A method for predicting shot intensity for a shot peening process

Abstract

The application provides a shot intensity prediction method for a shot peening process, which comprises establishing a millimeter-level micro model and a macro model of a standard Almen test piece, performing shot peening simulation calculation on the micro model based on predetermined shot peening process parameters to obtain residual stress along the thickness direction of the micro model, mapping the obtained residual stress to the macro model as a boundary condition, and obtaining a residual stress model along the thickness direction of the macro model through simulation calculation, performing actual shot peening test on the standard Almen test piece according to the predetermined shot peening process parameters to obtain actual residual stress data along the thickness direction, correcting the residual stress model of the macro model based on the actual residual stress data, thereby obtaining a corrected residual stress model, and then predicting the shot intensity of the shot peening on workpieces with different thicknesses under the predetermined shot process parameters by using the corrected residual stress model.

Description

Technical Field

[0001] This invention relates to shot peening strengthening processes for strengthening the surface of materials, and more particularly to a method for predicting the shot peening intensity of shot peening strengthening processes. Background Technology

[0002] Shot peening is an effective surface strengthening technology that uses a shot blasting gun to propel a stream of shot onto the surface of metal parts. This stream of shot impacts the metal parts, creating a residual stress field that strengthens the parts and enhances their fatigue resistance. Shot peening is simple to operate and produces significant results, making it widely used in aerospace, motorcycles, nuclear power, automotive, and other fields.

[0003] In actual production, process documents typically specify the required shot peening strengthening degree for different parts and structures. However, many factors influence the shot peening strengthening effect, such as material properties like density, Poisson's ratio, and Young's modulus, as well as the influence of various shot peening process parameters. Furthermore, conducting shot peening tests using a shot peening machine to individually test each shot peening process parameter to obtain the target shot peening strengthening effect is cumbersome, requiring significant manpower and resources. It also necessitates comparing a large amount of experimental data to derive reasonable shot peening parameters, making it difficult to achieve the target shot peening strengthening effect simply, quickly, and efficiently.

[0004] Existing numerical simulation methods can perform simulation calculations on shot peening processes, obtaining a large amount of simulated stress and strain data for in-depth research. Furthermore, existing numerical simulation software can not only establish shot peening simulation models for single and arrayed projectiles, but also create random projectile models to simulate the actual shot peening process, significantly improving the realism of the simulation results. However, as the simulation models become increasingly closer to the real shot peening process, the computational workload increases dramatically, causing the cost of simulating shot peening effects to rise rapidly. Accurate and effective shot peening effect simulations require high-end and expensive configurations.

[0005] Furthermore, since most of the institutions or organizations conducting research on the shot peening process are domestic and foreign universities and research institutions, they often lack industry practical knowledge and engineering experimental data to correct the simulation results. This often causes the simulation results to deviate from the actual shot peening strengthening effect, resulting in unreliable simulation results and the inability to reliably predict the shot peening strengthening effect using existing methods. Consequently, they cannot guide the recommendation of shot peening process parameters. Summary of the Invention

[0006] Therefore, the technical problem to be solved by the present invention is to overcome the problem that the simulation calculation of existing shot peening strengthening processes is too large and the simulation results deviate too much from the actual engineering results, which makes it impossible for existing simulation results to accurately and reliably predict the shot peening strengthening effect, and also impossible to recommend shot peening strengthening process parameters for a given shot peening strengthening effect. A novel method for predicting shot peening intensity for shot peening strengthening processes is proposed.

[0007] Specifically, the present invention provides a method for predicting the shot peening intensity in a shot peening strengthening process, the method comprising:

[0008] A millimeter-scale micro-model of a standard Almen specimen was established, and shot peening simulation calculations were performed on the micro-model based on predetermined shot peening process parameters to obtain the residual stress along the thickness direction on the micro-model.

[0009] A macroscopic model of a standard Almen specimen is established. The residual stress of the obtained microscopic model is mapped to the macroscopic model as a boundary condition. The residual stress model along the thickness direction on the macroscopic model is obtained through simulation calculation.

[0010] According to the predetermined shot peening strengthening process parameters, an actual shot peening strengthening test was carried out on the standard Almen specimen to obtain the actual residual stress data along the thickness direction on the standard Almen specimen.

[0011] The residual stress model is modified based on actual residual stress data to obtain the modified residual stress model. Then, the modified residual stress model is used to predict the shot peening intensity of workpieces of different thicknesses under predetermined shot peening process parameters.

[0012] According to one embodiment of the present invention, the prediction method further includes using a residual stress model of a macroscopic model to predict the shot peening intensity for shot peening workpieces of different thicknesses under predetermined shot peening process parameters.

[0013] According to another embodiment of the present invention, the shot peening strengthening process parameters include one or more of the following: shot peening material, shot peening size, shot peening speed, shot peening direction, shot peening flow rate, shot peening pressure, shot peening distance, and shot peening angle.

