An optimization method for the blooming forging process of high-strength steel based on grain size simulation

Abstract

The present invention discloses an optimization method for the blooming forging process of high-strength steel based on grain size simulation. Through hot compression simulation experiments, the present invention processes data with the aid of the DEFORM—Mat window, establishes a grain size simulation material model, and uses cylindrical experiments for verification and optimization. At the same time, a blooming forging finite element model is established in combination with the existing production tooling, molds and process environment to accurately predict the grain size and field distribution after the blooming forging of high-strength steel. Finally, the simulation results are used to guide the process optimization. The model parameters in the present invention are all obtained by fitting on the actual data obtained from experiments, and the simulation parameters are all set according to the experimental process and working conditions. The material model and the finite element model are verified step by step through physical experiments. Therefore, it can truly reflect the deformation characteristics of the material and the production working conditions, thereby improving the accuracy of grain size prediction in blooming forging and providing more accurate guidance for the production process.

Description

Technical Field

[0001] The present invention belongs to the technical field of high-strength steel forging forming, and particularly relates to an optimization method for the blooming forging process of high-strength steel based on grain size simulation. Background Art

[0002] Large key load-bearing components have the characteristics of high material deformation resistance, large forging size, large cross-sectional area change, and complex structure, and are generally manufactured by forging. The forging process mainly includes open-die forging and die forging. Open-die forging is also called blooming forging. The main purposes of blooming forging of large forgings mainly include two points: one is forming; the other is to improve the internal quality of the ingot, break the coarse as-cast structure in the ingot, and make the structure uniform; forge and close internal shrinkage cavities, porosity and other defects. Finally, a billet with uniform and dense structure is obtained, laying a foundation for subsequent forging processing of forgings.

[0003] With the increase of the ingot size, defects such as segregation, inclusion, shrinkage cavity and porosity in the ingot will become more serious. If the dendrites are not fully broken after blooming forging of the billet, or coarse-grained and mixed-grained structures are caused by uneven deformation, these are potential safety hazards for the subsequent die forging process of load-bearing structural components. Therefore, in order to ensure that the quality of large forgings meets the requirements, a suitable forging process should be selected during the blooming forging of the ingot, so as to fully break the dendrites, obtain a uniform structure through recrystallization, and finally obtain a forging blank with uniform properties.

[0004] At present, the production and manufacturing process of key load-bearing structural components mainly still rely on previous on-site forging experience. This not only makes it difficult to ensure the production quality of each structural component, but also will cause huge economic losses if the properties of the forgings are unqualified. In this case, the finite element simulation technology came into being accordingly.

[0005] People hope to reduce the forging cost and shorten the production cycle with the help of finite element software. At present, finite element analysis software has been widely applied to actual production. With the help of finite element analysis technology, the forming process of forgings can be simulated and analyzed to obtain the flow velocity field, temperature field, equivalent strain field of the forgings, and then combined with on-site production experience to finally formulate the optimal forging process plan.

[0006] However, at present, the application maturity of finite element simulation is more reflected in the macroscopic perspective (such as size, temperature, strain), and more attention is paid to the research of thermal physical properties parameters and boundary conditions. For the microscopic grain size and tissue distribution, due to factors such as the low accuracy of the material model, on-site forming process and working conditions, there is still a large deviation between the finite element simulation prediction and the engineering practice. At the same time, there is less research on the hot deformation process of as-cast high-strength steel at present. There is relatively little research on the critical strain model, recrystallization kinetics model, recrystallized volume fraction percentage model, and grain growth model required for the simulation calculation of its microscopic tissue changes, and the material model is missing. Therefore, it is necessary to carry out process experiments, establish a microscopic tissue calculation model of as-cast high-strength steel during cogging forging, improve the accuracy of finite element simulation, and more accurately guide the optimization of production processes. Summary of the Invention

[0007] The purpose of the present invention is to provide an optimization method for the cogging forging process of high-strength steel based on grain size simulation. Through the establishment and optimization of the hot deformation constitutive equation, recrystallization model, and grain growth model, combined with finite element simulation software, the accuracy of predicting the grain size of high-strength steel cogging forging can be effectively improved; then, based on the grain size simulation results, the production process is optimized. The dynamic recrystallization model includes: recrystallization kinetics model, recrystallized volume percentage model, and recrystallized grain size model.

