A rapid modeling method for multi-shot peening finite element model of planar target

Abstract

The present invention discloses a rapid modeling method for a multi-shot peening finite element model of a planar target, which is suitable for the research on shot peening strengthening technology of large planar targets in aviation, aerospace and civil aviation. This method proposes a rapid modeling method for the highly difficult peening finite element modeling, and develops a parametric modeling system for shot peening simulation using the Python language; based on this system, the automatic segmentation of the planar target area and the automatic meshing of each area, the parameterization of boundary conditions and hierarchical unit groups can be realized; the parameterization of the projectile diameter, incident angle, incident speed, projectile position distribution, projectile grid size and the contact between the projectile and the target can be realized. For the three-factor four-level shot peening orthogonal test, it only takes 0.13 hours to construct 16 groups of finite element models with different process conditions using the rapid modeling method. One of the models was experimentally verified and it was found that the rapid modeling method greatly improved the modeling efficiency of shot peening simulation while ensuring the simulation accuracy.

Description

[0001] The present invention proposes a rapid modeling method for a finite element model of multi-shot shot peening of a planar target, which belongs to the field of numerical simulation of shot peening process and is suitable for the shot peening process simulation of large planar targets under large shot amounts and complex working conditions in the fields of aviation, aerospace and civil aviation, but is only applicable to shot peening processes using spherical shots. Background Art

[0002] Shot peening is a surface treatment technology aimed at improving the fatigue strength or stress corrosion resistance of mechanical components. Its strengthening mechanism is as follows: when the metal target is subjected to repeated and high-speed impacts of the shot peening medium (projectile), the metal on the target surface around the crater exceeds the yield limit of the material, resulting in slight cold plastic deformation, which changes the organizational structure of the material, manifested as the refinement of grains and subgrains, increased dislocation density, increased lattice distortion, and residual compressive stress on the target surface, thereby improving the surface strength and hardness of the metal target. This technology has the advantages of high adaptability, high versatility, high production efficiency, reasonable equipment costs, and low energy requirements, and is widely used in the fields of aviation, aerospace, and civil aviation.

[0003] The material behavior during shot peening is complex, and there are many factors that affect the strengthening effect. It is often difficult to achieve the best effect by optimizing the process parameters of shot peening based on trial and error methods based on experimental means, and there are defects such as low efficiency and high cost. Especially for expensive imported aviation components, the cost of process optimization based on trial and error is extremely high. Therefore, finite element modeling and simulation technology are used to study the dynamic behavior of the projectile and the rheological behavior of the target metal during shot peening. Further optimization of the shot peening process is carried out under the premise of experimental verification. This can not only reduce the number of trial peenings, thereby improving efficiency and controlling costs, but also increase the process optimization window to achieve the best optimization effect.

[0004] The simulation of multi-shot shot peening process based on finite element method requires tracking the dynamic behavior of thousands of random ejections of shot and reproducing the material rheological behavior when the shot hits the target at high speed. Therefore, there are many technical difficulties. In order to improve the feasibility of simulation, the finite element simulation of shot peening process generally simplifies the complex process of random ejection of shot into a uniform collision process. Even so, the large number of shot will significantly reduce the simulation efficiency. In order to improve the efficiency of simulation, the finite element modeling process usually simplifies the number of shot to hundreds or even dozens, and the target model is also simplified to a local micro-area on the surface of the actual component. However, shot peening finite element modeling and result analysis based on these simplifications still face great difficulties, because the shot peening simulation under each process condition needs to set the size / material parameters / injection angle / injection speed of the shot, the thickness / material constitutive / boundary conditions of the target, and the complex contact conditions between the shot and the target. After the calculation is completed, the simulation results need to be processed complexly, such as extracting the average residual stress of each layer unit of the plane target model. In this way, when optimizing and analyzing the shot peening process of large planar targets based on the finite element method, the finite element modeling and result analysis of the shot peening process under various process conditions will face a huge workload, which to a certain extent reduces the efficiency of the multi-shot shot peening simulation process. The above limitations of finite element modeling and result analysis of the multi-shot shot peening process restrict the large-scale application of shot peening simulation technology in the fields of aviation, aerospace and civil aviation, and it is urgent to develop a fast modeling and efficient post-processing method. Summary of the invention

