Titanium alloy radial forging method and system based on radial reduction rate and feed rate

Abstract

The invention relates to the technical field of titanium alloy radial forging, in particular to a titanium alloy radial forging method and system based on the radial reduction rate and the feed rate, and the method comprises the steps that a titanium alloy blank is obtained, the initial temperature, the radial reduction rate and the feed rate range are set, multi-pass forging is conducted, and a morphology distribution diagram is detected; building a forging simulation model, calculating radial strain and axial stress distribution, and evaluating material flow characteristics; segmenting the morphology distribution diagram through a local gradient and a dynamic threshold value, and generating an actual deformation distribution diagram in combination with strain information; and comparing the actual deformation uniformity distribution diagram with the simulated deformation uniformity distribution diagram to adjust the model so as to realize optimization. According to the method, the deformation uniformity and the technological parameter control precision of titanium alloy radial forging can be improved, and the dependence on high-cost equipment is reduced.

Description

Technical Field

[0001] The invention belongs to the technical field of metal material processing, and in particular relates to a titanium alloy radial forging method and system based on radial reduction rate and feed rate. Background Art

[0002] Titanium alloy materials have been widely used in aerospace, medical and chemical fields due to their excellent mechanical properties and corrosion resistance. With the continuous improvement of modern industry's demand for high-performance materials, the requirements for their mechanical properties, microstructure uniformity and yield rate are becoming increasingly stringent. Radial forging, as an important processing technology, has significant advantages in grain refinement and improving microstructure uniformity. However, the existing titanium alloy radial forging method still has shortcomings in process parameter control, production efficiency and cost optimization, which limits its large-scale industrial application. For example, the patent with publication number CN111286686B proposes a short-process preparation method for large-size bars of TC4 titanium alloy, which significantly reduces the number of forging fires and improves the yield rate through "large-tonnage press hot forging billet + large-tonnage precision forging machine radial forming". However, this method does not accurately control the key process parameters (such as radial reduction rate and feed rate) in the radial forging process, which may lead to uneven deformation and affect the consistency of product microstructure and performance. In addition, the high dependence of this method on large-tonnage equipment increases the initial investment and operating costs, and it is difficult to meet the needs of efficient and low-cost industrialization. Therefore, there is an urgent need for a titanium alloy radial forging method and system based on precise control of radial reduction rate and feed rate to achieve high efficiency and refinement of the forging process, improve product performance consistency, and reduce dependence on high-cost equipment, so as to better meet the needs of modern industry for high-performance titanium alloy materials. Summary of the invention

[0003] The present invention provides a titanium alloy radial forging method and system based on radial reduction rate and feed rate, the main purpose of which is to improve the deformation uniformity and the accuracy of process parameter control during the radial forging process of the titanium alloy, while reducing the dependence on high-cost equipment. To achieve the above purpose, the titanium alloy radial forging method based on radial reduction rate and feed rate provided by the present invention includes: obtaining a titanium alloy billet, setting an initial temperature range, a radial reduction rate range and a feed rate range, performing multiple forging passes based on the titanium alloy billet, the initial temperature range, the radial reduction rate range and the feed rate range to obtain a forged workpiece; performing surface morphology detection based on the forged workpiece to obtain a morphology distribution diagram; constructing a forging simulation model based on the titanium alloy billet, calculating the radial strain distribution and the axial stress distribution, and evaluating the material flow characteristics using the radial strain distribution and the axial stress distribution; constructing a simulated deformation uniformity distribution based on the material flow characteristics. Figure; calculate the local gradient, set the gradient dynamic threshold, segment the morphology distribution map according to the local gradient and the gradient dynamic threshold, and obtain the gradient segmentation image; set the strain dynamic threshold, calculate the strain information, segment the gradient segmentation image based on the strain dynamic threshold and the strain information, and obtain the actual deformation distribution map; mark based on the actual deformation distribution map to obtain the marked area, calculate the deformation degree of the marked area, and construct the actual deformation uniformity distribution map based on the deformation degree; adjust the forging simulation model based on the actual deformation uniformity distribution map and the simulated deformation uniformity distribution map to obtain the optimized forging simulation model, and complete the forging process optimization.

