Method for rapidly simulating and predicting structure of titanium alloy in thermal deformation

Abstract

The invention provides a method for rapidly simulating and predicting a structure of a titanium alloy in thermal deformation. The method comprises the following steps: determining initial parameters of thermal deformation of a sample; a critical criterion of a metal microstructure evolution type and a kinetic model and a grain size model corresponding to the metal microstructure type are established, the metal microstructure evolution type in thermal deformation is determined, and the corresponding volume fraction and grain size of the metal microstructure evolution type are calculated; a grain growth or mixed crystal grain calculation model is selected to calculate the average grain size of the metal microstructure; a temperature calculation model in thermal deformation is introduced, and temperature input parameters of the next deformation pass are iteratively updated; and the metal microstructure type of the sample after thermal deformation, the corresponding volume fraction, the grain size and the final average grain size are output. According to the establishing method, the influence of temperature and the like on the material structure in field production is fully considered, the field dynamic adjustment and optimization of the thermal deformation process can be guided, the forming quality is improved, and the machining cost is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of real-time simulation and prediction of metal microstructures in intelligent manufacturing, and in particular to a method for rapid simulation and prediction of the structure of titanium alloys undergoing thermal deformation. Background Art

[0002] In the era of intelligent manufacturing, the deep integration of information technology and manufacturing is becoming increasingly close. Digital twin technology has become one of the key technologies promoting the high-quality development of advanced manufacturing. Digital twin technology digitally creates virtual models of physical entities. Based on data, with models as the core and software as the carrier, it realizes the control, monitoring, optimization, and decision-making of physical entities through interactive feedback between the virtual and the real.

[0003] Hot working is a common method for improving, optimizing, and controlling the microstructure and performance of metallic materials. The complex interactions between hot working deformation conditions and the evolution of the material's microstructure in titanium alloys increase the difficulty of process optimization, quality control, and cost reduction. Therefore, establishing a responsiveness between process parameter input and microstructure evolution is crucial for process development and forming quality control, and can promote the rapid development of advanced manufacturing technologies that integrate material forming and properties.

[0004] Real-time simulation of microstructure. Common microstructure simulations during thermal deformation, such as cellular automata and phase field methods, are highly accurate but time-consuming. They are deployed at production sites for fast / real-time simulation, but are costly and difficult.

[0005] Therefore, establishing a rapid response between deformation process parameters and the microstructure of materials / workpieces, real-time prediction, and serving the dynamic adjustment and optimization of the process in actual production are problems that need to be urgently solved by existing technologies.

[0006] The influence law between "process parameters-structure-performance" during thermal deformation is the key to developing advanced new processes and achieving comprehensive high performance of materials. By correlating deformation conditions with tissue evolution, real-time simulation and prediction of microstructure during the forming process can be achieved, and online tissue control technology can be developed. This is of great significance for reducing production and design costs, optimizing forming process parameters, and realizing integrated forming and performance technology. Summary of the Invention

[0007] The present invention provides technical support and solutions for the development of advanced new processes in the era of intelligent manufacturing, promotes the development of online microstructure control processes during thermal deformation of titanium alloys, improves forming quality, and reduces processing and manufacturing costs.

[0008] To achieve the above objectives, the present invention adopts a technical solution: a method for rapid simulation and prediction of the microstructure of titanium alloy during thermal deformation, comprising the following specific steps:

[0009] S1. Determine the initial parameters of thermal deformation of the specimen;

[0010] S2. Establishing a critical criterion for the metal microstructure evolution type and a kinetic model and a grain size model corresponding to the metal microstructure type, determining the metal microstructure evolution type during thermal deformation based on the initial parameters in step S1, and calculating and outputting the volume fraction and grain size corresponding to the metal microstructure evolution type;

[0011] S3, based on the volume fraction and grain size output in step S2, selecting a grain growth or mixed grain model to calculate the average grain size of the metal microstructure;

[0012] S4, introducing the temperature calculation model in thermal deformation, iteratively updating the temperature input parameters of the next deformation pass in step S2;

[0013] S5. Output the metal microstructure type and its corresponding volume fraction, grain size, and final average grain size of the sample after thermal deformation.