[0008] To achieve the above purpose, the present invention adopts the following technical solutions:

[0009] An optimization method for the cogging forging process of high-strength steel based on grain size simulation, including:

[0010] Through hot compression simulation experiments, obtain the flow stress curve of high-strength steel and construct a hot deformation constitutive equation;

[0011] Through the DEFORM—Mat post-processing window, set parameters according to the actual hot compression simulation experiment conditions, import the flow stress curve, and statistically analyze the dynamic recrystallized grain size data to fit the dynamic recrystallization model required for grain size simulation; the parameters include temperature, strain rate, and initial grain size;

[0012] Through the orthogonal experiment of high-strength steel heating and heat preservation heat treatment, observe and statistically analyze the change of grain size, and fit and establish an austenite grain growth model;

[0013] Use the DEFORM—3D basic module to import the established hot deformation constitutive equation, dynamic recrystallization model, and grain growth model, and verify and optimize the model through the upsetting experiment of cylindrical specimens; obtain the verified and optimized material model;

[0014] Using the DEFORM-MO multi-step module, import and verify the optimized material model, and establish a finite element model for bloom forging by combining the tooling, dies, process environment and equipment parameters on the production site. Simulate the forging process of the forgings to predict the grain size and field distribution after forming;

[0015] Carry out trial production of bloom forging of large-sized forgings, record the temperature and size changes of forgings at different characteristic parts during the production process, dissect the trial-produced parts to count the grain size, and compare with the simulation results to verify and optimize the boundary condition setting in the finite element model; Obtain the optimized finite element model;

[0016] Use the optimized finite element model to analyze the grain size and field distribution corresponding to the bloom forging process under different process conditions, and establish the regular characteristics of the grain size distribution under different process parameters;

[0017] Combined with the actual product grain size grade requirements, or taking the highest grade of grain size at the center part as the criterion, guide the design of bloom forging process parameters.

[0018] During industrial application or research and development, through Gleeble thermal simulation compression experiment.

[0019] During industrial application or research and development; A bloom forging process optimization method for high-strength steel based on grain size simulation, including the following steps:

[0020] Step 1, through thermal simulation compression experiment, fit to obtain the flow stress curve of high-strength steel, and construct a constitutive equation in the form of hyperbolic sine, the expression of which is:

[0021]

[0022] In the formula: is the strain rate; A is a constant; α is called the stress factor (mm2·N-1); n is the stress index; T represents the deformation temperature (K); σ represents the flow stress (MPa), usually taking the peak stress value on the curve; Q is the thermal activation energy of the material (KJ / mol); R is the gas constant (generally 8.314J / mol.K);

[0023] Step 2, through the DEFORM-Mat post-processing window, set the experimental parameters, import the flow stress and dynamic recrystallization grain size data, and establish a dynamic recrystallization model for high-strength steel, which are respectively:

[0024] The expression of the recrystallized volume fraction model:

[0025]

[0026] In the formula: X drx is the volume fraction of dynamic recrystallization of the material; εc is the critical strain; ε 0.5 is the strain at which 50% dynamic recrystallization occurs; ε is the strain; β d , k d are material constants obtained by fitting experimental data;

[0027] Expression of the recrystallization kinetics model:

[0028]

[0029] In the formula: d0 is the initial grain size (mm); T is the deformation temperature (K); Q2 is the activation energy for heat (KJ / mol) when the volume fraction of dynamic recrystallization is 50%; a, m, n2, c are material constants obtained by fitting experimental data;

[0030] Expression of the recrystallized grain size model:

[0031]

[0032] In the formula: a2, h, n3, m2, c2 are all material constants obtained by fitting experimental data;

[0033] Step 3, through the orthogonal experiment of high-strength steel heat treatment, observe the microstructural changes during the heat treatment process, and establish a grain growth model for high-strength steel by fitting. Its expression is:

[0034]

