Establishment of finite element simulation model for titanium alloy bar forging and forging method

Abstract

The application discloses a kind of titanium alloy bar forging finite element simulation model establishment and forging method, comprising: based on the tensile test of sample to be tested, obtain the stress-strain curve of titanium alloy at different temperatures;Based on the stress-strain curve of titanium alloy at different temperatures, the high-temperature deformation constitutive model of titanium alloy is constructed;Based on the microstructure statistical result in tensile test result, the dynamic recrystallization constitutive model of titanium alloy is constructed;The titanium alloy material constant of high-temperature deformation constitutive model and dynamic recrystallization constitutive model is solved using genetic algorithm, and titanium alloy bar forging finite element simulation model is established.When forging, titanium alloy bar forging finite element simulation model is used for regulation and control.The temperature field, stress field and recrystallization structure result of the model are analyzed, and suitable forging process parameters can be designed, which provides important guidance and theoretical support for the development of large-size TC4 titanium alloy forging bar forging forming technology.

Description

Technical Field

[0001] This invention relates to the field of titanium alloy material production technology, and in particular to the establishment of a finite element simulation model for titanium alloy bar forging and a forging method. Background Technology

[0002] TC4 titanium alloy is a typical α+β two-phase titanium alloy, widely used in aerospace and other fields. For large-size bars, traditional forging processes suffer from significant problems such as inhomogeneous microstructure and large fluctuations in mechanical properties. This is mainly due to the extremely uneven distribution of temperature and strain fields during the deformation of large forgings, leading to significant differences in dynamic recrystallization and grain size across different parts of the forging. Under high magnification, large areas of α-phase, α-lamellae, and Widmanstätten structures exist, exhibiting crystal defects. If forging process parameters are inappropriate, the forged bars will develop numerous crystal defects, greatly reducing the mechanical properties of the forgings and causing significant losses in engineering applications. Furthermore, the microstructure of large-size TC4 forged bars is sensitive to forging process parameters, resulting in problems such as inhomogeneous microstructure, unstable mechanical properties, and difficulty in coordinating surface finish during high-temperature forging. However, by utilizing the excellent fluidity and low deformation resistance of titanium alloys at high temperatures, large-size TC4 titanium alloy forgings can be obtained using small-tonnage forging equipment.

[0003] Although existing technologies (such as patent CN120268943B) improve the impact toughness of TC4 titanium alloy by adjusting the pre-forging, final forging and heat treatment processes, and some studies have used numerical simulation to predict the forging process, these methods lack a refined mathematical model for the global uniformity control of large-size bars, and in particular lack a systematic scheme for coupling simulation and optimization of the material's high-temperature deformation behavior with the evolution of dynamic recrystallization microstructure.

[0004] Therefore, existing technologies still need improvement. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a finite element simulation model for titanium alloy bar forging and a forging method, thereby solving the technical problem of the lack of a refined mathematical model for global uniformity control of large-size bars in the prior art.

[0006] To address the aforementioned technical problems, in one aspect, some embodiments of the present invention disclose a method for establishing a finite element simulation model for titanium alloy bar forging, comprising: Step 1: Based on the tensile test of the sample to be tested, obtain the stress-strain curves of the titanium alloy at different temperatures; Step 2: Based on the stress-strain curves of titanium alloy at different temperatures, construct a high-temperature deformation constitutive model of titanium alloy; Based on the statistical results of the microstructure in the tensile test, a dynamic recrystallization constitutive model of the titanium alloy is constructed. Step 3: Use a genetic algorithm to solve for the titanium alloy material constants of the high-temperature deformation constitutive model and the dynamic recrystallization constitutive model, and establish a finite element simulation model for titanium alloy bar forging.

[0007] Furthermore, in step one, the test sample is taken from multiple regions of the titanium alloy forged bar. The tensile test was a high-temperature uniaxial tensile test, with a tensile strain rate of 0.0001 ~ 1.0 s⁻¹. -1 The forging temperature is between 600 and 1200 ℃.