[0005] In response to the remaining problems described in the background technology, the present invention proposes a rapid modeling method for a finite element model of multi-shot peening of planar targets. This method passes a series of control parameters in the actual shot peening process into the finite element software kernel file, and the software generates a command stream file that can be recognized by the finite element software under specific shot peening process conditions through the passed-in parameters. When certain parameters change, only the parameters corresponding to the kernel file need to be modified and the file needs to be re-executed to achieve parametric modeling, thereby realizing rapid modeling of the shot peening process, avoiding a large number of manual repetitive operations, and greatly improving the modeling efficiency of the finite element model of the multi-shot peening process.

[0006] The technical process of the invention is as follows Figure 1 As shown, it includes the following steps:

[0007] Step 1: Based on the python secondary development of the finite element software kernel file, a shot peening rapid modeling system is formed. According to the thickness size of the actual planar target and the shot peening parameters of the actual shot peening process, a series of specific control parameters are passed into the system.

[0008] Step 2: Use the target model building function provided by this system to divide the planar target into different areas and assign different grid sizes to different areas; use the post-processing stress result extraction function provided by this system to establish a target hierarchical unit group; use the projectile model building function provided by this system to divide each projectile and assign a specific grid size to achieve the establishment of a multi-projectile model and automatic meshing.

[0009] Step 3: Perform model logic operations, automatically calculate the projectile position distribution for the constructed projectile model according to the control parameters, and set a series of complex mechanical boundary conditions for the plane target and projectile model constructed in step 2.

[0010] Step 4: The finite element software reads the command stream file generated by the system to automatically establish a shot peening finite element model under specific control parameters. The stress distribution result of the planar target after shot peening can be obtained by simply submitting the finite element model for calculation.

[0011] Step 5: Use a cast steel shot peening machine to perform shot peening treatment on a planar target of a specific material under specific control parameters.

[0012] Step 6: Use X-ray diffraction plus electrolytic stripping method to measure the stress distribution results of the actual planar target material, and compare and verify with the stress distribution results of the shot peening finite element model obtained by rapid modeling under specific control parameters.

[0013] Step 7: Design a three-factor and four-level optimization process test window, use the rapid modeling method to build finite element models under multiple groups of test conditions, and verify the modeling efficiency of this method.

[0014] ● In step 1, the command stream file generated by the finite element software modeling process (recording the modeling instructions generated by each step of the user's operation) is used to extract the modeling instructions needed for the shot peening modeling process, and the modeling process instructions are reintegrated using the python language and the specific modeling parameters are replaced by variables. The integrated file is integrated into the kernel file of the finite element software to form a shot peening rapid modeling subsystem inside the finite element software. The specific control parameters in the system include the material constitutive model of the target, the target length, the target width, the target depth, and the target mesh size; the material properties, quantity, diameter, mesh size, incident angle, speed, friction coefficient, and shot peening time of the projectile.

[0015] ● In step 2, the target model function is established. Using the target size parameters passed in step 1, the system first Figure 2The Cartesian system is established in the Weiqu domain. The origin (0, 0) and the second feature point position (L1, L2) are calculated to automatically establish a two-dimensional rectangle with a stretching length of 0.5*h. The generated three-dimensional plane target is segmented. The projectile restriction area is determined by four dividing lines along the depth direction of the target, namely Line1, Line2, Line3, and Line4. Each dividing line is determined by two feature points, and the two satisfy the following relationship:

[0016] The coordinates of the two points a1 and a2 on Line1 satisfy:

[0017] a1=(0.25*L1,0,h)

[0018] a2=(0.25*L1,L2,h)

[0019] The coordinates of the two points a1 and a2 of Line2 satisfy:

[0020] a1=(0.75*L1,0,h)

[0021] a2=(0.75*L1,L2,h)

[0022] The coordinates of the two points a1 and a2 of Line3 satisfy:

[0023] a1=(0,0.25*L2,h)

[0024] a2=(L1,0.25*L2,h)

[0025] Line4 two points coordinates a1, a2 satisfy:

[0026] a1=(0,0.75*L2,h)

[0027] a2=(L1,0.75*L2,h)

[0028] Where L1 is the target length, L2 is the target width, and h is the target depth.