[0035] In the formula: D is the average grain size (μm); t is the holding time (s); T is the heating temperature (K), R is the gas constant (8.314 J / mol·K); Q3 is the activation energy for grain growth (J / mol); m3 and a3 are constants obtained by calculating and fitting experimental data;

[0036] Step 4, use the DEFORM-3D basic module to import the constitutive equation, recrystallization model, and grain growth model of high-strength steel established in Step 1, Step 2, and Step 3, simulate the upsetting process of the cylindrical specimen, and compare with the grain size and distribution results sampled from the physical experiment to verify and optimize the constructed material model until the error of the recrystallization volume percentage obtained from the simulation and the experiment is less than 15% and the grain sizes are of the same grade; obtain the verified and optimized material model;

[0037] Step 5: Use the DEFORM-MO multi-step module to import the verified and optimized material model in Step 4, establish a finite element model for cogging forging of large-sized forgings, simulate the forming process, and compare the temperature, size, and grain size results of different geometric feature parts to verify and optimize the established finite element model for cogging forging of large forgings until the temperature and size errors of the forgings are less than 15% and the grain size is of the same grade; obtain the optimized finite element model.

[0038] Step 6: Use the optimized finite element model in Step 5 to simulate the cogging forging process of large forgings under different forming processes, predict the grain size and field distribution after forging, and summarize the correlation characteristics between process parameters and grain size results based on the simulation results.

[0039] Step 7: Combine the actual product grain size grade requirements, or use the highest grain size grade in the central part as the criterion, and optimize the cogging forging process according to the correlation characteristics between the process parameters and grain results obtained in Step 6.

[0040] Specifically, for the convenience of fitting calculation, the concept of the Z parameter is introduced. The physical meaning of the Z parameter is the strain rate factor after temperature compensation, and its expression is:

[0041]

[0042] where is the strain rate; Q is the thermal activation energy of the material (KJ / mol), R is the gas constant (generally 8.314 J / mol·K), and T represents the deformation temperature (K).

[0043] Specifically, with the help of the DEFORM-Mat material processing window, use the Properties-JMAK Model function to set parameters such as temperature, strain rate, and initial grain size according to the process parameters of the compression experiment carried out, import the flow stress curve and the dynamic recrystallization grain size data under different process parameters, and establish a dynamic recrystallization model during the hot deformation of high-strength steel.

[0044] Specifically, after carrying out the orthogonal experiment on the heating and heat preservation heat treatment of high-strength steel, count the correlation data of initial grain size - heating temperature - heat preservation time - grain size, import it into the DEFORM-Mat material processing window, and fit and establish a grain growth model. When importing the counted correlation data of initial grain size - heating temperature - heat preservation time - grain size into the DEFORM-Mat material processing window in the present invention, the correlation data refers to the change situation (including the change value) of the grain size corresponding to the initial grain size at a certain heating temperature and a certain heat preservation time.

[0045] Specifically, using the DEFORM-3D basic module, the established dynamic recrystallization model and grain growth model are imported, and the high-strength steel material model is verified and optimized through the upsetting test of cylindrical specimens.

[0046] Specifically, when simulating the upsetting of cylindrical specimens, the simulation parameters are set according to the transfer time and friction lubrication conditions in the actual physical experiment. The transfer time is set to 15 s, the friction coefficient between the die and the blank is set to 0.18, the moving speed of the upper die is 2 mm / s, and the deformation amounts are 40%.

[0047] Specifically, using the DEFORM-MO multi-step analysis module, a finite element model of the cogging forging process of large-sized forgings is established, the forging process of large forgings is simulated, and the trial production of large forgings is carried out. By comparing the experimental data and simulation prediction results of different geometric feature parts, the finite element model is verified and optimized.

[0048] Specifically, using the verified and optimized finite element model, the cogging forging process under different process parameters is simulated by finite element method to obtain the correlation characteristics between process parameters and grain size distribution. According to the actual grain size requirements of the product, or taking the high grain size grade at the center of the product as the criterion, the process is optimized.