[0008] Furthermore, in step two, the high-temperature deformation constitutive model is:

[0009] in, σ represents strain rate, σ represents rheological stress, Q1 represents thermal deformation activation energy, R represents gas constant, and T represents deformation temperature.

[0010] Furthermore, in step two, the constitutive equations for stress levels in the high-temperature deformation constitutive model include high-temperature deformation constitutive equations under low stress levels, high-temperature deformation constitutive equations under high stress levels, and high-temperature deformation constitutive equations under all stress levels. Low stress level refers to High stress level refers to All stress levels refer to stress levels that are not limited to a specific stress range and apply to the entire stress interval. α represents the material constant.

[0011] Furthermore, the constitutive equation for high-temperature deformation under the low stress level is:

[0012] The constitutive equation for high-temperature deformation under high stress level is:

[0013] The constitutive equations for high-temperature deformation under all stress levels are as follows:

[0014] in, σ represents strain rate, σ represents rheological stress; A, A1, A2, n, n1, α, β all represent material constants; Q represents thermal deformation activation energy, R represents gas constant, and T represents deformation temperature.

[0015] Furthermore, the dynamic recrystallization constitutive model includes a constitutive model of dynamic recrystallization critical strain and a dynamic recrystallization volume fraction model.

[0016] Furthermore, the constitutive model for the critical strain of dynamic recrystallization includes:

[0017]

[0018]

[0019] in, Indicates strain rate, Indicates the peak strain. The critical strain value is represented by d0, which is the initial grain size; a1, n1, and m1 are all material constants of the titanium alloy; Q1 represents the recrystallization activation energy, R represents the gas constant, and T represents the recrystallization temperature. The dynamic recrystallization volume fraction model includes:

[0020]

[0021] in This indicates the volume fraction of dynamic recrystallization. The critical value of strain is represented by ε, where ε represents strain. 0.5 This represents the strain value when the recrystallization volume fraction reaches 50%, where d0 is the initial grain size; a2, n2, m2, β d k d All represent the material constants of titanium alloys; Q2 represents the dynamic recrystallization activation energy, R represents the gas constant, and T represents the dynamic recrystallization temperature.

[0022] Furthermore, the dynamic recrystallization constitutive model also includes a dynamic recrystallization grain size constitutive model, which is as follows:

[0023] in, This represents the dynamic recrystallization size related to the Z parameter. a 3 and m 3 represents the material constant of titanium alloy; The Z parameter is the Zener-Hollomon parameter, determined by the strain rate. Activation energy of thermal deformation Q Gas constant R and deformation temperature T Jointly decided, the expression is .

[0024] On the other hand, this invention also discloses a method for precisely controlling the microstructure of titanium alloy bars during forging, which uses the aforementioned finite element simulation model for titanium alloy bar forging for control.

[0025] Furthermore, the material model was imported into the finite element software Deform-3D to establish a finite element simulation model for titanium alloy bar forging. By comprehensively analyzing the temperature field, strain field, stress field, and dynamic recrystallization field in the simulation results, the microstructure at different locations inside the forging was predicted. Through repeated iterative simulations, the optimal forging temperature, deformation amount, and strain rate were optimized.

[0026] By adopting the above technical solution, the present invention has at least the following beneficial effects: This invention provides a finite element simulation model for forging titanium alloy bars and a forging method. It establishes the high-temperature deformation constitutive equation and dynamic recrystallization constitutive model of TC4 titanium alloy and constructs a finite element simulation model. After analyzing the temperature field, stress field and recrystallization structure of the model, suitable forging process parameters can be designed. This can provide important guidance and theoretical support for the development of forging technology for large-size TC4 titanium alloy forged bars. Attached Figure Description