[0029] In order to facilitate the post-processing and extraction of stress results in the depth direction of the target, the projectile restriction area is further divided into the study area and each layer unit is grouped along the depth direction. Since the size of the study area changes with the diameter of the projectile, the study area is determined by four dividing lines along the depth direction of the target, namely Line5, Line6, Line7, and Line8. Each dividing line is determined by two characteristic points, and each point has a linear relationship with the projectile diameter:

[0030] The coordinates of the two points a1 and a2 of Line5 satisfy the following relationship:

[0031] a1=(0.25*L1+0.5*d, 0.25*L2+0.5*d, h)

[0032] a2=(0.25*L1+0.5*d, 0.75*L2-0.5*d, h)

[0033] The coordinates of the two points a1 and a2 of Line6 satisfy the following relationship:

[0034] a1=(0.75*L1-0.5*d, 0.25*L2+0.5*d, h)

[0035] a2=(0.75*L1-0.5*d, 0.75*L2-0.5*d, h)

[0036] The coordinates of the two points a1 and a2 of Line7 satisfy the following relationship:

[0037] a1=(0.25*L1+0.5*d, 0.25*L2+0.5*d, h)

[0038] a2=(0.75*L1-0.5*d, 0.25*L2+0.5*d, h)

[0039] The coordinates of the two points a1 and a2 of Line8 satisfy the following relationship:

[0040] a1=(0.25*L1+0.5*d, 0.75*L2-0.5*d, h)

[0041] a2=(0.75*L1-0.5*d, 0.75*L2-0.5*d, h)

[0042] Where d is the diameter of the projectile. The restricted area after segmentation is divided into fine grids, and coarse grids are used for other divided areas. Since there is a linear relationship between the research area of ​​the target segmentation and the projectile restriction area and the target size and the projectile diameter, it is only necessary to determine the target length, width, depth, and projectile diameter to automatically calculate the spatial coordinates of the characteristic points on the regional dividing line to complete the target area division and hierarchical unit grouping, thereby avoiding manual repeated target area segmentation operations.

[0043] The projectile model building function is established. According to the projectile diameter d passed in the control parameter, in the two-dimensional sketch of the finite element software, the arc passing through the three characteristic points is determined to satisfy the following coordinate relationship:

[0044] a1=(0,0.5*d)

[0045] a2=(0.5*d,0)

[0046] a3=(-0.5*d,0)

[0047] According to the arc determined by three points, a three-dimensional spherical projectile model is established through the rotation function. The diameter of the projectile is d, and the center position of the projectile passes through the point (0, 0, 0). The established projectile model is divided through XY and YZ surfaces, and the projectile is given a specific grid size by distributing global seeds to realize parametric modeling of the projectile model.

[0048] ●In step 3, it is assumed that a single projectile only hits the target once, each projectile has the same diameter, the incident angle and incident speed of each projectile are the same, and there is no interference between any two adjacent projectiles. For vertical incidence of the projectile, the initial position coordinates of the feature point of the projectile center satisfy the following formula:

[0049]

[0050]

[0051] z i =(h+r)+2*N*2r*random(0,1)

[0052] For non-vertical incidence of the projectile, the initial position coordinates of the projectile center feature point satisfy the following formula:

[0053]

[0054] z i =(h+r)+2*N*2r*random(0,1)

[0055]