[0014] Preferably, in step S1, the initial parameters of the thermal deformation of the sample are obtained through an isothermal hot compression experiment, and the initial parameters include: the number of deformation passes is 1 to 2 times, the deformation rate is 0.001s -1 ~10s -1 The deformation amount is 20% to 60%, the initial temperature is 880℃ to 980℃, the deformation pass interval is 1 to 300s, and the initial grain size of the sample is obtained by observing the microstructure of the sample through optical microscope and scanning electron microscope.

[0015] Preferably, the metal microstructure evolution types include dynamic recrystallization, metadynamic recrystallization and static recrystallization; the specific process of step S2 is:

[0016] S21. Establishing critical criteria for the occurrence of dynamic recrystallization and kinetic models and grain size models corresponding to dynamic recrystallization, metadynamic recrystallization, and static recrystallization;

[0017] S22, if the initial parameters of step S1 meet the critical criterion for the occurrence of dynamic recrystallization in step S21, using a dynamic recrystallization kinetic model and a dynamic recrystallization grain size model to respectively calculate the dynamic recrystallization volume fraction and the dynamic recrystallization grain size;

[0018] S23, if the dynamic recrystallization volume fraction in step S22 is less than 0.95, using a dynamic recrystallization kinetic model and a dynamic recrystallization grain size model to calculate the dynamic recrystallization volume fraction and the dynamic recrystallization grain size respectively;

[0019] S24, if the initial parameters of step S1 do not meet the critical criterion of dynamic recrystallization in step S21, using a static recrystallization kinetic model and a static recrystallization grain size model to calculate the static recrystallization volume fraction and the static recrystallization grain size respectively;

[0020] S25. Output the volume fraction and grain size corresponding to dynamic recrystallization, meta-dynamic recrystallization and static recrystallization.

[0021] Preferably, in step S21, the critical criterion for the occurrence of dynamic recrystallization is:

[0022] Z≤Z lim &&ε * ≥ε c ; (1)

[0023] in: Z is the Zener-Holomon parameter, Q is the deformation activation energy, T is the initial temperature, R is the gas constant, Z lim is the critical Z parameter for dynamic recrystallization, ε c is the critical strain for dynamic recrystallization to occur, ε * is the deformation amount, * is the number of lane changes;

[0024] Based on the initial parameters in step S1, the Z parameter, the critical Z parameter, the critical strain of dynamic recrystallization, and the deformation are calculated and compared to determine whether dynamic recrystallization occurs in the metal microstructure;

[0025] The kinetic models and grain size models of dynamic recrystallization, metadynamic recrystallization and static recrystallization in step S21 are:

[0026]

[0027] Where: X DRX is the dynamic recrystallization volume fraction, k, n are constants, ε is the deformation amount, ε 0.5 For X DRX = 0.5 when the strain;

[0028] d DRX =AZ m ; (3)

[0029] Where: d DRX is the dynamically recrystallized grain size, A and m are constants, and Z is the Zener-Holomon parameter;

[0030]

[0031] Where: X MDRX is the volume fraction of metadynamic recrystallization, k, n are material constants, t 0.5 For XMDRX = 0.5 hours;

[0032] d MDRX =AZ m ; (5)

[0033] Where: d MDRX Subdynamic recrystallization grain size, A and m are constants, and Z is the Zener-Hollomon parameter;

[0034]

[0035] Where: X SRX is the static recrystallization volume fraction, k, n are material constants, t 0.5 For X SDRX = 0.5 hours;

[0036]

[0037] Where: d SRX is the static recrystallization grain size, A, p, q, v are constants, d i is the grain size when the deformation pass is i, Q is the apparent activation energy, T is the deformation temperature, is the deformation rate;

[0038] The volume fraction and grain size corresponding to dynamic recrystallization, metadynamic recrystallization and static recrystallization are calculated respectively through the kinetic models and grain size models of dynamic recrystallization, metadynamic recrystallization and static recrystallization.