[0027] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0028] Figure 1 This is a process optimization flowchart of a forging method for precise control of the microstructure of titanium alloy bars disclosed in some embodiments of the present invention. Figure 2 In the data-driven forging method for large-size TC4 titanium alloy bars disclosed in Embodiment 1 of this invention, the forged TC4 titanium alloy is subjected to a strain rate of 0.0001 s. -1 The stress-strain curve diagram; Figure 3 In the data-driven forging method for large-size TC4 titanium alloy bars disclosed in Embodiment 1 of this invention, the forged TC4 titanium alloy is subjected to a strain rate of 0.001 s⁻¹. -1 The stress-strain curve diagram; Figure 4 In the data-driven forging method for large-size TC4 titanium alloy bars disclosed in Embodiment 1 of this invention, the forged TC4 titanium alloy is subjected to a strain rate of 0.01 s⁻¹. -1The stress-strain curve diagram; Figure 5 In the data-driven forging method for large-size TC4 titanium alloy bars with precise microstructure control disclosed in Embodiment 1 of this invention, the forged TC4 titanium alloy at a strain rate of 0.1 s⁻¹... -1 The stress-strain curve diagram; Figure 6 In the data-driven forging method for large-size TC4 titanium alloy bars disclosed in Embodiment 1 of this invention, the forged TC4 titanium alloy is subjected to a strain rate of 1.0 s⁻¹. -1 The stress-strain curve diagram; Figure 7 This is a finite element simulation model of a large-size φ800 * 1600 mm TC4 titanium alloy bar during the upsetting process in a data-driven forging method for precise control of microstructure of large-size TC4 titanium alloy bars disclosed in Embodiment 1 of the present invention. Figure 8 This is a simulation result of the temperature field during the upsetting process of forged TC4 titanium alloy in a data-driven forging method for precise control of microstructure of large-size TC4 titanium alloy bars disclosed in Embodiment 1 of the present invention. Figure 9 This is a simulation result of the strain field during the upsetting process of forged TC4 titanium alloy in a data-driven forging method for precise control of microstructure of large-size TC4 titanium alloy bars disclosed in Embodiment 1 of the present invention. Figure 10 This is a simulation result of the stress field during the upsetting process of forged TC4 titanium alloy in a data-driven forging method for precise control of microstructure of large-size TC4 titanium alloy bars disclosed in Embodiment 1 of the present invention. Figure 11 This is a simulation result of the dynamic recrystallization of the forged TC4 titanium alloy during the upsetting process in a data-driven forging method for precisely controlling the microstructure of large-size TC4 titanium alloy bars disclosed in Embodiment 1 of the present invention. Figure 12 This is a microstructure diagram of the upsetting process of TC4 titanium alloy in the forging process, which is a data-driven forging method for large-size TC4 titanium alloy bars with precise microstructure control disclosed in Embodiment 1 of the present invention. Detailed Implementation

[0029] The embodiments of this disclosure will be further described in detail below with reference to the accompanying drawings and examples. The detailed description of the embodiments and the accompanying drawings are used to illustrate the principles of this disclosure by way of example, but should not be used to limit the scope of this disclosure. This disclosure can be implemented in many different forms and is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

[0030] These embodiments are provided to make the disclosure thorough and complete, and to fully express the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specifically stated, the relative arrangement of components and steps, material composition, numerical expressions, and values ​​set forth in these embodiments should be interpreted as exemplary only and not as limiting.

[0031] It should be noted that, in the description of this disclosure, unless otherwise stated, "a plurality of" means two or more; the terms "upper," "lower," "left," "right," "inner," and "outer," etc., indicating orientation or positional relationship, are only for the convenience of describing this disclosure and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this disclosure. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0032] Furthermore, the terms "first," "second," and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different parts. "Vertical" is not strictly vertical, but within the permissible margin of error. "Parallel" is not strictly parallel, but within the permissible margin of error. Terms such as "including" or "contains" mean that the element preceding the word encompasses the element listed after the word, and do not exclude the possibility of encompassing other elements as well.

[0033] It should also be noted that, in the description of this disclosure, unless otherwise expressly specified and limited, the terms "installed," "connected," and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in this disclosure depending on the specific circumstances. When a particular device is described as being located between a first device and a second device, an intermediary device may or may not be present between the particular device and the first or second device.