[0056] In the formula, (x i ,y i , z i ) represents the coordinates of the feature point of the center of the i-th projectile, x ac is the length of the plane target analysis area, x rz It is represented by the length of the plane target constraint area, N is the total number of projectiles, random(0,1) represents any value between the interval [0,1], and θ represents the angle between the projectile and the plane target. The projectile incident angle parameter passed into the system and combined with the above formula can calculate the distribution position of the projectile in space; the projectile incident speed is related to the shot peening pressure, shot flow rate, and projectile diameter, and the empirical formula satisfied between them is as follows:

[0057]

[0058] In the formula, v is the average velocity of the projectile, p is the shot peening pressure, m is the shot peening flow rate, and d is the projectile diameter. The velocity components of the projectile in the X, Y, and Z directions can be calculated by combining the average incident velocity of the projectile with the incident angle. The velocities in the three directions satisfy the following relationship:

[0059] Velocity1=-v*sinθ

[0060] Velocity2=0

[0061] Velocity3=-v*cosθ

[0062] Use Python loop definition to move the center of the i-th (i is an integer, 0<i<=N) projectile from (0,0,0) to (x i ,y i , z i ), assign velocity values ​​in three directions to each projectile, and set the projectile as a rigid body constraint to establish a multi-projectile model; use the projectile restriction area defined in step 1 to set contact with the outer surface of the established projectile model, set the projectile restriction area as the slave surface, the outer surface of the projectile as the master surface, the contact attribute as penalty contact, and set the friction coefficient.

[0063] The beneficial effects of the present invention are as follows: the present invention proposes a rapid modeling method for a finite element model of multi-shot peening of a planar target, and forms a rapid modeling system for peening based on the secondary development of a finite element software kernel file in Python. The control parameters are passed into the system to automatically calculate the coordinates of the model feature points and the size of the velocity components. The feature point parameters are passed in through internal modeling instructions combined with Python loop definitions to complete the segmentation of the model and the setting of the attributes. The function of parametrically constructing a finite element model can be achieved by modifying the control parameters, and the method has high automation, flexibility, and diversity. At the same time, the method quickly builds a finite element model by passing in control parameters, which can realize secondary editing of the model and save the complicated work of building the model in the early stage. In addition, the generated finite element model can be transferred on different computers, has high commonality, and can improve the efficiency of the multi-shot peening simulation process to a certain extent, creating higher economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 Flowchart of the rapid modeling method for multi-shot peening finite element model of planar target

[0065] Figure 2 Planar target local area unit level grouping diagram

[0066] Figure 3 Meshing diagram of the finite element model of the local area of ​​the planar target

[0067] Figure 4 Mesh model diagram of projectile and target finite element

[0068] Figure 5 Contact setup diagram between projectile and target

[0069] Figure 6 Graph of numerical simulation results and experimental results DETAILED DESCRIPTION

[0070] The invention is described below with reference to the accompanying drawings and specific examples.

[0071] Step 1: Input the control parameters of the local area finite element model of the planar target during the shot peening process into the system. The target material is titanium alloy grade TC4, the target density is 4.44g / cm3, the elastic modulus is 113.8GPa, and the Poisson's ratio is 0.342. The local area size of the planar target is defined as 3mm*3mm*1.5mm, the grid size is defined as 0.015mm, and the grid transition ratio is 4. The system automatically divides the center area of ​​the target into a 0.79mm*0.79mm study area and a 1.5mm*1.5mm projectile restriction area, automatically divides the grid, and sets the units in the study area to hierarchical groups along the depth direction of the target, such as Figure 2 The system automatically constrains the displacement of the four sides and bottom of the target along the plane normal direction. The number of target units is 635040, and the local area finite element model of the plane target is as follows: Figure 3 shown.