[0039] Preferably, in step S3, the selection of the grain growth or mixed crystal grain calculation model is based on the following criteria: the static recrystallization volume fraction output in step S2 is greater than 0.95 or the sum of the dynamic recrystallization volume fraction and the sub-dynamic recrystallization volume fraction is greater than 0.95;

[0040] If X is satisfied SRX ≥0.95 or X DRX +X MDRX ≥0.95; the average grain size is calculated using a grain growth calculation model; the grain growth calculation model is:

[0041]

[0042] Where: d s is the initial recrystallized grain size, t is the residence time after recrystallization is completed, and A is a constant;

[0043] If X is not satisfied SRX ≥0.95||X DRX +X MDRX≥0.95; the average grain size is calculated using a mixed crystal grain calculation model; the mixed crystal grain calculation model is:

[0044]

[0045] Where: i is the deformation pass, j is the recrystallization type, j = 1 is dynamic recrystallization, j = 2 is sub-dynamic recrystallization, j = 3 is static recrystallization, d rex 、X i is the grain size and volume fraction corresponding to the recrystallization type, d j-1 is the grain size of the part that has not been recrystallized;

[0046] Finally, the metal microstructure type and its corresponding recrystallization type grain size, volume fraction and average grain size are output.

[0047] Preferably, in step S4, the temperature calculation model is used to simulate and analyze the temperature change during thermal deformation, and the temperature change Δt after each pass of thermal deformation is:

[0048] Δt=Δt p +Δt f -(Δt φ +Δt u +Δt d ); (11)

[0049] Where, Δt p is the temperature rise of the specimen during thermal deformation, Δt f is the temperature rise caused by the friction heat generated between the molds during thermal deformation, Δt d , Δt φ , Δt u The temperature drop is caused by heat conduction between the contact part of the mold and the sample, radiation heat transfer between the sample surface and the air medium, and convection heat transfer generated during the operation of the sample.

[0050] Preferably, the deformation pass is the number of times steps S2-S4 are cycled. When the deformation pass is greater than 1, the initial input in step S2 is the initial parameter of the previous step and the temperature parameter output in step S4.

[0051] Preferably, the method completes the organization simulation prediction algorithm during thermal deformation through the computer programming language JAVA and SpringBoot architecture as well as the established metal microstructure evolution model and temperature calculation model, and realizes fast / real-time prediction of the recrystallization type, volume fraction and grain size of the sample during thermal deformation.

[0052] Compared with the prior art, the present invention has the following advantages:

[0053] The model established by the present invention can simulate and analyze the microstructure information of different types of structural evolution during thermal deformation, including dynamic recrystallization, sub-dynamic recrystallization and static recrystallization and their corresponding volume fractions and grain sizes.

[0054] The present invention establishes a real-time simulation prediction process for the microstructure of titanium alloys during actual thermal deformation, and uses computer programming language to algorithmize the model, which can quickly predict the material microstructure information during thermal deformation.

[0055] The present invention establishes a temperature model to correlate the deformation parameters during processing with the tissue model, which is more suitable for the actual production process on site, realizes dynamic feedback of tissue simulation prediction and on-site process adjustment, lays the foundation for real-time prediction of "process parameters-structure-performance", further guides process design and optimization, improves product R&D efficiency and reduces processing and manufacturing costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 This is the overall technical flow chart of the metal structure simulation prediction during thermal deformation of the present invention;

[0057] Figure 2 1. It is a schematic diagram of the calculation process of volume fraction and grain size of different tissue types of the present invention;

[0058] Figure 3 1 is a schematic diagram of the average grain size calculation process of the present invention;

[0059] Figure 4 This is the structure of the recrystallization simulation prediction program in the example of the present invention, where (a) is the directory structure, (b) is the model layer, (c) is the control layer, and (d) is the service layer;

[0060] Figure 5 It is the initial parameter input interface of the recrystallization prediction program in the embodiment of the present invention;

[0061] Figure 6 It is the output interface of the recrystallization prediction program in the embodiment of the present invention;

[0062] Figure 7 This is the output result interface of the recrystallization prediction program in the example of the present invention, where (a) is the output of volume fraction and (b) is the output of grain size;

[0063] Figure 8 It is the design interface for the volume fraction and grain size output results of the recrystallization prediction program in the example of the present invention. DETAILED DESCRIPTION