[0034] All terms used in this disclosure have the same meaning as understood by one of ordinary skill in the art to which this disclosure pertains, unless otherwise specifically defined. It should also be understood that terms defined in general dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant art, and not as idealized or highly formalized, unless expressly defined herein.

[0035] Techniques, methods, and equipment known to those skilled in the art may not be discussed in detail, but where appropriate, they should be considered part of the specification.

[0036] Some embodiments of the present invention disclose a method for establishing a finite element simulation model for titanium alloy bar forging, including: Step 1: Based on the tensile test of the test sample, obtain the stress-strain curves of the titanium alloy at different temperatures. The test sample is preferably taken from multiple regions of a large-size TC4 titanium alloy forged bar, where "large-size" refers to a bar diameter of 500-800 mm. To ensure the accuracy of the experimental data, each group of test samples should contain 5-7 samples, and large-size forged bars with similar stress-strain curves in different regions should be selected as much as possible. Similar stress-strain curves mean that, at the same temperature and strain rate, the relative deviation of the rheological stress value at the same strain in each region does not exceed ±5%, preferably not exceeding ±3%. Subsequently, a high-temperature uniaxial tensile test is performed on the test sample to obtain the stress-strain curves of the TC4 titanium alloy at different temperatures.

[0037] This invention's method is not only applicable to large-size bars but also to small-size titanium alloy forging bars, though it is preferred for large-size bars. Small-size bars (e.g., diameter <200mm) experience relatively uniform deformation and cooling during forging, resulting in minimal microstructure differences. The stress-strain curves in different regions tend to be naturally similar, simplifying the steps of multi-region sampling and curve similarity screening. However, the same constitutive model and finite element method can still be used for microstructure control. This invention specifically addresses the control challenges posed by the non-uniform microstructure of large-size bars. Through multi-region sampling and curve screening, it ensures the representativeness of the material model, thereby achieving accurate prediction and optimization of the microstructure at different locations. Therefore, large-size bars best demonstrate the technical advantages of this invention, but its application to small-size bars is not excluded. Generally, in tensile tests, the tensile strain rate should be ensured to be between 0.0001 and 1.0 s⁻¹. -1 The forging temperature should be between 600 and 1200 ℃. Simultaneously, process parameters with a tensile specimen deformation greater than 60% should be selected, and a database of TC4 titanium alloy forging process parameters should be established to ensure the uniformity of large-size forgings.

[0038] Step 2: Based on the stress-strain curves of titanium alloys at different temperatures, the stress-strain curve data obtained from the tests can be input into a high-temperature mechanical property database to construct a high-temperature deformation constitutive model of titanium alloys.

[0039] The high-temperature deformation constitutive model is as follows:

[0040] in, σ represents strain rate, and σ represents rheological stress. Q 1 represents the activation energy for thermal deformation. R Represents the gas constant. T Indicates the deformation temperature.

[0041] The constitutive equations for stress levels in the high-temperature deformation constitutive model can include high-temperature deformation constitutive equations under low stress levels, high stress levels, and all stress levels. This breaks through the limitations of traditional single equations and more accurately describes the mechanical response of TC4 titanium alloy at different deformation stages.

[0042] The constitutive equation for high-temperature deformation under low stress level is:

[0043] The constitutive equation for high-temperature deformation under high stress level is:

[0044] The constitutive equations for high-temperature deformation under all stress levels are as follows:

[0045] in, σ represents strain rate, σ represents rheological stress; A, A1, A2, n, n1, α, β all represent material constants; Q represents thermal deformation activation energy, R represents gas constant, and T represents deformation temperature.