[0049] When establishing the finite element model of cogging forging, the production site tooling, dies, and process environment are considered, including the geometric shapes and sizes of the tooling and dies, production transfer time, heat transfer coefficient and friction coefficient between the die and the blank, moving speed of the equipment, maximum load, and pass feed amount, etc.

[0050] In industrial applications, the obtained optimized process generally needs to be experimentally verified.

[0051] Through the establishment and optimization of the material constitutive equation, dynamic recrystallization model, and grain growth model, combined with the DEFORM finite element simulation software, this invention uses the upsetting experiment of cylindrical specimens to verify and optimize the material model, and uses the trial production of cogging forging of large-sized forgings to verify and optimize the finite element model, realizing the accurate prediction of the grain size and field distribution after the cogging forging of high-strength steel. Using the simulation results, the correlation characteristics of process parameters on grain size distribution are summarized, and finally, combined with the actual grain size requirements of the product, it guides the process design and optimization.

[0052] Principle and advantages:

[0053] The present invention has established a constitutive equation for hot deformation, a recrystallization model, and a grain growth model. The model parameters are all actual data obtained through experiments and optimized. The simulation parameters used in the simulation are all set according to the experimental process and working conditions. The material model and the finite element model are gradually verified and optimized through the upsetting of cylinders and the trial production of bloom forging of large forgings. Therefore, it can truly reflect the deformation characteristics of the material and the actual production conditions, thereby improving the accuracy of predicting the grain size of bloom forging and providing more accurate guidance for the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0055] Figure 1 It is a schematic diagram of the dimensions of the specimen before and after hot compression;

[0056] Figure 2 It is a finite element model diagram of the cylindrical specimen for upsetting verification;

[0057] Figure 3 It is the strain distribution and the average grain size of dynamic recrystallization in the upsetting simulation of the 300M steel cylindrical specimen;

[0058] Figure 4 It is a comparison diagram of the metallographic structures at different positions of the 300M steel cylindrical specimen;

[0059] Figure 5 It is the metallographic distribution map after the bloom forging of the large-sized forging;

[0060] Figure 6 It is a comparison nephogram of the grain size distribution after the bloom forging of the forging under different process parameters;

[0061] Figure 7 It is the cumulative distribution diagram of the grain size proportion under different process parameters;

[0062] Figure 8 It is the grain size of the long, wide, and high three-directional cross-sections inside the forging;

[0063] Figure 9 It is the grain size diagram of the billet obtained from the simulation calculation in Example 2;

[0064] Figure 10 It is the billet diagram of the actual forging blank obtained in Example 2;

[0065] Figure 11 It is the characterization diagram of the actual forging blank in Example 2.

[0066] Figure 1 In it, a is a schematic diagram of the specimen size before hot compression of the specimen; b is a schematic diagram of the specimen size after hot compression of the specimen.

[0067] From Figure 2 the finite element model of the cylindrical specimen for upsetting verification can be seen.

[0068] Figure 3 In it, a is the contour map of strain distribution; b is the simulation diagram of the average grain size of dynamic recrystallization.

[0069] Figure 4 In it, a is the metallographic diagram at the R / 2 position after upsetting of the 300M steel cylindrical specimen; b is the metallographic diagram at the center position after upsetting of the 300M steel cylindrical specimen.

[0070] Figure 5 In it, a is the metallographic distribution map of the surface layer of the formed square billet; b is the metallographic distribution map of the core part of the formed square billet.

[0071] Figure 6 In it, a is the grain size distribution map of the surface of the product obtained by cogging forging under different process parameters; b is the grain size distribution map of the core part of the product obtained by cogging forging under different process parameters.

[0072] Figure 7 It can be seen that under different process parameters, the proportion of the interval of different grain size grades inside the forgings.

[0073] Figure 8 In it, a is the grain size along the longitudinal section of the central axis; b is the grain size along the height section in the middle of the central axis; c is the grain size along the width section in the middle of the central axis.

[0074] Through Figure 9 , 10 , and the combination of 11, it can be seen that the grain size of the actual forging blank is quite consistent with the simulation results, and the grain size is 5.5 - 6 grades. Specific implementation manner

[0075] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0076] Embodiment 1

[0077] The forging material for the experiment is 300M steel, the die material is 55NiCrMoV7, and the simulation software is DEFORM-3D.

