A method for controlling residual stress of automobile plate spring by shot peening

Abstract

The application aims to provide a kind of automobile leaf spring shot residual stress regulation method, comprising the following steps: (1) by adjusting residual stress in fatigue simulation to obtain the optimal point of automobile leaf spring fatigue life;(2) extract the optimal point residual stress state, and use sine and function fitting;(3) according to Almen test piece standard to establish shot simulation model, calculate shot intensity and saturation time, at the same time, according to the surface shape of automobile leaf spring, establish shot simulation model, calculate the residual stress distribution corresponding to shot intensity and compare with sine and function;(4) based on neural network model and process parameters and residual stress relationship, back-propagation best shot process parameters, realize the regulation of automobile leaf spring residual stress.The application uses finite element technology to regulate residual stress, improves the shot efficiency of automobile leaf spring, and greatly improves the fatigue life of leaf spring service, and finally improves the reliability and durability of the whole suspension system.

Description

Technical Field

[0001] This invention relates to a method for strengthening the surface of parts, specifically a method for controlling the residual stress on the surface of automotive leaf springs after shot peening. Background Technology

[0002] Automotive leaf springs are key elastic components supporting the main body of a vehicle and are widely used in suspension systems. During service, their primary function is to connect the frame and axle, transmitting loads and providing guidance. Through the deflection of the leaf spring, vertical vibrations and impacts are stored as strain energy in the steel spring, which is then slowly released to make the vehicle ride smoother. To enhance the fatigue life of the leaf springs, shot peening is performed on their surface to generate residual stress and extend their service life. The main process parameters affecting shot peening intensity include shot peening time, shot material, shot peening coverage, and shot energy. These parameters have a crucial impact on the residual stress distribution of the automotive leaf springs. Research shows that shot peening intensity is not always better the higher it is; there is an optimal shot peening intensity. Poor control can easily lead to insufficient or excessive shot peening, resulting in insignificant improvement in fatigue life. Because the shot peening process for leaf springs is complex and residual stress is difficult to control, it is challenging to precisely control process parameters and shot peening intensity to obtain the optimal residual stress state. Therefore, to avoid insufficient or excessive shot peening of the automotive leaf spring surface, it is necessary to reverse the process to deduce the expected residual stress field. Traditional methods for controlling residual stress in shot peening using the finite element method mainly focus on predicting residual stress based on given process parameters. This approach has limitations for components like leaf springs, which are subjected to complex stresses during processing and service. Firstly, the bending pre-stress of the leaf spring before shot peening cannot be considered during processing. Secondly, it fails to establish a correlation between residual stress and the fatigue life of the leaf spring for reverse derivation, thus making it impossible to determine whether the obtained residual stress from shot peening represents the optimal residual stress state for fatigue life. Summary of the Invention

[0003] The purpose of this invention is to provide a residual stress control method that not only considers the influence of bending prestress before shot peening of leaf springs, but also correlates residual stress with the fatigue life of leaf springs, which is beneficial for optimizing the shot peening process parameters of leaf springs and improving the fatigue life of leaf springs.

[0004] A method for controlling residual stress in shot peening of automotive leaf springs includes the following steps: Step 1: Obtain the optimal fatigue life of automotive leaf springs: Different residual stresses are introduced as initial stresses into the fatigue simulation calculation of leaf springs. Based on the fatigue simulation calculation and experimental test results, the leaf spring with the longest fatigue life is recorded as the optimal fatigue life point. Step 2, extract the optimal residual stress state: Extract the initial residual stress of the optimal leaf spring setting and fit the functional relationship between the residual stress and the stress layer depth; Step 3: Use finite element method to back-calculate shot peening intensity: Based on the geometry, material type, and shot peening equipment parameters of the single-leaf spring, a shot peening finite element model was established. Different shot quantities were set to ensure the residual stress extracted by the finite element method was consistent with the function in step two. A shot peening finite element model was established based on the Almen test specimen standard, and the curve of shot peening time versus shot peening intensity was calculated, along with the saturation time. Step 4: Back-engineer the optimal shot peening process parameters to control residual stress: Based on the saturation time determined in step three, and based on the neural network model and the relationship between process parameters and residual stress, the optimal shot peening process parameters, such as shot velocity, incident angle, and shot peening time, are deduced.