[0046] In step two, a dynamic recrystallization constitutive model of the titanium alloy is constructed based on the statistical results of the microstructure in the tensile test. This dynamic recrystallization constitutive model may include a constitutive model of the dynamic recrystallization critical strain and a dynamic recrystallization volume fraction model. The constitutive model of the dynamic recrystallization critical strain includes:

[0047]

[0048]

[0049] in, Indicates strain rate, Indicates the peak strain. The critical strain value is represented by d0, which is the initial grain size; a1, n1, and m1 are all material constants of the titanium alloy; Q1 represents the recrystallization activation energy, R represents the gas constant, and T represents the recrystallization temperature. The dynamic recrystallization volume fraction model includes:

[0050]

[0051] in This indicates the volume fraction of dynamic recrystallization. The critical value representing strain. εε represents strain. 0.5 This represents the strain value when the recrystallization volume fraction reaches 50%, where d0 is the initial grain size; a2, n2, m2, β d k d All represent the material constants of titanium alloys; Q2 represents the dynamic recrystallization activation energy, R represents the gas constant, and T represents the dynamic recrystallization temperature.

[0052] The aforementioned dynamic recrystallization constitutive model may also include a dynamic recrystallization grain size constitutive model, which is as follows:

[0053] in, The value represents the dynamic recrystallization size related to the Z parameter, and a3 and m3 represent the material constants of the titanium alloy.

[0054] By incorporating initial grain size and thermodynamic parameters into a dynamic recrystallization critical strain model, the accuracy of critical condition prediction is improved. ε can be introduced into the dynamic recrystallization volume fraction model. 0.5 The grain size equation related to the Z-parameter quantifies the correlation between recrystallization degree and process parameters. By precisely controlling grain refinement through a dynamic recrystallization model, the strength and mechanical property stability are improved, addressing a core challenge in large-size forgings.

[0055] Step 3: Use a genetic algorithm to solve for the titanium alloy material constants of the high-temperature deformation constitutive model and the dynamic recrystallization constitutive model, and establish a finite element simulation model for titanium alloy bar forging.

[0056] In this embodiment, multi-model collaborative optimization is utilized. By integrating a high-temperature deformation constitutive model, a dynamic recrystallization model, and genetic algorithm parameter optimization, a multi-physics coupled simulation system is constructed. Compared to existing studies that often focus on a single model (such as only a constitutive model or recrystallization model) or a specific process stage, this embodiment achieves a closed loop from parameter optimization to microstructure prediction through multi-model coupling and full-process simulation. Compared to traditional forging processes that rely on experience-based adjustments, this embodiment improves the scientific rigor and repeatability of process design through data-driven genetic algorithms and quantitative models. By constructing accurate high-temperature deformation constitutive models and dynamic recrystallization models, combined with multi-physics simulation, the forging process parameters are optimized, ultimately significantly improving the uniformity of the microstructure and the stability of the mechanical properties of the forged bar.

