined according to the literature of the specific material. Click Check to check the data, and confirm that there is no error.

[0065] The second part uses Deform 3D to model the heat-affected zone and subsequent overall heat treatment (this place is to establish the model of the heat-affected zone first, without designing the simulation of heat treatment). First, enter the HeatTreatment module of Deform 3D, and establish the geometry model. Then, mesh the model. The number of meshes is determined based on the size of the model. Import the material KEY file calculated by Jmatpro, check whether the values of material density, elastic modulus, Poisson's ratio, start temperature of each phase transition, end temperature of each phase transition, thermal expansion coefficient, thermal conductivity, latent heat, hardness, heat capacity, thermal radiation coefficient, and diffusion coefficient are normal. Next, set the heating medium (Media). For the heat treatment process, the heating furnace and the cooling furnace need to be defined with heat transfer coefficient (Heat transfer coefficient) and radiation coefficient (Radiation). Next, establish the microstructure model by entering the Microstructure module and opening the model file established by Heat Treatment. Select Add project, Define, and set the dislocation density:

[0066] where the dislocation density is set by the formula

[0067] dρ i = (h - rρ i )d ε

[0068]

[0069] where: p 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, and Q is the activation energy.

[0070] The recrystallization model of the strength and toughness regulation is static recrystallization, and the nucleation model equation of the static recrystallization is

[0071]

[0072] where: c is the nucleation parameter, H min is the minimum distortion energy at the beginning of the static recrystallization, V t is the probability of the material at time t that can continue to generate recrystallization nuclei, and Q act is the nucleation activation energy.

[0073] H is the current distortion energy, which is calculated by the formula

[0074] H = C0·p·μ·b 2 ·V t

[0075] where: C o is a constant, μ is the shear modulus value, b is the Burgers vector, and V t is the molar volume of austenite.

[0076] The driving force of the grain growth is calculated by the formula

[0077] P = a·μ·b 2 ·Δp

[0078] where: Δp is the dislocation density difference.

[0079] Crystallization model Static Recrystallization; Nucleation condition Function of threshold dislocation density and probability, Probability of nucleation according to material, Grain boundary migration velocity set as constant 1; Set the natural flow stress of the material, Elastic shear modulus and Burgers vector, and the rest of the parameters A, a1, a2, a3 remain the default values; Set the initial grain size and initial dislocation density.

[0080] (3) The third step is to determine the processing technology and set the model process boundary conditions. The specific welding process is determined by the specific service environment of the material, and the temperature change curve of the welding and heat treatment process is obtained (which can be extracted by Abaqus). The obtained temperature curve is input into the model established in the second step as a thermal boundary condition. The first part of the model is achieved by setting the initial temperature and heat transfer surface and heat transfer coefficient, and the second part of the model is achieved by scheduling different media (including heating rate, heating time, holding time, cooling rate, and ambient temperature).

[0081] (4) The fourth step is to set the simulation parameters and perform microstructure simulation. The established model is set with appropriate simulation parameters, including the maximum and minimum step lengths, the interval step number stored each time (if the total number of steps is less than 3000, store every five steps; if it exceeds 3000, store every ten steps), the temperature change range of each step (the allowed temperature change of each step should not exceed 50℃), and the grain growth range. Submit the calculation, and check the Calculation module, Deform 3D directly submits, and the microstructure results of the model under the set process are obtained.

[0082] (5) The fifth step is to analyze the phase content and distribution of the microstructure, as well as the size and morphology of the grains. When analyzing, it is important to distinguish between different phases, such as austenite, martensite, bainite, and pearlite, which correspond to different microstructure morphologies. It is necessary to accurately analyze the maximum and minimum sizes of each grain, and mark the areas where the grain size increases or decreases sharply.

[0083] (6) The sixth step is to couple the microstructure results to the finite element model to realize the prediction of the strength and toughness of the pipeline steel. The geometric model established in the second step is coupled through the UMAT subroutine of Abaqus to compile the obtained micro-grain parameters into the subroutine, including the relationship between the grain size and the yield strength, the relationship between the true stress and the engineering stress, the relationship between the true strain and the engineering strain, and the relationship between the strength coefficient A and the strain hardening index n and the grain size. The yield strength of the material is determined by the following formula:

[0084] The corresponding relationship between the yield strength of the material and the grain is determined by the Hall-Petch formula

[0085]

[0086] In the formula: σ y is the yield strength of the material; σ0 is the lattice friction resistance generated by moving a single dislocation; K is the Hall-Petch constant; and d is the average grain diameter.