[0005] Preferably, the fatigue simulation described in step one is calculated based on the fe-safe software.

[0006] Preferably, the fitting formula for the residual stress and depth described in step two uses a sine sum formula: ; Where x represents the distance from the surface of the leaf spring. , , The six parameters, i=1,2, were obtained through regression analysis. The maximum value and depth of residual stress were obtained through an optimization method.

[0007] Preferably, in step three, the finite element model of the leaf spring shot peening is subjected to a prestress field generated by bending; the shot peening intensity is represented by the arc height h of the Almen specimen, and its relationship with the shot peening time is fitted by the following formula: ; Where a and b are parameters.

[0008] Preferably, the relationship between the process parameters described in step four and the shot peening intensity includes the following formulas: Relationship between incident angle and residual stress: ; The relationship between shot peening time and coverage is as follows: ; in, Let be the shot peening time (related to the number of projectiles), r be the radius of a single crater, and R be the number of projectiles falling on a unit area per unit time. For residual stress, The angle of incidence of the projectile. The angle of incidence is Residual stress during firing; projectile velocity Through machine learning and , , A neural network model is built using six parameters (i=1,2).

[0009] Beneficial effects: Compared with the prior art, the present invention has the following advantages and effects: 1. This invention is about constructing the relationship between residual stress and process parameters, and solving the shot peening process parameters of automotive leaf springs based on the residual stress field obtained from fatigue simulation analysis. It is a reverse process, which is targeted and can effectively avoid trial and error costs.

[0010] 2. This invention considers the bending prestress of automotive leaf springs before shot peening and realizes the mapping of the shot peening stress field to fatigue simulation, so as to establish a correlation between residual stress and the fatigue life of leaf springs, which can improve the accuracy of fatigue simulation and reduce the cost of fatigue experiments.

[0011] 3. This invention is applicable to the optimization of shot peening process parameters for automotive leaf springs, which can improve the fatigue life of leaf springs. It is also applicable to improving the fatigue life of other automotive parts that require shot peening. Attached Figure Description

[0012] Figure 1 This is a flowchart of the present invention; Figure 2 This is a schematic diagram of the geometric model of the automotive leaf spring used in this example; Figure 3 This is a schematic diagram of stress shot peening for automotive leaf springs; Figure 4 This is a schematic diagram of the finite element model for fatigue simulation used in this example; Figure 5 This is a schematic diagram of the finite element model for stress shot peening simulation used in this example; Figure 6 This is a schematic diagram of a neural network model; The attached diagram is labeled as follows: 1 is the first leaf spring with an arc height of 50mm; 2 is the second leaf spring with an arc height of 7mm; 3, 6, and 7 are intermediate pads; 4 is the center bolt; and 5 is the center nut. Detailed Implementation

[0013] The invention will be further described in detail below with reference to implementation examples.

[0014] This invention discloses a method such as Figure 1 The method for controlling residual stress in automotive leaf springs via shot peening, as shown, can be implemented through the following specific implementation scheme.

[0015] The implementation example is automotive leaf springs, such as... Figure 2As shown in the diagram. A schematic diagram of the residual stress and prestress state of each single-leaf spring after shot peening is shown below. Figure 3 As shown. Each single-leaf spring has a variable cross-sectional thickness, with a central thickness of 40 mm, and is made of 52CrMoV material with a yield strength of 1450 MPa. It undergoes shot peening treatment with cast steel shot having a yield strength of 550 MPa.

[0016] The specific implementation process is as follows: Step 1: Establish a finite element model for fatigue life prediction based on the FE-Safe software, such as... Figure 4 As shown, fatigue simulation data was established based on the SN curve of 52CrMoV material. The optimal fatigue life was obtained by adjusting different residual stress states. In this implementation case, under experimental conditions of a maximum load of 75KN and a minimum load of 7.5KN, a total of 20 residual stress states were set, with the highest fatigue life reaching 638,000 cycles.