[0072] Step 2: Construct a multi-projectile subdivision model. Use the multi-projectile model construction function of this system to input the projectile material properties, projectile density 7.8g / cm3, elastic modulus 210GPa, Poisson's ratio 0.3. Use a random algorithm to generate a random number of projectile models at a specific angle along the plane target section above the projectile restriction area; first randomly generate the first projectile with coordinates (x1, y1, z1). At this time, the number of projectiles is small, and continue to generate the i-th projectile with coordinates (x i ,y i , z i ), if the projectile interferes with the previous i-1 projectiles, the i-th projectile will be removed and regenerated. If there is no interference, the projectile will be retained and the i+1-th projectile will continue to be generated. The projectile grid size is defined as 0.02mm. The system automatically divides each projectile model and assigns a grid according to the input control parameters, realizing automatic grid division of multiple projectiles, material property assignment and distribution of projectile positions. The projectile incident direction is 75° to the target section, the shot peening pressure is set to 0.15MPa, and the shot peening distance is 150mm. The above parameters are substituted into the empirical formula of the average projectile velocity. The result shows that the average projectile incident velocity is 32m / s. The finite element mesh model of the projectile and the target is as follows: Figure 4 shown.

[0073] Step 3: Perform model logic operations, perform logic operations on the plane target subdivision model and the multi-projectile subdivision model. The system automatically selects the outer surface of the projectile and sets the grouping, constrains the projectile as a rigid body, and automatically establishes contact settings between the outer surface of the projectile and the upper surface of the restricted area on the plane target. The contact friction coefficient is input and set to 0.03. The contact settings between the projectile and the target are as follows: Figure 5 shown.

[0074] Step 4: Submit the finite element model for calculation to obtain the stress distribution results of the plane target after shot peening. In the post-processing module of the finite element analysis software, open the finite element model after the analysis operation, and use the hierarchical unit grouping set by the system in step 1 to quickly extract the residual stress value of each layer unit in the study area along the depth direction of the target in the result file of the finite element model.

[0075] Step 5: Use a cast steel shot shot peening machine of equipment model MP8000, the nozzle adopts a boron carbide Venturi type 10mm caliber short nozzle, the projectile is a high-hardness cast steel shot, the projectile diameter is 0.71mm, the shot peening air pressure is 0.15MPa, the shot flow rate is 7.8kg / min, the shot peening distance is 150mm, and the projectile incident direction is 75° with the horizontal plane of the target. The flat target material with a material of titanium alloy grade TC4 and a size of 200*200*3mm is shot peened under this process.

[0076] Step 6: Verify the reliability of the rapid modeling method based on the finite element model of the multi-shot peening process of the planar target. The experiment uses the Xstress3000 G2R X-ray stress tester and tests according to the requirements of the national standard GBT 7704-2017 "Non-destructive Testing X-ray Stress Determination Method". The residual stress of the planar target sample after shot peening is measured according to the fifth step shot peening parameters. Three different test points are selected in the area after shot peening, and the average of the three measured residual stress values ​​is taken as the surface residual stress of this group of experiments. Subsequently, the sample is stripped using an electrolytic polishing device, with a stripping depth of 20um each time, and the distribution of the residual stress after shot peening along the depth direction of the target is measured. The stress distribution under this working condition measured experimentally is compared with the stress distribution of the simulation results. Figure 6 shown.

[0077] Step 7: Design a three-factor four-level optimization process parameter test window for a flat target material with a material of titanium alloy grade TC4 and a size of 200*200*3mm. Use the rapid modeling method to build a finite element model under multiple test conditions. The specific test window is shown in the following table:

[0078]

[0079] By establishing finite element models under multiple sets of process conditions and conducting specific tests for comparison and verification, the results show that:

[0080] (1) The numerical prediction results are in good agreement with the experimental measurement data. The relative errors of the maximum surface compressive stress and the compressive stress influence depth are 13.4% and 4.8%, respectively.

[0081] (2) The rapid modeling method of the multi-shot peening finite element model of a planar target proposed in this paper can greatly improve the modeling efficiency of the model. The three-factor and four-level optimized process window finite element model can build 16 sets of models in only 0.13 hours.

[0082] In summary, the multi-shot finite element model under a specific shot peening process established through the rapid modeling of the multi-shot peening process finite element model in this paper can efficiently simulate the shot peening strengthening process of planar targets.

[0083] The above description is only a partial embodiment of the present invention and is not a limitation of the present invention in any form. Any person familiar with the professional field, without departing from the scope of the technical solution of the present invention, according to the technical essence of the present invention, any simple modification, equivalent replacement and improvement made to the above embodiment still falls within the protection scope of the claims of the present invention.