[0078] 1. Hot compression simulation experiment of 300M steel

[0079] The true stress-strain curve of 300M was obtained through hot compression simulation experiment, and the specimen size was The deformation temperature was 800 - 1200 °C, and the strain rates were 0.001, 0.01, 0.1, 1, 10 s -1 . The hot compression simulation experiment was carried out on Gleeble3800, and the dimensions of the specimen before and after compression were as Figure 1 shown.

[0080] 2. Fitting of the constitutive equation of 300M steel

[0081] When fitting the constitutive equation, for the convenience of calculation, the concept of Z parameter was introduced. The physical meaning of the Z parameter is the strain rate factor after temperature compensation, and its expression is shown in Equation (6) of the specification. Using the obtained flow stress curve and combining with Equations (1) and (6) in the specification, Q = 406363, α = 0.007, A = 8.57×10 16 , n = 9.26; the hyperbolic sine form of the constitutive equation was obtained;

[0082] 3. Fitting of the dynamic recrystallization model of 300M steel

[0083] With the help of the DEFORM-Mat material processing window, using the Properties-JMAK Model function, set the parameters such as the temperature, strain rate, and initial grain size of the compression experiment carried out, statistically analyze the change of the average grain size of dynamic recrystallization under different deformation conditions, import the flow stress curve, and fit to obtain β d = 0.8 and k d = 2 of the recrystallized volume fraction model in Equation (2) of the specification, a = 0.005, m = 0.125, n2 = 0, Q2 = 26128, and c = 0 of the recrystallization kinetics model in Equation (3), a2 = 13.14, h = 1, n3 = 0.1608, m2 = -0.2345, and c2 = 0 of the recrystallized grain size model in Equation (4), etc.; the recrystallized volume fraction model, the recrystallization kinetics model, and the recrystallized grain size model were obtained.

[0084] 4. Establishment of the grain growth model of 300M steel

[0085] When conducting orthogonal experiments on the heating and holding heat treatments of high-strength steel, the designed heating temperature includes the actual temperature range during the hot deformation process. The holding times are 15 min, 30 min, 60 min, and 120 min respectively. A correlation database of initial grain size - heating temperature - holding time - grain size is statistically established, and through the calculation and fitting of experimental data, the parameter values of Q3 = 462000, m3 = 4.24, and a3 = 4.28×10 21 in Equation (5) of the specification are obtained, and a grain growth model is obtained.

[0086] 5. Upsetting simulation and experimental verification of cylindrical specimens

[0087] The established material model of as-cast 300M steel is imported into the DEFORM-3D material library (that is, the established constitutive equation, recrystallization model, and grain growth model of high-strength steel are imported into the DEFORM-3D material library), and a finite element model for upsetting cylindrical specimens is established, as Figure 2 shown. At the same time, the simulation boundary conditions are set according to the transfer time and friction lubrication conditions in the actual physical experiment. The transfer time is set to 15 s, the friction coefficient between the die and the blank is set to 0.18; the moving speed of the upper die is 2 mm / s, and the deformation amounts are 40%. The upsetting process of cylindrical specimens is simulated using DEFORM, and the strain distribution nephogram and average recrystallized grain size after upsetting are obtained as Figure 3 shown.

[0088] In the experiment, after the cylindrical specimen is upset, it is immediately immersed in water with pliers to retain the deformed structure at high temperature. Metallographic specimens are cut at R / 2 and the center position on the central plane of the specimen by wire cutting. The corresponding microstructure after upsetting is as Figure 4 shown. By comparison, it can be found that the microstructure distribution obtained by numerical simulation using the established material model meets the error requirements (the error requirements are: until the error of the recrystallized volume fraction obtained by simulation and experiment is less than 15%, and the grain size is of the same grade), and the verified and optimized material model is obtained. Figure 4 a Complete dynamic recrystallization occurs at the center of the specimen, and the average grain size is about 49 μm; Figure 4 b Complete recrystallization does not occur at the R / 2 position on the central plane, and its average grain size is larger than that at the center position, about 78 μm, which is about 1.5 times the average grain size of the central region.