[0064] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0065] like Figure 1 As shown, the present invention provides a technical solution: a method for rapid simulation and prediction of the structure of titanium alloy during thermal deformation, comprising the following steps:

[0066] S1. Determine the initial parameters of thermal deformation of the specimen;

[0067] S2. Establishing a critical criterion for the metal microstructure evolution type and a kinetic model and a grain size model corresponding to the metal microstructure type, determining the metal microstructure evolution type during thermal deformation based on the initial parameters in step S1, and calculating and outputting the volume fraction and grain size corresponding to the metal microstructure evolution type;

[0068] S3, based on the volume fraction and grain size output in step S2, selecting a grain growth or mixed grain model to calculate the average grain size of the metal microstructure;

[0069] S4, introducing the temperature calculation model in thermal deformation, iteratively updating the temperature input parameters of the next deformation pass in step S2;

[0070] S5. Output the metal microstructure type and its corresponding volume fraction, grain size, and final average grain size of the sample after thermal deformation.

[0071] In the embodiment provided by the present invention, taking the titanium alloy forging process as an example, in step S1, the forging deformation pass N is determined to be 1 to 2 times and the deformation rate is determined to be 1 to 2 times by isothermal hot compression experiment. 0.001s -1 ~10s -1 The deformation amount ε is 20% to 60%, the initial temperature T is 880℃ to 980℃, and the deformation interval time t i The process parameter values related to the initial grain size d0 of the sample are obtained by observing the microstructure of the sample through optical microscope and scanning electron microscope, and the number of simulation cycles of the program is determined according to the deformation pass N.

[0072] In the embodiment provided by the present invention, in step S2, the metal microstructure evolution type includes dynamic recrystallization, metadynamic recrystallization and static recrystallization. The specific process of step S2 is as follows: Figure 2 As shown, specifically,

[0073] a. Based on the initial input parameters in step S1, calculate the Z parameter, Z lim , ε c and ε * And compare whether the criterion for the occurrence of dynamic recrystallization DRX is met:

[0074] Z≤Z lim &&ε * ≥ε c (1)

[0075] in: Z is the Zener-Holomon parameter, Q is the deformation activation energy, T is the initial temperature, R is the gas constant, Z lim is the critical Zener-Holomon parameter for dynamic recrystallization, ε c is the critical strain for dynamic recrystallization to occur, ε * is the deformation amount, * is the number of lane changes;

[0076] b. If the criterion for the occurrence of dynamic recrystallization DRX is met, the volume fraction X at this time is calculated using the dynamic recrystallization DRX kinetic model and grain size model. DRX and grain size d DRX , and further judge X DRX ≥0.95, if this condition is met, then output the current X DRX and d DRX ;

[0077]

[0078] Where: X DRX is the dynamic recrystallization volume fraction, k, n are material constants, ε 0.5 For X DRX = 0.5 when the strain;

[0079] d DRX =AZ m (3)

[0080] Where: d DRX is the dynamically recrystallized grain size, A and m are constants, and Z is the Zener-Holomon parameter;

[0081] c. If X is not satisfied DRX ≥0.95, the volume fraction X of the metadynamic recrystallization MDRX is calculated using the kinetic model and grain size model of the metadynamic recrystallization MDRX. MDRX and grain size d MDRX , output X at this time DRX d DRX 、X MDRX and d MDRX ;

[0082]

[0083] Where: X MDRX is the volume fraction of metadynamic recrystallization, k, n are material constants, t 0.5 For X MDRX = 0.5 hours;

[0084] d MDRX =AZ m (5)

[0085] Where: d MDRX Subdynamic recrystallization grain size, the other parameters have the same meanings as formula (3);

[0086] d. If the conditions in step a are not met, the volume fraction X at this time is calculated using the kinetic model and grain size model of static recrystallization SRX SRX and grain size d SRX , and output the X at this time SRX and d SRX ;

[0087]

[0088] Where: X SRX is the static recrystallization volume fraction, k, n are material constants, t 0.5 For X SDRX = 0.5 hours;

[0089]

[0090] Where: d SRX is the static recrystallization grain size, A, p, q, v are constants, di is the grain size when the deformation pass is i, Q is the apparent activation energy, T is the deformation temperature, is the deformation rate;

[0091] e. Output the calculated volume fraction and grain size values: X DRX d DRX / X MDRX d MDRX / X SRX d SRX .