[0087] When the grain size distribution and its unevenness are introduced, a grain size distribution correction coefficient f needs to be introduced, and the calculation formula of f is

[0088]

[0089] In the formula: d max is the maximum grain diameter, d min is the minimum grain diameter

[0090] Therefore, the correction of the Hall-Petch formula is

[0091] σ y = σ0 + Kfd -1 / 2

[0092] The power-law hardening model is calibrated to establish the power-law relationship between the stress and the plastic strain, and the formula is

[0093]

[0094] In the formula: σ z is the true stress, ε p is the plastic strain, A is the strength coefficient, and n is the strain hardening index.

[0095] The relationship between the true stress and the engineering stress is determined by the formula

[0096] σ z = σ e (1 + ε e )

[0097] The relationship between the true strain and the engineering strain is determined by the formula

[0098] ε z = ln(1+ε e )

[0099] The strength coefficient A and the strain hardening index n are related to the grain size distribution, and the relationship therebetween is obtained by the formula

[0100]

[0101] In the formula, n1, n2, b and c are obtained from the material

[0102] The power-law hardening formula considering the grain size distribution is obtained by arrangement

[0103]

[0104] Based on the improved Hall-Petch formula tensile model, a toughness analysis model based on the flexible damage criterion is constructed, and the micro-grain parameters obtained in the fifth step are assigned to the corresponding region for finite element simulation by using the UMAT subprogram in ABAQUS software. The tensile and toughness sample models are established in ABAQUS, the transition mesh is adopted, the C3D8R unit type is adopted, the yield strength, tensile strength and engineering stress-strain curve of the material are mainly obtained for the tensile simulation, and the P-CMOD curve is mainly obtained for the toughness simulation, and the strength and toughness of the material are obtained through the yield strength, tensile strength, engineering stress-strain curve and P-CMOD curve.

[0105] (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 for process adjustment, and then repeat steps three to seven until the material reaches the expected results, and the strength and toughness are adjusted.

[0106] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0107] Embodiment 1

[0108] X80 pipeline steel is used as the material to be controlled in strength and toughness, the treatment process of the material and the desired material strength and toughness performance are determined, the establishment of the control model is carried out, and the specific steps are as follows:

[0109] (1). The density, elastic modulus, Poisson's ratio, starting temperature of each phase change, ending temperature of each phase change, initial carbon content of pipeline steel, thermal expansion coefficient, thermal conductivity, latent heat, hardness, heat capacity, thermal radiation coefficient, diffusion coefficient of the material are calculated by inputting the component parameters of the material into the Jmatpro software, and the obtained data are saved in the.KEY format.

[0110] (2). A microstructure control model is established, the first part being the fusion zone of the weld, which is realized by Procast software, the specific steps being: a weld model is established in SolidWorks, the actual weld shape being relatively complex, here a simplified square model is used to represent the weld area, saved in IGS format, then imported into the Mesh module in Procast for meshing, the mesh size being one-third of the recommended value of the software, the recommended value of this model being 0.1, therefore 0.033 is used, the next step being to enter the Cast module, opening the manager (manager) to input the element 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%, establishing a Material database (material library) named 123, setting the initial material temperature to 2620℃; the heat transfer coefficient is set to 55 W / (m·K), the bulk nucleation parameters being ΔT max = 15k, ΔT σ = 1k, n max = 3.7×10 14 m -2 , the surface nucleation parameters being ΔT max = 5k, ΔT σ = 0.5k, n max = 6×10 6 m -2 ; Cells in a block is set to 10 and Cell Size-micronsmeters is set to X=5, Y=5, Z=5; click Check to check the data, and confirm that there is no error. Figure 2

[0111] 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, and build a 10mm x 10mm x 10mm square model representing the heat affected zone. The grid is divided into 32000. Import the material KEY file calculated by Jmatpro, check whether the values of material density, elastic modulus, Poisson's ratio, start temperature of each phase change, end temperature of each phase change, thermal expansion coefficient, 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 air. The heat conduction coefficient with the weld is set to 55 W / (m·K), and the heat exchange coefficient with air is 5 W / (m 2 ·K). Next, build the microstructure model. Enter the Microstructure module, open the model file established by Heat Treatment, select Add Items, define, set the dislocation density, and set it according to the following formula:

[0112] Dislocation density

[0113] dρ i =(h-rρ i )d ε

[0114]

[0115] Model equation

[0116]

[0117] Current distortion energy

[0118] H=C0·ρ·μ·b 2 ·V t

[0119] Driving force of grain growth

[0120] P=a·μ·b 2 ·Δρ

[0121] The dislocation density parameter is set to ε0=1, r0=2000, Q=220000, K=0.005, h0=0.00075, m=0.2, the nucleation probability is set to 0.02, the grain boundary migration constant is set to 1, the flow stress is set to the temperature-dependent value 100℃-150000000Pa, 800℃-100000000Pa, the elastic shear modulus is set to 5e10Pa, the Burgers vector is set to 3e-10, and the remaining parameters A, a1, a2, a3 remain the default values 1, 1, 0.1, 0.1, the initial grain size is 20μm, and the initial dislocation density is 5e12 cm -2 , the setting is completed. As Figure 3 .

[0122] (3). The boundary conditions and processing parameters of the established model are set. For the cubic model, the bottom surface is fixed to match the actual scene during simulation, and the actual processing parameters are added. For the Procast model, the upper surface is set to exchange heat with air, and the lower bottom and four sides are set to conduct heat with the base material. For the Deform 3D model, the welding and heat treatment temperature curves are scheduled through the medium added in b. to realize the simulation of the welding and heat treatment temperature process. As Figure 4 .

[0123] (4). The simulation parameters are set to determine the step size and save time interval during simulation. The Procast model checks (post) option, and the remaining settings remain default; the Deform 3D simulation parameters Temp. change per step is set to 5, Min / Max time per step is set to 0.01 / 10, Step increment to save is set to 5, and the check is correct. Submit operation.

[0124] (5). After obtaining the calculation results, the results are analyzed, the size of the grains is counted, the maximum and minimum grains are recorded, and the regions with grain size mutation are marked.

[0125] The micro-simulation results obtained are as follows Figure 5

[0126] Figure 5 a is the Procast micro-simulation result, Figure 5 b is the Deform 3D micro-simulation result, which is analyzed and counted through the respective post-processing tools to obtain the analysis results of the grain size and distribution, and is saved.

[0127] (6). Obtain the micro-simulation analysis results, establish a finite element coupling model, and couple the micro-simulation results to the finite element model. This process is to establish a tensile pattern and a toughness pattern model through ABAQUS (as shown in FIG. 8), compile the obtained grain size and distribution data through the UMAT subroutine in ABAQUS, realize the coupling with the material yield strength and toughness, and the specific formula is as follows: Figure 6

[0128] Hall-Petch modified formula

[0129] σ y = σ0+ Kfd -1 / 2

[0130] Power-law hardening formula considering grain size distribution

[0131]

[0132] Figure 6 a is the strength model and 6b is the toughness model. After coupling the micro-simulation results to the model, the two models are loaded to test their strength and toughness. Figure 7 is the stress-strain change in the loading process, Figure 8 is the fracture process in the loading process.

[0133] (7). Through the simulation results analysis of the finite element coupling model, it is obtained that the region with refined and uniform grain size, and the strength of the material is improved. According to the Hall-Petch relationship, it can be known that grain refinement can increase the grain boundary density, the crack propagation path is tortuous, the energy absorption rate is high, thereby hindering the dislocation movement of the grain, so that the strength of the material is improved. Moderate grain refinement can simultaneously improve the toughness. Fine grains (such as nanoscale) may have a tendency to brittleness due to limited dislocation activity. Local coarse grains or mixed grain structures are easy to cause stress concentration and become the source of crack initiation, which significantly reduces the toughness. Through the result analysis, it is judged whether the strength and toughness of the material meet the actual demand. If it meets the actual demand, the determined process is put into use; if it does not meet the actual demand, steps three to seven are repeated until the requirement is met.

[0134] Through the analysis and simulation, the yield strength and tensile strength are 480 MPa and 583 MPa respectively. The crack is initiated from the machining notch due to stress concentration, and expands along the stress concentration direction with the increase of the tensile load. The expansion direction may change due to the influence of the grain structure, until the sample is fractured due to the decrease of the bearing capacity. The maximum stress of the cover welding sample in the process from the start of loading to the fracture is 1082 MPa, which indicates that it has high strength. Based on the above analysis, the strength and toughness of the material obtained by the process in this simulation meet the actual demand, so it can be put into production and use.