[0014] According to another embodiment of the present invention, the prediction method further includes changing the value of one or more of the shot peening process parameters multiple times to obtain a modified residual stress model under different shot peening process parameters; obtaining the relationship curve between the shot peening process parameters and the shot peening intensity for workpieces of the same thickness and establishing a corresponding parameter intensity relationship model.

[0015] According to another embodiment of the present invention, the prediction method further includes recommending values ​​for one or more shot peening process parameters based on the user's target shot peening intensity value, according to a parametric intensity relationship model and a relationship curve between shot peening process parameters and shot peening intensity.

[0016] According to another embodiment of the present invention, the prediction method further includes encapsulating both the modified residual stress model and the parametric strength relationship model in application software.

[0017] According to another embodiment of the present invention, the step of obtaining the residual stress along the thickness direction on the micro-model includes establishing a millimeter-level micro-model based on finite element software, performing the shot peening strengthening simulation calculation on the micro-model using the finite element calculation method, and obtaining the residual stress distribution on the micro-model.

[0018] According to another embodiment of the present invention, the step of obtaining the residual stress along the thickness direction on the micro-model further includes extracting the residual stress along the thickness direction on the micro-model using statistical methods based on the residual stress distribution of the micro-model.

[0019] According to another embodiment of the present invention, the step of obtaining actual residual stress data includes acquiring images of the surface of a standard Almen specimen subjected to shot peening strengthening test based on machine vision technology, identifying the shot peening on the surface of the standard Almen specimen to statistically calculate the shot peening coverage; and combining shot peening parameters including shot peening material, shot peening size and shot peening speed with the shot peening coverage to obtain actual residual stress data along the thickness direction on the standard Almen specimen.

[0020] According to another embodiment of the present invention, the step of obtaining actual residual stress data includes detecting the residual stress on the surface of a standard Almen specimen subjected to shot peening strengthening test using a residual stress detection device or an electrolytic corrosion peeling method, thereby obtaining actual residual stress data along the thickness direction on the standard Almen specimen.

[0021] According to another embodiment of the present invention, the step of using a residual stress model to predict shot peening strengthening includes obtaining residual stress data along the thickness direction of a workpiece of a specific thickness according to the residual stress model, calculating the arc height value corresponding to the residual stress data using a common arc height test method, and obtaining the shot peening intensity of the corresponding shot peening strengthening process based on the calculated arc height value.

[0022] According to another embodiment of the present invention, the standard Almen test piece includes standard Almen test piece A, standard Almen test piece C and standard Almen test piece N.

[0023] Based on common knowledge in the field, the above-mentioned preferred conditions can be combined arbitrarily to obtain various preferred embodiments of the present invention.

[0024] The positive and progressive effects of the above-described embodiments of the present invention are as follows: A residual stress model generated by shot peening is established through a microscopic simulation model, and then the microscopic simulation model is mapped to a macroscopic simulation model, resulting in shorter simulation calculation time, higher accuracy, and effectively reduced computational costs for shot peening process simulation analysis. Furthermore, using actual experimental data for reverse iterative optimization of the simulated residual stress model ensures that the predicted results of the corrected residual stress model are consistent with engineering practice results. In addition, this method extracts the mechanism of shot peening and encapsulates it into software, thereby facilitating accurate prediction of shot peening and precise recommendation of process parameters. Attached Figure Description

[0025] Figure 1 A flowchart of a method for predicting shot peening intensity in a shot peening strengthening process according to a preferred embodiment of the present invention.

[0026] Figure 2 According to Figure 1 Microscopic models established using prediction methods in [the text];

[0027] Figure 3 According to Figure 1 A schematic diagram of the simulation results of shot peening strengthening simulation calculation of micro-model using the prediction method in the figure;

[0028] Figure 4 According to Figure 1 A schematic diagram of the residual stress model along the thickness direction of the macroscopic model obtained by the prediction method in the paper. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings, which illustrate multiple embodiments according to this application. It should be understood that the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments described in this application without creative effort will fall within the scope of protection of this application.

[0030] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used in the description of this application is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms "comprising," "having," "having," etc., in the description, claims, and foregoing drawings of this application are open-ended terms. Therefore, "comprising" or "having" means, for example, a method having one or more steps, but not limited to having only these one or more steps.

[0031] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a mutually exclusive, independent, or alternative embodiment. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application can be combined with other embodiments.

[0032] In this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, in this application, the character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0033] Shot peening is an effective surface strengthening technology that uses a shot blasting gun to propel a stream of shot onto the surface of metal parts. This stream of shot impacts the metal parts, creating a residual stress field that strengthens the parts and enhances their fatigue resistance. Shot peening is simple to operate and produces significant results, making it widely used in aerospace, motorcycles, nuclear power, automotive, and other fields.