[0092] In the embodiment provided by the present invention, the method for determining the microstructure model of the titanium alloy material is as follows:

[0093] a. Using isothermal hot compression test, first sample the titanium alloy (TC11), characterize its initial structure using SEM, obtain its initial grain size, and then prepare or Cylindrical hot compression specimens.

[0094] b. For the dynamic recrystallization DRX model, different temperatures (880 / 930 / 980℃), strain rates (0.01 / 1 / 10s -1 ) and a fixed initial grain size, a single-pass thermal simulation experiment with a total deformation of 60% was conducted to obtain stress-strain curves under different deformation conditions; based on the obtained stress-strain curves, the logarithms of both sides of formulas (2) and (3) were taken, and the following model formula was established through regression:

[0095] ln[-ln(1-X DRX )]=ln k+n ln((ε-ε c ) / ε 0.5 );

[0096]

[0097] Combining the dynamic recrystallization volume fractions of different deformation conditions obtained in the formula, we can solve the constant value in the dynamic recrystallization kinetic model, ln[-ln(1-X DRX )] and ln(ε-ε c ) / ε 0.5 There is a good linear regression fitting relationship between them, indicating that there is a good correlation between the two. Therefore, the values of the material constants n and k in the motion recrystallization model of TC11 titanium alloy can be directly obtained from the slope and intercept of the fitting line, which are 2 and -0.693 respectively. Therefore, the original model formula is:

[0098]

[0099] d DRX =22300.0(Z) -0.27 ;

[0100] In the formula, the strain when the dynamic recrystallization volume fraction reaches 0.5

[0101] The critical strain ε for dynamic recrystallization c =0.83ε p ;

[0102]

[0103] in, Z is the Zener-Hollomon parameter, Q is the deformation activation energy, R is the gas constant, T is the initial temperature, is the deformation rate, ε is the deformation amount, and d0 is the initial grain size;

[0104] c. For the solution of the metadynamic recrystallization MDRX and static recrystallization SRX models of TC11 titanium alloy, a double-pass hot compression experiment is adopted. In addition to considering the difference in temperature and strain rate in step S22(b), the double-pass hot compression experiment also needs to set the strain amount of the first pass and the interval time between the first pass and the second pass. For the metadynamic recrystallization MDRX, the deformation amount of the first pass must reach or exceed X DRX = 0.5 or the strain that causes the peak stress. The deformation of the first pass of static recrystallization SRX must be less than X DRX = 0.5, the interval time can be set according to the required softening degree (for example, an appropriate value between 1 and 300 s). After obtaining the stress-strain curves under different deformation conditions, the logarithms of both sides of formulas (4) to (7) are taken, and the following model formula can be obtained through linear fitting regression:

[0105]

[0106] d MDRX =22600.0(Z) -0.23 ;

[0107]

[0108] In the formula, the constant

[0109] Time t when the volume fraction of metadynamic recrystallization is 0.5 0.5 =8.31×10 -15 , Z is the Zener-Hollomon parameter, Q is the deformation activation energy, R is the gas constant, T is the initial temperature, is the deformation rate, ε is the deformation amount, d0 is the initial grain size;

[0110] Finally, the established kinetic model and grain size model were tested and corrected by sampling and analyzing the samples after hot compression, characterizing the microstructure by SEM (backscatter mode), and statistically analyzing the recrystallized volume fraction and grain size using Image-pro plus software.

[0111] In the embodiment provided by the present invention, in step S3, based on the output in S2: X DRX d DRX 、X MDRX d MDRX 、X SRX and d SRX , the simulation calculation method to determine the average grain size of its microstructure is: "grain growth" or "mixed grain" method, the process is as follows Figure 3Specifically:

[0112] a. First determine whether the criteria for calculating the average grain size are met:

[0113] X SRX ≥0.95||X DRX +X MDRX ≥0.95 (8)

[0114] That is, the static recrystallization volume fraction or the sum of the dynamic recrystallization and sub-dynamic recrystallization volume fractions is greater than 0.95;