[0135] ​The above merely describes several preferred embodiments of the present application in a more specific and detailed manner, but should not be understood as a limitation to the patent scope of the present application. It should be noted that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application.

Claims

1. A method for prediction and control of strength and toughness of a pipeline steel, characterized by, Comprising the following steps: (1) Obtain the physical property parameters of the pipeline steel, including density, elastic modulus, Poisson's ratio, phase change start temperature, phase change end temperature, initial carbon content, thermal expansion coefficient, thermal conductivity, latent heat, hardness, heat capacity, thermal radiation coefficient and diffusion coefficient; (2) Establish a microstructure prediction and coupling model: This model has two parts, which can realize the simulation of the microstructure of the pipeline steel under the set process conditions, the main function is to visualize the microstructure change of the material processing process, and to establish a molecular dynamics model coupling dislocation surface density and nucleation activation energy, which innovatively realizes the coupling function of microstructure and strength and toughness; The first part: adopt Procast software to build the weld fusion zone model, complete through geometric modeling, mesh division, material database setting, nucleation parameter setting; The second part: adopt Deform software to build the heat affected zone model, complete by importing physical property parameters, setting dislocation density model and recrystallization model; (3) Determine the process parameters based on the service environment, assign the determined process to the simulation model in the form of boundary conditions, including welding temperature curve and heat treatment process parameters; (4) Set the simulation step and the interval step number of storage, and perform microstructure simulation; (5) Analyze the phase content distribution, grain size and morphology in the simulation results, and mark the grain size and morphology mutation area in the simulation results; (6) Coupling the grain size distribution to the finite element model, establishing a strength and toughness prediction model, and writing a 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 strength and toughness with the expected value, and return to step (3) to adjust the process until it meets the standard.

2. The method of claim 1, wherein, In step (1), the mechanical properties are obtained by tensile test and hardness test, and the high-temperature thermal properties are generated by material performance calculation software Jmatpro and saved as KEY format file.

3. The method of claim 1, wherein, In step (2) the first part: The mesh size is 1 / 3 of the recommended value in the Mesh module of Procast software; The nucleation parameters include bulk nucleation density, surface nucleation density, and their corresponding average undercooling and standard deviation; The dislocation density model uses the formula: dρ i = (h - rρ i )d ε 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, and Q is the activation energy.

4. The method of claim 1, wherein, In step (2) the second part: The recrystallization model is static recrystallization, and the nucleation equation is as follows: wherein: c is a nucleation parameter, H min is the minimum distortion energy at which static recrystallization begins, V t is the probability that at time t the material can continue to form recrystallization nuclei, Q act nucleation activation energy; H is the current distortion energy, which is calculated by the formula H = Co - p - m - b 2 • V t where C o is a constant, μ is the shear modulus value, b is the Burgers vector, V t is the molar volume of austenite, and ρ is the dislocation density.

5. The method of claim 1, wherein, In step (3): The process boundary conditions include: The fusion zone model is set by initial temperature, heat transfer surface and heat transfer coefficient; The heat affected zone model is realized by heating rate, holding time, cooling rate and environment temperature scheduling.

6. The method of claim 1, wherein, In step (5): The grain size analysis includes counting the maximum grain diameter d max , the minimum grain diameter d min , and calculating the grain size correction factor f, the formula for which is d is the average grain diameter.

7. The method of claim 1, wherein, In step (6): The modified Hall-Petch formula is: σ y = σ0+ Kfd -1 / 2 where f is a grain size correction factor, K is a Hall-Petch constant, σ y is the yield strength of the material; σ0is the lattice frictional resistance generated by moving a single dislocation, and d is the average grain diameter. The power law hardening model is: where σ z is true stress, ε p is plastic strain, A is strength coefficient; n is strain hardening exponent.

8. The method of claim 1, wherein, In step (6): The grain parameters are embedded into the finite element model by UMAT subroutine, and C3D8R element is used in the tensile simulation to output the engineering stress-strain curve and P-CMOD curve.

9. The method of claim 8, wherein, The finite element model contains: The tensile specimen model is used to predict the yield strength and tensile strength; The notched specimen model is used to predict the fracture toughness.

10. The method of claim 1, wherein, The process adjustment rules in step (7) are: If the proportion of the local grain size abnormal area exceeds the threshold value, change the welding process and optimize the heat treatment temperature curve; If the strength and toughness are insufficient, increase the cooling rate of the weld and the temperature curve of the heat treatment.

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