[0057] like Figure 1As shown in the illustration, this invention also discloses a method for precise control of the microstructure of titanium alloy bars during forging. This method utilizes the finite element simulation model for titanium alloy bar forging disclosed in the aforementioned embodiments. Specifically, it includes: importing the material model into the finite element software Deform-3D to establish a finite element simulation model for titanium alloy bar forging; predicting the microstructure state at different locations within the forging by comprehensively analyzing the temperature field, strain field, stress field, and dynamic recrystallization field in the simulation results; and optimizing the forging temperature, deformation amount, and strain rate through iterative simulation. The material model includes: a macroscopic ingot model, a high-temperature deformation constitutive model, a constitutive model of the critical strain for dynamic recrystallization, and a dynamic recrystallization volume fraction model. This embodiment achieves data-driven precise control of the microstructure of large-size TC4 titanium alloy bars during forging. Appropriate experimental parameters are selected before forging, and high-temperature deformation constitutive equations and dynamic recrystallization constitutive equations are further established. Based on the strain field, stress field, temperature field, dynamic recrystallization field, and microstructure evolution data of different regions during the simulated deformation process, the formability of TC4 titanium alloy materials can be quickly predicted. Forging process parameters can be efficiently optimized, and the implementation process is simple and efficient, greatly improving production efficiency. This invention overcomes the problems of large forming performance errors and large forging dimensions. Utilizing the excellent fluidity and low deformation resistance of titanium alloys at high temperatures, large-sized TC4 titanium alloy forgings can be obtained using small-tonnage forging equipment. The principle is based on two key characteristics: the deformation resistance of titanium alloys decreases sharply with increasing temperature, and the combined effect of superplastic / isothermal forging processes, which significantly reduces the required forming force. Small-tonnage equipment (such as 1600–2000 t level) is sufficient to produce large-diameter TC4 bars with diameters of over 200 mm, and the cross-section can be continuously widened through multiple forging processes. This invention, through simulation optimization to achieve precise microstructure control, enables the stable production of bar stock with diameters ranging from 500 to 800 mm on small-tonnage equipment. This overcomes the limitation of traditional processes, which typically only produce bars with diameters of 100 to 150 mm on equipment of the same tonnage. The introduction of constitutive modeling for high-temperature deformation and finite element simulation (Deform-3D) transforms the optimization of these process parameters from "trial and error" to "data-driven precision design," avoiding microstructure coarsening or cracking failures caused by improper parameters. This overcomes the challenges of large forming performance errors and large forging dimensions. This invention solves problems related to the forming of TC4 titanium alloys through simulation results and provides guidance for the material processing of other grades of titanium alloys and complex alloys. This invention demonstrates substantial innovation in three aspects: theoretical model construction, algorithm application, and process parameter optimization. It not only solves the key technical challenges of large-size forging of TC4 titanium alloys but also provides a new methodology for the processing of complex alloys, possessing high innovation and industrial application value.

[0058] Example 1 This embodiment discloses a data-driven forging method for large-size TC4 titanium alloy bars with precise microstructure control. This method optimizes the forging process parameters of the titanium alloy, improves its microstructure uniformity, and enhances the mechanical properties and stability of the TC4 titanium alloy forged bars. The entire process optimization flow is as follows: Figure 1 As shown. The main experimental equipment used includes a high-temperature tensile testing machine, forging and forging equipment, the finite element software Defrom-3D, and secondary Fortran language development. In this method, samples are taken from different regions of a large-size TC4 titanium alloy forged bar in the forged state. The test specimens are selected from large-size forged bars with similar stress-strain curves in different regions. The bar size is φ800 * 1600 mm. Subsequently, a high-temperature tensile test is performed to obtain the stress-strain curves of TC4 titanium alloy at different temperatures and strain rates. The tensile rate is ensured to be within 0.01 s. -1 Temperature at 800 o C. In this embodiment, forged TC4 titanium alloy is selected as the specific material. The obtained stress-strain curve data is input into a high-temperature mechanical property database, such as... Figures 2-6 As shown, a high-temperature deformation constitutive model of TC4 titanium alloy was constructed. The final result is as follows: Figure 7 The figure shows a finite element simulation model of a large φ800 * 1600 mm bar during the upsetting process.

[0059] The high-temperature deformation constitutive model of this embodiment is as follows:

[0060] The constitutive equation for stress level can be divided into the following three forms. The constitutive equation for high-temperature deformation under low stress level is as follows:

[0061] The constitutive equation for high-temperature deformation under high stress levels is as follows:

[0062] The constitutive equations for high-temperature deformation under all stress levels are as follows:

[0063] In this embodiment, the strain rate is solved using a genetic algorithm. The value is in the range of 0.01 s. -1 Material constants A , A 1, A 2 takes the value 2.0 × 10 12 , n , n 1 is set to 4.0; α and β are set to 1.0; thermal deformation activation energy QThe value is 250,000 J / mol, the gas constant. R The value is 8.314 J / mol, deformation temperature T The value is 800 ℃.