[0004] Optionally, the calculation of radial strain distribution and axial stress distribution includes: setting a parameter acquisition unit, wherein the parameter acquisition unit includes: a strain measurement unit, a stress detection unit and a temperature monitoring unit; collecting radial strain data based on the strain measurement unit, and recording axial displacement and radial displacement, and measuring axial stress distribution using the stress detection unit; monitoring forging temperature using the temperature monitoring unit, obtaining time parameters, damping coefficient and strain hardening coefficient, and calculating radial strain distribution and axial stress distribution based on the radial strain data, axial displacement, radial displacement, axial stress distribution, forging temperature, damping coefficient and strain hardening coefficient.

[0005] Optionally, the evaluating of material flow characteristics based on the radial strain distribution and the axial stress distribution includes: obtaining material yield strength, material hardness, flow factor and attenuation factor, and evaluating material flow characteristics based on the radial strain distribution, the axial stress distribution, material yield strength, material hardness, flow factor and attenuation factor.

[0006] Optionally, the calculating of the local gradient, setting of the gradient dynamic threshold, and segmentation of the morphology distribution map according to the local gradient and the gradient dynamic threshold to obtain a gradient segmented image include: setting the pixel horizontal coordinate and the pixel vertical coordinate, calculating the horizontal change rate, the vertical change rate and the normal change rate, obtaining the normal angle, the normal direction parameter, the horizontal axis flatness parameter and the vertical axis flatness parameter, and calculating the local gradient based on the pixel horizontal coordinate, the pixel vertical coordinate, the horizontal change rate, the vertical change rate, the normal change rate, the normal angle, the normal direction parameter, the horizontal axis flatness parameter and the vertical axis flatness parameter; calculating the gradient mean and the gradient standard deviation based on the local gradient, setting dynamic parameters, and setting the gradient dynamic threshold according to the gradient mean, the gradient standard deviation and the dynamic parameters; constructing a segmentation function based on the local gradient and the gradient dynamic threshold; and segmenting the morphology distribution map based on the segmentation function to obtain a gradient segmented image.

[0007] Optionally, the forging simulation model is adjusted based on the actual deformation uniformity distribution map and the simulated deformation uniformity distribution map to obtain an optimized forging simulation model, including: setting a difference threshold and a model state, obtaining a grid density, marking based on the deformation degree to obtain a deformation index, and counting the total number of intervals and the actual deformation contribution value according to the actual deformation uniformity distribution map and the simulated deformation uniformity distribution map, wherein the model state includes: a normal state and a deviation state; calculating a contribution difference value based on the deformation index, the total number of intervals, the interval deformation contribution value and the actual deformation contribution value; judging whether the contribution difference value is greater than a difference threshold; if the contribution difference value is not greater than the difference threshold, confirming that the model state of the forging simulation model is a normal state; if the contribution difference value is greater than the difference threshold, confirming that the model state of the forging simulation model is a deviation state, and increasing the grid density by 10% to obtain an adjusted density; completing the adjustment based on the adjusted density to obtain an optimized forging simulation model.

[0008] To achieve the above-mentioned purpose, the present invention also provides a titanium alloy radial forging system based on radial reduction rate and feed rate, including: a morphology detection module, used to obtain a titanium alloy billet, set an initial temperature range, a radial reduction rate range and a feed rate range, perform multiple forgings based on the titanium alloy billet, the initial temperature range, the radial reduction rate range and the feed rate range to obtain a forged workpiece, perform surface morphology detection based on the forged workpiece, and obtain a morphology distribution diagram; a simulation analysis module, used to construct a forging simulation model based on the titanium alloy billet, calculate the radial strain distribution and the axial stress distribution, and use the radial strain distribution and the axial stress distribution to evaluate the material flow characteristics; based on the material flow characteristics, construct a simulated deformation uniformity distribution Figure; an actual analysis module, used to calculate the local gradient, set the gradient dynamic threshold, segment the morphology distribution map according to the local gradient and the gradient dynamic threshold, and obtain a gradient segmentation image; set the strain dynamic threshold, calculate the strain information, segment the gradient segmentation image based on the strain dynamic threshold and the strain information, and obtain an actual deformation distribution map; mark based on the actual deformation distribution map to obtain a marked area, calculate the deformation degree of the marked area, and construct an actual deformation uniformity distribution map based on the deformation degree; a model optimization module, used to adjust the forging simulation model based on the actual deformation uniformity distribution map and the simulated deformation uniformity distribution map to obtain an optimized forging simulation model and complete the forging process optimization.

[0009] In order to solve the above problems, the present invention also provides an electronic device, which includes: a memory storing at least one instruction; a processor executing the instructions stored in the memory to implement the above-mentioned titanium alloy radial forging method based on radial reduction rate and feed rate.