[0115] b. If satisfied, the average grain size is calculated using the grain growth model; otherwise, the average grain size is calculated using the mixed crystal grain model;

[0116] The grain growth calculation model is:

[0117]

[0118] Where: d s is the initial recrystallized grain size, t is the residence time after recrystallization is completed, and A is a constant;

[0119] The calculation model of the average grain size of mixed crystal grains is:

[0120]

[0121] c. Output tissue type and corresponding volume fraction X DRX 、X MDRX 、X SRX , average grain size

[0122] In the embodiment provided by the present invention, in step S4, there are relatively few factors affecting the deformation speed and deformation amount in the forging process, which can be set as constants or simply changed according to actual needs. Temperature is a key control process parameter in the actual forging process, and its temperature model is recommended to iteratively update the temperature input for the next deformation step / pass.

[0123] The temperature model is:

[0124] Δt=Δt p +Δt f -(Δt φ +Δt u +Δt d ) (11)

[0125] Specifically: In the actual forging process, the convective heat loss of the workpiece during the gap time is much smaller than the radiation heat loss. For the convenience of calculation, the convective heat loss of the workpiece can be and Δt u Combined calculation, while Δtf and Δt p The simplified temperature model is: Δt=Δt p -KΔt φ -Δt d , where K>1; for Δt p , Δt φ and Δt d The calculation is based on parameters such as workpiece material, deformation zone area, and pressure in actual forging processing, and the temperature model is corrected in combination with temperature changes collected on site.

[0126] In step S5, microstructure information such as the microstructure type and corresponding volume fraction, grain size, and final average grain size of the titanium alloy after forging is output.

[0127] Example 1

[0128] like Figure 4 The developed program shown in (a) is mainly divided into three layers: "model", "Controller" and "Service". Figure 4 (b) “Model” defines the input variables and the variables that receive the output results. Figure 4 (c) "Controller" is used to interact with the browser during program operation. Figure 4 The "Service" layer in (d) stores all the business code required to implement the recrystallization calculation and simulation functions. The "cal" folder defines all the models related to recrystallization in the program, including dynamic recrystallization, sub-dynamic recrystallization, static recrystallization models, and the recrystallization critical criterion calculation model. At the same time, the program encapsulates some of the same formulas in the recrystallization model in "BaseCal.java" and defines them in an unordered manner. These formulas can be directly called to implement the relevant functions. "DrxService.java" implements the main program logic for the recrystallization calculation and simulation: it calls the recrystallization calculation model defined in the "cal" package, determines the type of recrystallization, calculates the volume fraction and grain size, and returns a list of calculation results.

[0129] On this basis, the open source visualization library E-Charts was used to design the initial input parameter interface for recrystallization structure simulation prediction and the output interface for simulation results, such as Figures 5 to 8 As shown. In the initial parameter input interface, all input parameters of the program are defined, including initial deformation temperature, deformation amount, deformation pass number, deformation speed, gap time and initial grain size. The output interface sets up a simple table to display the calculation simulation results, and sets "recrystallization volume fraction" and "recrystallization grain size" to achieve jumps between different interfaces. Click the corresponding "label" to jump to the following interface. Figure 7 and Figure 8Recrystallized volume fraction and grain size distribution statistics are shown.

[0130] Example 2

[0131] A complete piece of TC11 titanium alloy The cylindrical hot compression specimens were prepared by grinding and polishing the original specimens. The polished specimens were corroded with a 1:3:10 HF:HNO3:H2O etching solution for about 10s-12s. The microstructure of the specimens was observed using an optical microscope and a scanning electron microscope to obtain their initial grain size d0.