[0089] 6. Open-die forging simulation and trial production verification of large-sized forgings

[0090] Using the DEFORM-MO multi-step module, import and verify the optimized material model. Combine the tooling, dies, process environment, and equipment parameters on the production site to establish a finite element model for cogging forging. Simulate the forging process of the forgings to predict the grain size and field distribution after forming. Conduct trial production of cogging forging of large-sized forgings, record the temperature and size changes of forgings at different characteristic parts during the production process, dissect the trial-produced parts to count the grain size, and compare with the simulation results to verify and optimize the boundary condition settings in the finite element model. Until the error of the recrystallized volume percentage obtained from the simulation and experiment is less than 15%, and the grain size is of the same grade; obtain the optimized finite element model.

[0091] Using the optimized finite element model, analyze the grain size and field distribution corresponding to the cogging forging process under different process conditions, and establish the regular characteristics of the grain size distribution under different process parameters.

[0092] Combined with the grain size grade requirements of the actual product, or taking the highest grain size grade at the center part as the criterion, guide the design of the process parameters for cogging forging.

[0093] Combined with the tooling, dies, process environment, and equipment parameters on the production site, including the geometric shapes and size parameters of tooling fixtures, die anvils, etc., set the transfer time, heat transfer coefficient, friction coefficient, movement speed, and pass feed amount. Among them, the size of the upper die used for forming is Φ2500×200mm, the size of the knockout plate used is Φ2500×Φ410×600mm, the size of the upper and lower flat anvils is 2500×900×200 (L×W×H), the upsetting reduction is 50%; the reduction per pass for drawing is 10-20%, the feed amount is 600mm, the transfer time is 90s, the friction coefficient is 0.3, the heat transfer coefficient between the die and the blank is 1, the heat transfer coefficient between the blank and the air is 0.02, and the forming process is six-upsetting and six-drawing. Use DEFORM-MO to simulate the forming process and obtain the temperature field, strain field, and grain size field distribution after cogging forging.

[0094] After the trial production of large-sized forgings is completed, anatomical sampling is carried out immediately. Samples are taken at different characteristic geometric positions such as the tong end, the center of the forging body, and the surface of the forging body to observe their microstructures. The characterizations obtained from the trial-produced products are as Figure 5 shown, which are the metallographic structure comparisons between the surface layer and the core after the sample is formed. It can be seen that the grain size of the surface layer is smaller than that of the core, and at the same time, the grain size grades of both the surface layer and the core reach above grade 7, which is consistent with the simulation results of the optimized finite element model in terms of regularity, verifying the correctness and applicability of the established grain size simulation model.

[0095] 7. Grain size distribution characteristics corresponding to different process parameters

[0096] Based on the past product process design and actual production experience, four preliminary forging forming process plans for bloom are formulated. Based on the established finite element model of bloom forging, the forging forming processes of different plans are simulated. Among them: Plan ① is five upsetting and five drawing + drawing out forming; Plan ② is six upsetting and six drawing + drawing out forming; Plan ③ is four upsetting + drawing out + 30% bloom upsetting + drawing out forming; Plan ④ is five upsetting + drawing out + 30% bloom upsetting + drawing out forming. The grain size distribution nephograms after the forming of the four process plans are as Figure 5 shown; the interval proportions of different grain size grades inside the forgings after the forming of the four process plans are as Figure 6 shown.

[0097] It can be seen that the grain size distribution in the bloom decreases from the tong end and the free end to the middle, and increases from the surface to the inside. Due to the influence of the end face, the strain at the tong end and the free end is small, the degree of dynamic recrystallization is small, and the grain size is large; the strain in the middle part is large, the degree of dynamic recrystallization is large, and the grain size is small, but the temperature in the bloom decreases, and the required critical strain is large, so the degree of dynamic recrystallization becomes small.