[0017] Step two: Based on the optimal fatigue life obtained in step one, extract the residual stress and fit it using a sine function. In this implementation case, the relationship between residual stress and depth is as follows: ; Step 3: Based on the single-leaf spring's geometry, material type, and shot peening equipment parameters, establish a finite element model of the leaf spring for shot peening, and apply prestress, such as... Figure 5 As shown. Assuming the peening time for each projectile is one unit of time, a finite element model of the Almen specimen is established with different projectile numbers. The initial projectile velocity is set to 80 m / s, and the curve of projectile number versus peening intensity is calculated. In this implementation case, the number of projectiles is between 1000-3000 / mm. 2 Twenty sets of projectile counts were set up for comparison between the projectile counts, with a projectile count of 2400 / mm. 2 At that time, the spring reached saturation strength. After reaching saturation, increasing the number of projectiles further did not change the residual stress distribution of the leaf spring, which was still less than the function in step two. Therefore, it was necessary to increase the projectile velocity.

[0018] Step four: Under the condition that the shot peening intensity reaches saturation time, set 30 sets of projectile velocities (70-100 m / s) and fit the stress distribution function along the depth to obtain... , , i=1,2, six parameters, training a neural network model, such as Figure 6As shown. In this implementation case, based on the neural network model, the parameters from step two are substituted into the model, and the output result is that when the projectile velocity reaches 95m / s, the residual stress is consistent with that in step two. According to the layout of the spray gun on site, the incident angle is calculated to be 15°. Based on the relationship between the incident angle and the residual stress, the spray velocity of the spray gun is determined to be approximately 101.82m / s.

[0019] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for controlling residual stress in shot peening of automotive leaf springs, characterized in that, Includes the following steps: Step 1: Obtain the optimal fatigue life of automotive leaf springs: Different residual stresses are introduced as initial stresses into the fatigue simulation calculation of leaf springs. Based on the fatigue simulation calculation and experimental test results, the leaf spring with the longest fatigue life is recorded as the optimal fatigue life point. Step 2, extract the optimal residual stress state: Extract the initial residual stress of the optimal leaf spring setting and fit the functional relationship between the residual stress and the stress layer depth; Step 3: Use finite element method to back-calculate shot peening intensity: Based on the geometry, material type, and shot peening equipment parameters of the single-leaf spring, a shot peening finite element model is established; different shot quantities are set to make the residual stress extracted by the finite element model consistent with the function in step two; a shot peening finite element model is established according to the Almen test piece standard, the curve of shot peening time versus shot peening intensity is calculated, and the saturation time is calculated. Step 4: Back-engineer the optimal shot peening process parameters to control residual stress: Based on the saturation time determined in step three, and based on the neural network model and the relationship between process parameters and residual stress, the optimal shot peening process parameters are deduced: shot velocity, incident angle, and shot peening time. The relationship between process parameters and shot peening intensity includes the following formulas: The relationship between incident angle and residual stress is as follows: ； The relationship between shot peening time and coverage is as follows: ； in, Let be the shot peening time, r be the radius of a single crater, and R be the number of projectiles falling on a unit area per unit time. For residual stress, The angle of incidence of the projectile. The angle of incidence is Residual stress at time; C is shot peening coverage; projectile velocity Through machine learning and , , A neural network model is built using six parameters, i=1,2.

2. The method for controlling residual stress in automotive leaf springs by shot peening according to claim 1, characterized in that, The fatigue simulation described in step one is based on calculations using the fe-safe software.

3. The method for controlling residual stress in automotive leaf springs by shot peening according to claim 1, characterized in that, The fitting formula for the residual stress versus depth described in step two uses a sine sum formula: ； Where x represents the distance from the surface of the leaf spring. , , The six parameters, i=1,2, were obtained through regression analysis, and the maximum value and depth of residual stress were obtained through optimization methods.

4. The method for controlling residual stress in automotive leaf springs by shot peening according to claim 1, characterized in that, The finite element model of the leaf spring shot peening described in step three is subjected to a prestress field generated by bending; the shot peening intensity is represented by the arc height h of the Almen specimen, and its relationship with the shot peening time is fitted by the following formula: ； Where a and b are parameters.

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