Claims

1. A rapid modeling method for a planar target multi-shot peening finite element model, characterized in that: Step 1: Based on the secondary development of the finite element software kernel file in Python, a shot peening rapid modeling system is formed. According to the thickness of the actual planar target material and the shot peening parameters of the actual shot peening process, a series of specific control parameters are input into the system; Step 2: Use the target model building function provided by this system to divide the planar target into different areas and assign different grid sizes to different areas; use the post-processing stress result extraction function provided by this system to establish a target hierarchical unit group; use the projectile model building function provided by this system to divide each projectile and assign a specific grid size to achieve the establishment of a multi-projectile model and automatic grid division; In step 2, using the target size parameters passed in step 1, the system first establishes a Cartesian system in the sketch 2D curve domain, calculates the origin (0, 0) and the second feature point position (L1, L2), automatically establishes a 2D rectangle, and stretches the length to 0.5*h. The generated 3D plane target is segmented, and the projectile restriction area is determined by four segmentation lines along the depth direction of the target, namely Line1, Line2, Line3, and Line4. Each segmentation line is determined by two feature points, and the two satisfy the following relationship: The coordinates of the two points a1 and a2 on Line1 satisfy: a1=(0.25*L1,0,h) a2=(0.25*L1,L2,h) The coordinates of the two points a1 and a2 of Line2 satisfy: a1=(0.75*L1,0,h) a2=(0.75*L1,L2,h) The coordinates of the two points a1 and a2 of Line3 satisfy: a1=(0,0.25*L2,h) a2=(L1,0.25*L2,h) Line4 two points coordinates a1, a2 satisfy: a1=(0,0.75*L2,h) a2=(L1,0.75*L2,h) Where L1 is the length of the target, L2 is the width of the target, and h is the depth of the target. The projectile restriction area is further divided into the study area and each layer unit is grouped along the depth direction. Since the size of the study area changes with the diameter of the projectile, the study area is determined by four dividing lines along the depth direction of the target, namely Line5, Line6, Line7, and Line8. Each dividing line is determined by two characteristic points, and each point has a linear relationship with the projectile diameter: The coordinates of the two points a1 and a2 of Line5 satisfy the following relationship: a1=(0.25*L1+0.5*d, 0.25*L2+0.5*d, h) a2=(0.25*L1+0.5*d, 0.75*L2-0.5*d, h) The coordinates of the two points a1 and a2 of Line6 satisfy the following relationship: a1=(0.75*L1-0.5*d, 0.25*L2+0.5*d, h) a2=(0.75*L1-0.5*d, 0.75*L2-0.5*d, h) The coordinates of the two points a1 and a2 of Line7 satisfy the following relationship: a1=(0.25*L1+0.5*d, 0.25*L2+0.5*d, h) a2=(0.75*L1-0.5*d, 0.25*L2+0.5*d, h) The coordinates of the two points a1 and a2 of Line8 satisfy the following relationship: a1=(0.25*L1+0.5*d, 0.75*L2-0.5*d, h) a2=(0.75*L1-0.5*d, 0.75*L2-0.5*d, h) Where d is the diameter of the projectile. The restricted area after segmentation is divided into fine grids, and the other divided areas are divided into coarse grids. Since there is a linear relationship between the research area of ​​the target segmentation and the projectile restriction area and the target size and projectile diameter, it is only necessary to determine the target length, width, depth, and projectile diameter to automatically calculate the spatial coordinates of the characteristic points on the regional dividing line to complete the target area division and hierarchical unit grouping, thereby avoiding manual repetitive target area segmentation operations; establish a projectile model construction function, according to the projectile diameter d passed in the control parameter, in the two-dimensional sketch of the finite element software, determine that the arc passing through the three characteristic points satisfies the coordinate relationship as follows: a1=(0,0.5*d) a2=(0.5*d,0) a3=(-0.5*d,0) According to the arc determined by the three points, a three-dimensional spherical projectile model is established through the rotation function. The diameter of the projectile is d, and the center position of the projectile passes through the point (0, 0, 0). The established projectile model is divided through the XY and YZ planes. The projectile is given a specific grid size by distributing global seeds to realize parametric modeling of the projectile model. Step 3: Perform model logic operations, automatically calculate the projectile position distribution according to the control parameters of the constructed projectile model, and set a series of complex mechanical boundary conditions for the plane target and projectile model constructed in step 2; Step 4: The finite element software reads the command stream file generated by the system and automatically establishes the shot peening finite element model under specific control parameters. The stress distribution result of the planar target after shot peening can be obtained by simply submitting the finite element model to the calculation; Step 5: Use a cast steel shot peening machine to perform shot peening treatment on a planar target of a specific material under specific control parameters; Step 6: Use X-ray diffraction plus electrolytic stripping method to measure the stress distribution results of the actual planar target, and compare and verify with the stress distribution results of the shot peening finite element model obtained by rapid modeling under specific control parameters; Step 7: Design a three-factor and four-level optimization process test window, use the rapid modeling method to build finite element models under multiple groups of test conditions, and verify the modeling efficiency of this method.