[0098] Comparing Plan 2 with Plan 4, the overall strain in Plan 2 is greater than that in Plan 4, but the proportion of small grain size in Plan 2 is 84.67%, which is less than the proportion of small grain size in Plan 4, which is 87.73%. Therefore, it is judged that the influence of temperature on the grain size in this simulation is greater than the influence of strain on the grain size.

[0099] Comparing Plan 1 with Plan 2, the temperature distributions are similar after the heat treatment, but for the grain size above grade 9, the proportion in Plan 2 is 72.44%, and in Plan 1 it is 65.06%; for the grain size above grade 8, the proportion in Plan 2 is 79.29%, and in Plan 1 it is 76.47%. Therefore, it is judged that the increase in the magnitude and uniformity of the strain inside the forging is beneficial to obtaining smaller grain sizes.

[0100] Comparing Plan 1 with Plan 3, the temperature proportion and temperature distribution in Plan 3 are higher than those in Plan 1, but for the grain size above grade 9, the proportion in Plan 1 is 65.06%, and in Plan 3 it is 35.03%; for the grain size above grade 8, the proportion in Plan 1 is 76.47%, and in Plan 3 it is 61.22%. Therefore, for the bloom with a high temperature, it is necessary to increase the strain to drive dynamic recrystallization and avoid grain growth.

[0101] 8. Optimize the bloom forging process based on the grain size simulation results

[0102] The grain size requirement for the product in this simulation is that the grain size grades in the three directions of length, width and height of the main body of the forging are not lower than grade seven except for the tong and the free end. According to the requirements, combined with the grain size distribution under different process parameters as Figure 8 shown, select a suitable bloom forging process.

[0103] First, on the longitudinal section along the central axis, the coarse grain sizes are distributed in the middle section of the central axis. The grain size order from low to high is Plan 2 > Plan 4 > Plan 1 > Plan 3. More than 85% of the length of Plan 2 has a grain size grade of 8 or above, and 100% of the length has a grain size grade of 7 or above. For Plan 1 and Plan 4, the grain size in the middle section of 2000 mm is lower than grade 7, and the grain size of Plan 3 is even lower than grade 6.

[0104] Secondly, on the radial section in the middle of the central axis, the order of grain size from low to high is the same as that of the central axis. At this time, 100% of the height of Plan 2 has a grain size of 8 or above. For Plan 4, the grain size is lower than 8 at 200 - 300 mm, but 100% of the height reaches a grain size of 7 or above. The central grain size of the radial sections of Plan 1 and Plan 3 is lower than 7.

[0105] Finally, on the widthwise section in the middle of the central axis, 100% of the width of Plan 2 can meet the requirements; for Plan 4, there are grains that do not meet the requirements at 200 - 800 mm. When the grain size requirement is 7 or above, 100% of the height of Plan 4 can meet the requirements.

[0106] Based on the above results, it can be determined that for Plan ②, six upsetting and six drawing + drawing out can obtain the grain size and field distribution that meet the requirements after bloom forming. Except for the tong end and the free end, 100% of its grain size grade reaches 7 or above.

[0107] The above embodiments are merely examples clearly illustrating the present invention, rather than limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the embodiments here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.

[0108] Embodiment 2

[0109] According to Plan 2 in Step 7 of Embodiment 1, raise the ingot heating temperature by 100 °C. After performing bloom forging simulation using the same plan, the grain size inside the bloom increases exponentially, and the bloom grain size is 49 - 96 μm (as Figure 9 shown). And for the actual forging billet as Figure 10 shown, the grain size is comparable to the simulation result, and the grain size grade is 5.5 - 6 (as Figure 11 shown).