[0010] In order to solve the above problems, the present invention also provides a computer-readable storage medium, in which at least one instruction is stored. The at least one instruction is executed by a processor in an electronic device to implement the above-mentioned titanium alloy radial forging method based on radial reduction rate and feed rate.

[0011] The present invention solves the problems described in the background technology. Firstly, multiple forgings are performed based on a titanium alloy billet, an initial temperature range, a radial reduction rate range and a feed rate range to obtain a forged workpiece. By setting the initial temperature range, the radial reduction rate range and the feed rate range, data support consistent with actual forging conditions is provided for subsequent operations. By obtaining the forged workpiece, the source of deformation data is ensured to be reliable, laying a foundation for the verification and optimization of the forging simulation. Secondly, surface morphology detection is performed based on the forged workpiece to obtain a morphology distribution map. The surface morphology detection technology can capture the microscopic features of the forging surface with high precision, avoid human measurement errors, and improve the credibility of the measurement data. The morphology distribution map provides accurate input data for subsequent morphology analysis, gradient segmentation and deformation evaluation. Afterwards, a forging simulation model is constructed based on the titanium alloy billet, and the radial strain distribution and axial stress distribution are calculated. The calculation of the radial strain distribution and the axial stress distribution and the construction of the forging simulation model provide a foundation for the construction of a simulated deformation uniformity distribution map. By calculating the material flow characteristics, the material flow characteristics during the forging process can be quantified. Flow behavior; further, based on the material flow characteristics, a simulated deformation uniformity distribution map is constructed, and the simulated deformation uniformity distribution map can intuitively represent the contribution value of material flow in the deformation degree interval; then, the morphology distribution map is segmented to obtain a gradient segmentation image, and the morphology distribution map is segmented by local gradient and gradient dynamic threshold, which can accurately segment the deformation area and the undeformed area, and effectively remove noise interference; then, based on the strain dynamic threshold and strain information, the gradient segmentation image is segmented to obtain the actual deformation distribution map, and the gradient segmentation image is further segmented by strain information and strain dynamic threshold, which improves the accuracy of the actual deformation distribution map and is conducive to the subsequent construction of the actual deformation uniformity distribution map; finally, the actual deformation uniformity distribution map is constructed, and the forging simulation model is adjusted according to the actual deformation uniformity distribution map and the simulated deformation uniformity distribution map, and the forging simulation model is adjusted by comparing the actual and simulated deformation uniformity distribution maps, so that the output of the forging simulation model gradually approaches the actual deformation result, realizing closed-loop optimization, and significantly improving the accuracy of the forging process. Therefore, the present invention can improve the process parameter control accuracy and deformation uniformity of titanium alloy radial forging. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 A schematic flow chart of a titanium alloy radial forging method based on radial reduction rate and feed rate provided in one embodiment of the present invention; Figure 2 A functional module diagram of a titanium alloy radial forging system based on radial reduction rate and feed rate provided in one embodiment of the present invention; Figure 3 A schematic structural diagram of an electronic device for implementing the titanium alloy radial forging method based on radial reduction rate and feed rate provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0013] The present invention provides a titanium alloy radial forging method and system based on radial reduction rate and feed rate, the core of which is to improve the deformation uniformity and reduce the dependence on high-cost equipment during the titanium alloy forging process by accurately controlling the process parameters and optimizing the forging simulation model. Figure 1 To Attachment Figure 3 Specific embodiments of the present invention are described in detail.

[0014] First, if Figure 1 As shown, in step S1, obtaining the titanium alloy billet and setting the initial temperature range, radial reduction rate range and feed rate range are the basis of the entire forging process. In actual operation, the titanium alloy billet is selected as a cylindrical billet with a diameter of 100 mm and a length of 500 mm, and is preheated to an initial temperature range of 850°C to 950°C. The radial reduction rate range is set to 5% to 10% per pass, and the feed rate range is set to 2 mm to 5 mm per second. The selection of these parameters is based on the flow characteristics of the titanium alloy material and its plastic deformation ability at high temperatures to ensure that a uniform deformation distribution can be achieved during the multi-pass forging process. Subsequently, the above parameters are input into the forging equipment for multi-pass forging to obtain the final forged workpiece. The workpiece surface is preliminarily cleaned for subsequent inspection.