2. The rapid modeling method of a planar target multi-shot peening finite element model according to claim 1 is characterized in that In step 1, the command stream file generated by the finite element software modeling process is used to extract the modeling instructions required for the shot peening modeling process, the Python language is used to reintegrate the modeling process instructions and replace the specific modeling parameters with variables, and the integrated file is integrated into the kernel file of the finite element software to form a shot peening rapid modeling subsystem within the finite element software; the shot peening simulation process is modularized and parameterized to extract the shot peening modeling process control parameters, including material constitutive model, target length, target width, target depth, target grid size; the material properties, quantity, diameter, grid size, incident angle, speed, friction coefficient and shot peening time of the projectile can effectively establish a relationship between the control parameters and the coordinates of the characteristic points of the modeling process, thereby realizing parametric rapid modeling.

3. The method for rapid modeling of a planar target multi-shot peening finite element model according to claim 1 is characterized in that In step 3, assume that a single projectile hits the target only once, each projectile has the same diameter, the incident angle and incident speed of each projectile are the same, and there is no interference between any two adjacent projectiles. For vertical incidence of the projectile, the initial position coordinates of the feature point of the projectile center satisfy the following formula: z i =(h+r)+2*N*2r*random(0,1) For non-vertical incidence of the projectile, the initial position coordinates of the projectile satisfy the following formula: z i =(h+r)+2*N*2r*random(0,1) In the formula, (x i ,y i , z i ) represents the coordinates of the feature point of the center of the i-th projectile, x az is the length of the plane target analysis area, x rz It is represented by the length of the plane target constraint area, N is the total number of projectiles, random(0,1) represents any value between the interval [0,1], and θ represents the angle between the projectile and the plane target. The projectile incident angle parameter passed in by the system and combined with the above formula can calculate the distribution position of the projectile in space. The projectile incident speed is related to the shot peening pressure, shot flow rate, and projectile diameter, and satisfies the following formula: In the formula, v is the average velocity of the projectile, p is the shot peening pressure, m is the shot peening flow rate, and d is the projectile diameter. The velocity components of the projectile in the X, Y, and Z directions can be calculated by combining the average incident velocity of the projectile with the incident angle. The velocities in the three directions satisfy the following formula: Velocity1=-v*sinθ Velocity2=0 Velocity3=-v*cosθ Use Python loop definition to move the i-th (i is an integer, 0<i<=N) projectile from (0, 0, 0) to (x i ,y i , z i ), and assign velocity values ​​in three directions to each projectile, and set the projectile as a rigid body constraint to establish a multi-projectile model; use the projectile restriction area defined in step 1 to set contact with the outer surface of the established projectile model, set the projectile restriction area as the slave surface, the outer surface of the projectile as the master surface, the contact attribute as penalty contact, and set the friction coefficient.

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