[0064] Based on the statistical results of the microstructure observed in the experimental results, the constructed dynamic recrystallization constitutive model mainly consists of two parts: the first part is the constitutive model of the dynamic recrystallization critical strain, and the second part is the dynamic recrystallization volume fraction model. The model for the dynamic recrystallization critical condition includes the following three formulas:

[0065]

[0066]

[0067] In this embodiment, the initial grain size d0 is determined using a genetic algorithm and can be set according to the actual size of the sample; the material constants of the titanium alloy... a 1, n 1, m 1. Values ​​are taken at 0.01, 0.5, and 0.5 respectively; recrystallization activation energy Q 1. The value is 80 kJ, the gas constant. R The value is 8.314 J / mol.

[0068] A mathematical model for the dynamic recrystallization volume fraction can be established to simulate the degree of dynamic recrystallization in titanium alloys during forging deformation. The constitutive model for the dynamic recrystallization volume fraction includes:

[0069]

[0070] In this embodiment, the initial grain size is determined using a genetic algorithm. d 0 can be set according to the actual size of the sample; material constants of titanium alloys a 2, n 2, m 2, β d , k d The values ​​are 0.0005, 0.5, 0.5, 1.1, and 1.2 respectively.

[0071] During dynamic recrystallization, the grains of titanium alloys are refined, which can improve the strength and overall mechanical properties of titanium alloy materials. Therefore, a constitutive model for grain size during dynamic recrystallization was established, as follows:

[0072] In this embodiment, the material constants of the titanium alloy are obtained by using a genetic algorithm. a 3 and m 3 takes values ​​of 9.5 and 0.05 respectively.

[0073] A genetic algorithm was used to solve the material constants of titanium alloys in the high-temperature deformation constitutive model and the dynamic recrystallization constitutive model. A finite element simulation model of TC4 titanium alloy bar forging was successfully constructed, and its strain field, stress field, temperature field, dynamic recrystallization field, and microstructure evolution were analyzed. Figures 8-12 As shown, the temperature, stress-strain and microstructure characteristics of different regions in the forging process of titanium alloy bars can be predicted, which can optimize the design of the process parameters for titanium alloy forging.

[0074] In this embodiment, the optimized material model is imported into the finite element software Deform-3D, and a model is established as follows: Figure 7 The finite element model of upsetting a large-size bar stock is shown. The simulation results (…) Figures 8-12 Through comprehensive analysis of the temperature field, strain field, stress field, and dynamic recrystallization field, the microstructure of different locations within the forging was successfully predicted. Through iterative simulations, the optimal forging temperature, deformation amount, and strain rate were optimized. Finally, a process parameter window of 30-60℃ below the phase transformation point (α+β two-phase region) and deformation amount controlled at 60%-70% was determined, effectively ensuring sufficient dynamic recrystallization (high volume fraction) and uniform microstructure throughout the forging. This invention directly addresses the industry pain point of poor microstructure uniformity in large-size TC4 titanium alloy bars. Through full-domain simulation prediction and optimized process parameters, it ensures stable and consistent mechanical properties from the core to the edges, meeting the stringent requirements of aerospace and other fields for high-performance titanium alloy components.

[0075] The embodiments of this disclosure have now been described in detail. To avoid obscuring the concept of this disclosure, some details known in the art have not been described. Those skilled in the art can fully understand how to implement the technical solutions disclosed herein based on the above description.

[0076] While specific embodiments of this disclosure have been described in detail by way of examples, those skilled in the art should understand that the examples are for illustrative purposes only and not intended to limit the scope of this disclosure. Those skilled in the art should understand that modifications can be made to the above embodiments or equivalent substitutions can be made to some technical features without departing from the scope and spirit of this disclosure. In particular, as long as there is no structural conflict, the technical features mentioned in the various embodiments can be combined in any manner.