[0015] Next, in step S2, a surface morphology detection is performed based on the forged workpiece to obtain a morphology distribution map. This embodiment uses a laser scanning microscope as a morphology detection tool with a resolution of 1 micron, which can capture the microscopic features of the forging surface with high precision. By scanning the surface of the forged workpiece, a morphology distribution map containing a pixel horizontal coordinate x and a pixel vertical coordinate y is generated. The grayscale value of each pixel in the figure represents the height information of the corresponding position, thereby providing accurate input data for subsequent gradient segmentation and deformation evaluation. In addition, in order to eliminate noise interference, a Gaussian filtering algorithm is used to smooth the original morphology distribution map, and its formula is: ,in is the standard deviation, which is 1.5.

[0016] In step S3, a forging simulation model is constructed based on the titanium alloy billet and the radial strain distribution and axial stress distribution are calculated. This process requires the use of finite element analysis software. The specific modeling process includes: first, a three-dimensional geometric model is established based on the actual size and material properties of the titanium alloy billet; second, boundary conditions are defined, such as axial displacement ΔL and radial displacement , and forging temperature Then, the radial strain data is collected using the strain measurement unit. , and record the axial displacement and radial displacement At the same time, the axial stress distribution is measured by the stress detection unit , and combined with the temperature monitoring unit to monitor the forging temperature in real time Finally, the radial strain distribution and axial stress distribution are calculated based on these parameters, and the formulas are: , where E is the elastic modulus, is Poisson's ratio, R is the billet radius, and L is the billet length. These calculation results can further quantify the material flow characteristics and lay the foundation for the subsequent simulation of deformation uniformity distribution map.

[0017] Step S4 involves constructing a simulated deformation uniformity distribution map based on the material flow characteristics. In this process, the material yield strength is first obtained. , material hardness H, flow factor And the attenuation factor These parameters are obtained by experimental determination or by consulting the material database. and axial stress distribution , using the formula: , evaluate the material flow characteristics M. The simulated deformation uniformity distribution diagram plots the M value distribution in different regions to intuitively represent the contribution value of material flow in the deformation degree range. This diagram not only helps to understand the behavior of materials during the forging process, but also provides an important reference for subsequent optimization.

[0018] Enter step S5, calculate the local gradient and set the gradient dynamic threshold to segment the morphology distribution map to obtain a gradient segmentation image. The key to this step is to accurately calculate the local gradient and reasonably set the threshold. Specifically, first calculate the horizontal change rate , vertical rate of change and normal rate of change ,in: , , , then, get the normal angle , normal direction parameters , horizontal axis flatness parameter and longitudinal flatness parameter , used to comprehensively calculate local gradients: , based on all pixels Value, calculate the gradient mean and the standard deviation of the gradient , and set dynamic parameters . Gradient Dynamic Threshold The calculation formula is Finally, build the split function: , the morphology distribution map is segmented to obtain a gradient segmentation image. This image can accurately distinguish the deformed area from the undeformed area and effectively remove noise interference.

[0019] In step S6, the strain dynamic threshold is set and the strain information is calculated to further segment the gradient segmentation image to obtain the actual deformation distribution map. First, the strain dynamic threshold Ts is set to 0.05. Then, the strain information of each pixel is calculated based on the gradient segmentation image. , the formula is: ,in and are the minimum and maximum values ​​in the gradient segmentation image, respectively. Next, based on the strain dynamic threshold and strain information Construct a new split function: , the gradient segmentation image is segmented twice to obtain the actual deformation distribution map. This map can more accurately reflect the distribution of the actual deformation area and provide a reliable basis for subsequent deformation uniformity analysis.

[0020] The goal of step S7 is to mark the actual deformation distribution map, obtain the marked area, calculate the deformation degree of the marked area, and construct the actual deformation uniformity distribution map. First, mark each pixel point in the actual deformation distribution map. The marking rule is that if a pixel point satisfies , it is classified as a marked area. Then, the deformation degree of the marked area is calculated , the formula is: , where N is the total number of pixels in the marked area. The actual deformation uniformity distribution diagram is drawn by using different colors or grayscale values ​​to represent the deformation degree of different areas, providing an intuitive reference for subsequent optimization.