[0132] For the dynamic recrystallization DRX model, different temperatures (880 / 930 / 980℃), strain rates (0.01 / 1 / 10s -1 ) and a fixed initial grain size, a single-pass thermal simulation experiment with a total deformation of 60%; for the solution of the metadynamic recrystallization MDRX and static recrystallization SRX models, a double-pass hot compression experiment is adopted. In addition to considering the differences in temperature and strain rate, the double-pass hot compression experiment also needs to set the strain amount of the first pass and the interval time with the second pass: For the metadynamic recrystallization MDRX, the deformation amount of the first pass needs to reach or exceed X DRX = 0.5 or the strain that causes the peak stress. The deformation of the first pass of static recrystallization SRX must be less than X DRX = 0.5, the interval time can be set according to the required softening degree (for example, an appropriate value between 1 and 300 s) to obtain the stress-strain curves under different deformation conditions;

[0133] Input the experimental conditions of the first and second passes into the program and obtain the X calculated by the program. DRX d DRX 、X MDRX d MDRX 、X SRX and d SRX ; By sampling and analyzing single-pass and double-pass samples, SEM (backscatter mode) microstructure characterization, and using Image-pro plus software to calculate the recrystallization volume fraction and grain size, the results are consistent with the program output, and the fluctuation error is only 1%, which can prove the effectiveness of the example of the present invention.

[0134] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0135] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for rapid simulation and prediction of the structure of titanium alloy during thermal deformation, characterized in that: The method comprises the following steps: S1. Determine the initial parameters of thermal deformation of the specimen; S2. Establishing a critical criterion for the metal microstructure evolution type and a kinetic model and a grain size model corresponding to the metal microstructure type, determining the metal microstructure evolution type during thermal deformation based on the initial parameters in step S1, and calculating and outputting the volume fraction and grain size corresponding to the metal microstructure evolution type; S3, based on the volume fraction and grain size output in step S2, selecting a grain growth or mixed grain model to calculate the average grain size of the metal microstructure; S4, introducing the temperature calculation model in thermal deformation, iteratively updating the temperature input parameters of the next deformation pass in step S2; S5. Output the metal microstructure type and its corresponding volume fraction, grain size, and final average grain size of the sample after thermal deformation.

2. The method for rapid simulation and prediction of the microstructure of a titanium alloy undergoing thermal deformation according to claim 1, characterized in that: In step S1, the initial parameters of the thermal deformation of the sample are obtained through isothermal hot compression experiments. The initial parameters include deformation times of 1 to 2 times and deformation rate of 0.001s. -1 ~10s -1 The deformation amount is 20% to 60%, the initial temperature is 880℃ to 980℃, the deformation pass interval is 1 to 300s, and the initial grain size of the sample is obtained by observing the microstructure of the sample through optical microscope and scanning electron microscope.

3. The method for rapid simulation and prediction of the microstructure of a titanium alloy undergoing thermal deformation according to claim 1, characterized in that: The metal microstructure evolution types include dynamic recrystallization, metadynamic recrystallization and static recrystallization; the specific process of step S2 is: S21. Establishing critical criteria for the occurrence of dynamic recrystallization and kinetic models and grain size models corresponding to dynamic recrystallization, metadynamic recrystallization, and static recrystallization; S22, if the initial parameters of step S1 meet the critical criterion for the occurrence of dynamic recrystallization in step S21, using a dynamic recrystallization kinetic model and a dynamic recrystallization grain size model to respectively calculate the dynamic recrystallization volume fraction and the dynamic recrystallization grain size; S23, if the dynamic recrystallization volume fraction in step S22 is less than 0.95, using a dynamic recrystallization kinetic model and a dynamic recrystallization grain size model to calculate the dynamic recrystallization volume fraction and the dynamic recrystallization grain size respectively; S24, if the initial parameters of step S1 do not meet the critical criterion of dynamic recrystallization in step S21, using a static recrystallization kinetic model and a static recrystallization grain size model to calculate the static recrystallization volume fraction and the static recrystallization grain size respectively; S25. Output the volume fraction and grain size corresponding to dynamic recrystallization, meta-dynamic recrystallization and static recrystallization.