Claims

1. A method for establishing a finite element simulation model for forging titanium alloy bars, characterized in that, include: Step 1: Based on the tensile test of the sample to be tested, obtain the stress-strain curves of the titanium alloy at different temperatures; Step 2: Based on the stress-strain curves of titanium alloy at different temperatures, construct a high-temperature deformation constitutive model of titanium alloy; Based on the statistical results of the microstructure in the tensile test, a dynamic recrystallization constitutive model of the titanium alloy is constructed. Step 3: Use a genetic algorithm to solve for the titanium alloy material constants of the high-temperature deformation constitutive model and the dynamic recrystallization constitutive model, and establish a finite element simulation model for titanium alloy bar forging.

2. The method for establishing according to claim 1, characterized in that, In step one, the test sample is taken from multiple regions of the titanium alloy forged bar. The tensile test was a high-temperature uniaxial tensile test, with a tensile strain rate of 0.0001 ~ 1.0 s⁻¹. -1 The forging temperature is between 600 and 1200 ℃.

3. The method for establishing according to claim 1, characterized in that, In step two, the high-temperature deformation constitutive model is: in, σ represents strain rate, σ represents rheological stress, Q1 represents thermal deformation activation energy, R represents gas constant, and T represents deformation temperature.

4. The method for establishing according to claim 3, characterized in that, In step two, the constitutive equations for stress levels in the high-temperature deformation constitutive model include high-temperature deformation constitutive equations under low stress levels, high-temperature deformation constitutive equations under high stress levels, and high-temperature deformation constitutive equations under all stress levels. Low stress level refers to High stress level refers to All stress levels refer to stress levels that are not limited to a specific stress range and apply to the entire stress interval. α represents the material constant.

5. The method for establishing according to claim 4, characterized in that, The constitutive equation for high-temperature deformation under low stress level is: The constitutive equation for high-temperature deformation under high stress level is: The constitutive equations for high-temperature deformation under all stress levels are as follows: in, σ represents strain rate, σ represents rheological stress; A, A1, A2, n, n1, α, β all represent material constants; Q represents thermal deformation activation energy, R represents gas constant, and T represents deformation temperature.

6. The method for establishing according to claim 1, characterized in that, The dynamic recrystallization constitutive model includes a constitutive model of dynamic recrystallization critical strain and a dynamic recrystallization volume fraction model.

7. The method for establishing according to claim 6, characterized in that, The constitutive model for the critical strain of dynamic recrystallization includes: in, Indicates strain rate, Indicates the peak strain. The critical strain value is represented by d0, which is the initial grain size; a1, n1, and m1 are all material constants of the titanium alloy; Q1 represents the recrystallization activation energy, R represents the gas constant, and T represents the recrystallization temperature. The dynamic recrystallization volume fraction model includes: in This indicates the volume fraction of dynamic recrystallization. The critical value of strain is represented by ε, where ε represents strain. 0.5 This represents the strain value when the recrystallization volume fraction reaches 50%, where d0 is the initial grain size; a2, n2, m2, β d k d All represent the material constants of titanium alloys; Q2 represents the dynamic recrystallization activation energy, R represents the gas constant, and T represents the dynamic recrystallization temperature.

8. The method for establishing according to claim 6, characterized in that, The dynamic recrystallization constitutive model further includes a dynamic recrystallization grain size constitutive model, which is as follows: in, This represents the dynamic recrystallization size related to the Z parameter. a 3 and m 3 represents the material constant of titanium alloy; The Z parameter is the Zener-Hollomon parameter, determined by the strain rate. Activation energy of thermal deformation Q Gas constant R and deformation temperature T Jointly decided, the expression is .

9. A method for forging titanium alloy bars with precise microstructure control, characterized in that, The control is performed using the finite element simulation model for titanium alloy bar forging as described in any one of claims 1-8.

10. The forging method for precise control of the microstructure of titanium alloy bars according to claim 9, characterized in that, The material model was imported into the finite element software Deform-3D to establish a finite element simulation model for titanium alloy bar forging. By comprehensively analyzing the temperature field, strain field, stress field and dynamic recrystallization field in the simulation results, the microstructure state at different locations inside the forging was predicted. Through repeated iterative simulations, the optimal forging temperature, deformation amount and strain rate were optimized.

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