[0021] Finally, in step S8, the forging simulation model is adjusted based on the actual deformation uniformity distribution map and the simulated deformation uniformity distribution map to obtain an optimized forging simulation model. First, set the difference threshold is 0.1, and the model state is initialized to normal state. Then, get the mesh density Based on the degree of deformation Mark and get the deformation index . According to the actual deformation uniformity distribution diagram and the simulated deformation uniformity distribution diagram, the total number of intervals is counted and actual deformation contribution , where the model state includes the normal state and the deviation state. Based on the deformation index , Total number of intervals , interval deformation contribution value and actual deformation contribution Calculate contribution variance , the formula is: ,judge Is it greater than the difference threshold? If ΔC is not greater than , then confirm that the model state of the forging simulation model is normal; if Greater than , then confirm that the model state is a deviation state, and increase the mesh density by 10% to obtain the adjusted density Based on the adjusted density After the adjustment is completed, the optimized forging simulation model is obtained. By comparing the actual and simulated deformation uniformity distribution diagrams, the forging simulation model is adjusted to make the output gradually close to the actual deformation result, achieving closed-loop optimization and significantly improving the accuracy of the forging process.

[0022] The present invention also provides a titanium alloy radial forging system based on radial reduction rate and feed rate, such as Figure 2 As shown, it includes a morphology detection module 1, a simulation analysis module 2, an actual analysis module 3 and a model optimization module 4. The morphology detection module 1 is responsible for obtaining the titanium alloy billet and setting the initial temperature range, radial reduction rate range and feed rate range, performing multiple forging based on these parameters to obtain the forged workpiece, and performing surface morphology detection to generate a morphology distribution map. The simulation analysis module 2 is used to construct a forging simulation model, calculate the radial strain distribution and axial stress distribution, and evaluate the material flow characteristics to generate a simulated deformation uniformity distribution map. The actual analysis module 3 is responsible for calculating the local gradient and setting the gradient dynamic threshold, segmenting the morphology distribution map to obtain a gradient segmentation image, setting the strain dynamic threshold and calculating the strain information, and performing secondary segmentation on the gradient segmentation image to generate an actual deformation distribution map. The model optimization module 4 adjusts the forging simulation model based on the actual deformation uniformity distribution map and the simulated deformation uniformity distribution map, and finally obtains an optimized forging simulation model to complete the forging process optimization.

[0023] In addition, if Figure 3 As shown, the present invention also provides an electronic device, including a memory 5 and a processor 6. The memory 5 is used to store at least one instruction, and the processor 6 executes the instruction stored in the memory 5 to implement the above-mentioned titanium alloy radial forging method based on radial reduction rate and feed rate. The electronic device can be a high-performance computer, and its hardware configuration includes a central processing unit with a main frequency of 3.5GHz, 16GB memory and a 1TB solid-state hard disk, running the Windows 10 operating system, and installing finite element analysis software and programming environment.

[0024] In summary, the present invention achieves a significant improvement in deformation uniformity and process parameter control during radial forging of titanium alloys through a series of steps such as multi-pass forging, surface morphology detection, forging simulation model construction, gradient segmentation, strain analysis, and model optimization, while reducing dependence on high-cost equipment, and has important industrial application value.

Claims

1. A titanium alloy radial forging method based on radial reduction rate and feed rate, characterized in that The following steps are involved: Obtain a titanium alloy billet and set the initial temperature range to 850°C to 950°C, the radial reduction rate range to 5% to 10% per pass, and the feed rate range to 2 mm to 5 mm per second; perform multiple forging passes based on the titanium alloy billet, the initial temperature range, the radial reduction rate range, and the feed rate range to obtain a forged workpiece; perform surface morphology detection based on the forged workpiece to generate a morphology distribution map; construct a forging simulation model based on the titanium alloy billet, calculate the radial strain distribution and the axial stress distribution, and use the radial strain distribution and the axial stress distribution to evaluate the material flow characteristics; construct a simulated deformation uniformity distribution map based on the material flow characteristics; calculate the local gradient and set the gradient dynamic threshold, and segment the morphology distribution map according to the local gradient and the gradient dynamic threshold to obtain a gradient segmentation image; set the strain dynamic threshold to 0.05 and calculate the strain information, and segment the gradient segmentation image based on the strain dynamic threshold and the strain information to obtain an actual deformation distribution map; Marking based on the actual deformation distribution map to obtain a marked area, calculating the deformation degree of the marked area, and constructing an actual deformation uniformity distribution map based on the deformation degree; The forging simulation model is adjusted based on the actual deformation uniformity distribution diagram and the simulated deformation uniformity distribution diagram to obtain an optimized forging simulation model.

2. The method according to claim 1, characterized in that: The calculation of radial strain distribution and axial stress distribution includes: setting a parameter acquisition unit, wherein the parameter acquisition unit includes a strain measurement unit, a stress detection unit and a temperature monitoring unit; based on the strain measurement unit, radial strain data is collected and axial displacement and radial displacement are recorded, and the axial stress distribution is measured by the stress detection unit; the forging temperature is monitored by the temperature monitoring unit to obtain time parameters, damping coefficient and strain hardening coefficient; based on the radial strain data, axial displacement, radial displacement, axial stress distribution, forging temperature, damping coefficient and strain hardening coefficient, radial strain distribution and axial stress distribution are calculated.