Claims

1. A method for optimizing the blooming forging process of high-strength steel based on grain size simulation, characterized in that, Including: Obtain the flow stress curve of high-strength steel through hot compression simulation experiments, and construct a hot deformation constitutive equation; Through the DEFORM-Mat post-processing window, set parameters according to the actual hot compression simulation experiment conditions, import the flow stress curve, statistically analyze the dynamic recrystallization grain size data, and fit the dynamic recrystallization model required for grain size simulation; the parameters include temperature, strain rate, and initial grain size; Through the orthogonal experiment of high-strength steel heating and holding heat treatment, observe and statistically analyze the change of grain size, and fit and establish an austenite grain growth model; Use the DEFORM-3D basic module, import the established hot deformation constitutive equation, dynamic recrystallization model and grain growth model, and verify and optimize the model through the upsetting experiment of cylindrical specimens; obtain the verified and optimized material model; Use the DEFORM-MO multi-step module, import the verified and optimized material model, combine the tooling, die, process environment and equipment parameters on the production site, establish a blooming forging finite element model, and simulate the forging process of the forging to predict the grain size and field distribution after forming; Carry out the trial production of blooming forging of large-sized forgings, record the temperature and size changes of forgings at different characteristic parts during the production process, anatomize the trial-produced parts to statistically analyze the grain size, and compare with the simulation results to verify and optimize the boundary condition setting in the finite element model; obtain the optimized finite element model; Use the optimized finite element model to analyze the grain size and field distribution corresponding to the blooming forging process under different process conditions, and establish the regular characteristics of grain size distribution under different process parameters; Combined with the actual product grain size grade requirements, or taking the highest grade of grain size at the center part as the criterion, guide the design of blooming forging process parameters.

2. An optimization method for the blooming forging process of high-strength steel based on grain size simulation according to claim 1, characterized in that: When fitting the constitutive equation, the concept of Z parameter is introduced. The physical meaning of the Z parameter is the strain rate factor after temperature compensation, and its expression is: wherein is the strain rate; Q is the thermal activation energy of the material in KJ / mol, R is the gas constant, and T represents the deformation temperature in K.

3. An optimization method for the blooming forging process of high-strength steel based on grain size simulation according to claim 1, characterized in that: With the help of the DEFORM-Mat material processing window, use the Properties-JMAK Model function, set parameters and import the flow stress curve, statistically analyze the dynamic recrystallization grain size data, and establish a dynamic recrystallization model and a grain growth model for high-strength steel.

4. A method for optimizing the blooming forging process of high-strength steel based on grain size simulation according to claim 1, characterized in that: Carry out the orthogonal experiment of high-strength steel heating and holding heat treatment, import the correlated data of initial grain size - heating temperature - holding time - grain size into the DEFORM-Mat material processing window, and establish a grain growth model.

5. A method for optimizing the blooming forging process of high-strength steel based on grain size simulation according to claim 1, characterized in that: Use the DEFORM-3D basic module, import the constitutive equation, dynamic recrystallization model and grain growth model, and verify and optimize the material model through the upsetting experiment of cylindrical specimens.

6. The optimization method for the blooming forging process of high-strength steel based on grain size simulation according to claim 1, wherein: When simulating the upsetting of cylindrical specimens, set the simulation parameters according to the transfer time and friction lubrication conditions in the actual physical experiment. The transfer time is set to 15 s, the friction coefficient between the die and the blank is set to 0.18; the moving speed of the upper die is 2 mm / s, and the deformation amount is 40% respectively.

7. An optimization method for the blooming forging process of high-strength steel based on grain size simulation according to claim 1, characterized in that: Using the DEFORM-MO multi-step analysis module, import the optimized material model, and establish a cogging forging finite element model by combining the tooling, die, process environment and equipment parameter factors on the production site. Verify and optimize the finite element model through the trial production of large-sized forgings.

8. A method for optimizing the blooming forging process of high-strength steel based on grain size simulation according to claim 1, characterized in that: Record the temperature and size evolution of different characteristic geometric parts such as the center, surface, and variable cross-section of the forging during the forming process, dissect the sample parts to count the grain size, and compare with the simulation results.

9. A method for optimizing the blooming forging process of high-strength steel based on grain size simulation according to claim 1, characterized in that: Use the verified and optimized finite element model to conduct finite element simulations on the cogging forging process under different process parameters, predict the grain size and field distribution after forming, and obtain the grain size distribution characteristics under different process parameters.

10. A method for optimizing the blooming forging process of high-strength steel based on grain size simulation according to claim 1, characterized in that: According to the simulation results of the product grain size, optimize the process according to the required grain size grade for actual needs; or optimize the process based on the criterion of a high grain size grade at the center of the product.

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