4. The method for rapid simulation and prediction of the microstructure of a titanium alloy undergoing thermal deformation according to claim 3, characterized in that: In step S21, the critical criterion for the occurrence of dynamic recrystallization is: Z≤Z lim &&e * ≥e c (1) in: Z is the Zener-Holomon parameter, Q is the deformation activation energy, T is the initial temperature, R is the gas constant, Z lim is the critical Zener-Holomon parameter for dynamic recrystallization, ε c is the critical strain for dynamic recrystallization to occur, ε * is the deformation amount, * is the number of lane changes; Based on the initial parameters in step S1, the Z parameter, the critical Zener-Hollowmon parameter, the critical strain of dynamic recrystallization, and the deformation are calculated and compared to determine whether dynamic recrystallization occurs in the metal microstructure; The kinetic models and grain size models of dynamic recrystallization, metadynamic recrystallization and static recrystallization in step S21 are: Where: X DRX is the dynamic recrystallization volume fraction, k, n are constants, ε is the deformation amount, ε 0.5 For X DRX = 0.5 when the strain; d DRX =AZ m (3) Where: d DRX is the dynamically recrystallized grain size, A and m are constants, and Z is the Zener-Holomon parameter; Where: X MDRX is the volume fraction of metadynamic recrystallization, k, n are material constants, t 0.5 For X MDRX = 0.5 hours; d MDRX =AZ m (5) Where: d MDRX Subdynamic recrystallization grain size, A and m are constants, and Z is the Zener-Hollomon parameter; Where: X SRX is the static recrystallization volume fraction, k, n are material constants, t 0.5 For X SDRX = 0.5 hours; Where: d SRX is the static recrystallization grain size, A, p, q, v are constants, d i is the grain size when the deformation pass is i, Q is the apparent activation energy, T is the deformation temperature, is the deformation rate; The volume fraction and grain size corresponding to dynamic recrystallization, metadynamic recrystallization and static recrystallization are calculated respectively through the kinetic models and grain size models of dynamic recrystallization, metadynamic recrystallization and static recrystallization.

5. The method for rapid simulation and prediction of the microstructure of a titanium alloy undergoing thermal deformation according to claim 3, characterized in that: In step S3, the selection of the grain growth or mixed crystal grain calculation model is based on the following criteria: the static recrystallization volume fraction output in step S2 is greater than 0.95 or the sum of the dynamic recrystallization volume fraction and the sub-dynamic recrystallization volume fraction is greater than 0.95; If X is satisfied SRX ≥0.95 or X DRX +X MDRX ≥0.95; the average grain size is calculated using a grain growth calculation model; the grain growth calculation model is: Where: d s is the initial recrystallized grain size, t is the residence time after recrystallization is completed, and A is a constant; If X is not satisfied SRX ≥0.95||X DRX +X MDRX ≥0.95; the average grain size is calculated using a mixed crystal grain calculation model; the mixed crystal grain calculation model is: Where: i is the deformation pass, j is the recrystallization type, j = 1 is dynamic recrystallization, j = 2 is sub-dynamic recrystallization, j = 3 is static recrystallization, d rex 、X i is the grain size and volume fraction corresponding to the recrystallization type, d j-1 is the grain size of the part that has not been recrystallized; Finally, the metal microstructure type and its corresponding grain size, volume fraction and average grain size are output.

6. The method for rapid simulation and prediction of the microstructure of a titanium alloy undergoing thermal deformation according to claim 1, characterized in that: In step S4, the temperature calculation model is used to simulate and analyze the temperature change during thermal deformation. The temperature change Δt after each pass of thermal deformation is: Δt=Δt p +Δt f -(Δt φ +Δt u +Δt d ); (11) Where, Δt p is the temperature rise during thermal deformation of the specimen, Δt f is the temperature rise caused by the friction heat generated between the molds during thermal deformation, Δt d , Δt φ , Δt u The temperature drop is caused by heat conduction between the contact part of the mold and the sample, radiation heat transfer between the sample surface and the air medium, and convection heat transfer generated during the operation of the sample.

7. The method for rapid simulation and prediction of the microstructure of a titanium alloy undergoing thermal deformation according to claim 1, characterized in that: The deformation pass is the number of times steps S2-S4 are circulated. When the deformation pass is greater than 1, the initial input in step S2 is the initial parameter of the previous step and the temperature parameter output in step S4.

8. The method for rapid simulation and prediction of the microstructure of a titanium alloy undergoing thermal deformation according to claim 1, characterized in that: The method uses the computer programming language JAVA and SpringBoot architecture as well as an established metal microstructure evolution model and temperature calculation model to complete a microstructure simulation prediction algorithm during thermal deformation, thereby achieving rapid / real-time prediction of the recrystallization type, volume fraction, and grain size of the sample during thermal deformation.

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