3. The method according to claim 1, characterized in that The evaluation of material flow characteristics based on the radial strain distribution and the axial stress distribution includes: obtaining material yield strength, material hardness, flow factor and attenuation factor; and evaluating material flow characteristics based on the radial strain distribution, the axial stress distribution, the material yield strength, the material hardness, the flow factor and the attenuation factor.

4. The method according to claim 1, characterized in that: The method of calculating the local gradient and setting the gradient dynamic threshold comprises: setting the pixel horizontal coordinate and the pixel vertical coordinate, calculating the horizontal change rate, the vertical change rate and the normal change rate; obtaining the normal angle, the normal direction parameter, the horizontal axis flatness parameter and the vertical axis flatness parameter; calculating the local gradient based on the pixel horizontal coordinate, the pixel vertical coordinate, the horizontal change rate, the vertical change rate, the normal change rate, the normal angle, the normal direction parameter, the horizontal axis flatness parameter and the vertical axis flatness parameter; calculating the gradient mean and the gradient standard deviation based on the local gradient, setting the dynamic parameters, and setting the gradient dynamic threshold according to the gradient mean, the gradient standard deviation and the dynamic parameters.

5. The method according to claim 1, characterized in that The adjusting of the forging simulation model based on the actual deformation uniformity distribution map and the simulated deformation uniformity distribution map includes: setting a difference threshold value to 0.1 and a model state, and obtaining a mesh density; marking based on the deformation degree to obtain a deformation index; counting the total number of intervals and the actual deformation contribution value according to the actual deformation uniformity distribution map and the simulated deformation uniformity distribution map; calculating a contribution difference value based on the deformation index, the total number of intervals, the interval deformation contribution value, and the actual deformation contribution value; judging whether the contribution difference value is greater than a difference threshold value; if the contribution difference value is not greater than the difference threshold value, confirming that the model state of the forging simulation model is a normal state; if the contribution difference value is greater than the difference threshold value, confirming that the model state of the forging simulation model is a deviation state, and increasing the mesh density by 10% to obtain an adjusted density; completing the adjustment based on the adjusted density to obtain an optimized forging simulation model.

6. A titanium alloy radial forging system based on radial reduction rate and feed rate, characterized in that The invention comprises a morphology detection module (1), a simulation analysis module (2), an actual analysis module (3) and a model optimization module (4); the morphology detection module (1) is used to obtain a titanium alloy billet and set an initial temperature range, a radial reduction rate range and a feed rate range, perform multiple forgings based on the titanium alloy billet, the initial temperature range, the radial reduction rate range and the feed rate range to obtain a forged workpiece, and perform surface morphology detection based on the forged workpiece to generate a morphology distribution map; the simulation analysis module (2) is used to construct a forging simulation model based on the titanium alloy billet, calculate radial strain distribution and axial stress distribution, evaluate material flow characteristics using the radial strain distribution and axial stress distribution, and construct a simulated deformation uniformity distribution map based on the material flow characteristics; the actual analysis module (3) is used to calculate a local gradient and set a gradient dynamic threshold, segment the morphology distribution map according to the local gradient and the gradient dynamic threshold to obtain a gradient segmentation image, set a strain dynamic threshold and calculate strain information, and segment the gradient segmentation image based on the strain dynamic threshold and the strain information to obtain an actual deformation distribution map; The model optimization module (4) is used to adjust the forging simulation model based on the actual deformation uniformity distribution diagram and the simulated deformation uniformity distribution diagram to obtain an optimized forging simulation model.

7. The system according to claim 6, characterized in that The shape detection module (1) uses a laser scanning microscope as a shape detection tool, and its resolution is 1 micron.

8. The system according to claim 6, characterized in that The simulation analysis module (2) constructs a forging simulation model based on finite element analysis software.

9. An electronic device, characterized in that It comprises a memory (5) and a processor (6); the memory (5) is used to store at least one instruction; the processor (6) executes the instruction stored in the memory (5) to implement the titanium alloy radial forging method based on radial reduction rate and feed rate as described in any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that At least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the titanium alloy radial forging method based on radial reduction rate and feed rate as described in any one of claims 1 to 5.

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