[0034] In actual production, process documents typically specify the required shot peening strengthening degree for different parts and structures. The indicator for evaluating the shot peening strengthening degree is the degree of bending of the Almen specimen. To accelerate production, establishing the process parameters for shot peening and the process window for determining the Almen specimen's bending degree is crucial. Clearly, the greater the shot energy, the greater the bending degree of the Almen specimen. The shot energy depends on the shot velocity direction, magnitude, and the material of the shot itself. In typical workshop production, the shot material is mostly fixed to a single material, and for ease of operation, the shot peening angle is generally not changed; therefore, the shot peening velocity distribution becomes the most significant limiting factor.

[0035] However, many factors influence the shot peening effect, such as the material's inherent properties like density, strength, Poisson's ratio, Young's modulus, and tangent modulus, as well as shot peening process parameters, including shot type, shot size, shot shape, peening velocity, peening time, and peening coverage. Due to the complexity of the shot peening process and the multifactorial nature of its effect, achieving the desired shot peening effect often requires selecting various process parameters and conducting extensive trial and error, resulting in high costs and significant time and economic expenses for shot peening.

[0036] Furthermore, determining shot peening process parameters, such as shot peening pressure, flow rate, distance, and angle, through shot peening tests using a shot peening machine is often cumbersome and requires significant manpower and resources. It takes comparing a large amount of experimental data to arrive at reasonable shot peening parameters. Therefore, this method of determining shot peening process parameters is time-consuming, costly, and slow to yield results. Moreover, once workshop equipment is upgraded, the accumulated manual experience is often not readily applicable.

[0037] Existing numerical simulation methods can perform simulation calculations on shot peening processes, obtaining a large amount of simulated stress and strain data for in-depth research. Furthermore, existing numerical simulation software can not only establish shot peening simulation models for single and arrayed projectiles, but also create random projectile models to simulate the real shot peening process, significantly improving the realism of the simulation results. However, as simulation models become increasingly closer to the actual shot peening process, the scale of the simulation calculations increases dramatically, leading to a rapid rise in the cost of simulating the shot peening effect. Accurate and effective shot peening effect simulations require high-end and expensive configurations.

[0038] Furthermore, since most of the institutions or organizations conducting research on the shot peening process are domestic and foreign universities and research institutions, they often lack industry practical knowledge and engineering experimental data to correct the simulation results. This often causes the simulation results to deviate from the actual shot peening strengthening effect, resulting in unreliable simulation results and the inability to reliably predict the shot peening strengthening effect using existing methods. Consequently, they cannot guide the recommendation of shot peening process parameters.

[0039] Therefore, this invention provides a novel method for predicting shot peening intensity in shot peening strengthening processes. This method effectively shortens simulation calculation time, reduces computational costs, and ensures that the predicted shot peening strengthening results match engineering practice results. Furthermore, this method extracts the mechanism of shot peening strengthening and encapsulates it into software, thereby facilitating accurate prediction of shot peening strengthening and precise recommendation of process parameters.

[0040] Specifically, refer to Figure 1 As shown, the method for predicting the shot peening intensity for shot peening strengthening process includes the following steps: establishing a millimeter-scale micro-model of a standard Almen specimen in finite element software, and performing shot peening strengthening simulation calculation on the micro-model based on predetermined shot peening strengthening process parameters to obtain the residual stress along the thickness direction on the micro-model.

[0041] A macroscopic model of a standard Almen specimen is established. The residual stress of the obtained microscopic model is mapped to the macroscopic model as a boundary condition. The residual stress model along the thickness direction on the macroscopic model is obtained through simulation calculation.

[0042] According to the predetermined shot peening strengthening process parameters, an actual shot peening strengthening test was carried out on the standard Almen specimen to obtain the actual residual stress data along the thickness direction on the standard Almen specimen.

[0043] The residual stress model is modified based on actual residual stress data to obtain the modified residual stress model. Then, the modified residual stress model is used to predict the shot peening intensity of workpieces of different thicknesses under predetermined shot peening process parameters.

[0044] Currently, powerful finite element simulation software such as Ansys, Abaqus, NASTRAN / PATRAN, and Comsol-Multiphysics can perform numerical simulation calculations of shot peening processes. In this embodiment, Abaqus finite element software is used for numerical simulation calculations of the shot peening process. A model of a standard Almen specimen is established in Abaqus finite element software to simulate the actual shot peening process. Residual stress data for shot peening can be obtained through simulation calculations based on the finite element method. First, the actual standard Almen specimen is scaled down proportionally to obtain the shape and size of millimeter-level Almen specimens, including standard Almen specimen A, standard Almen specimen C, and standard Almen specimen N. Subsequently, as... Figure 2 As shown, based on the shape and size of the millimeter-level standard Almen specimen, a corresponding microscopic model is established in the finite element software. By establishing a microscopic model with the same shape and size as the standard Almen specimen, simulation calculations are performed, thereby reducing the amount of computation required for simulation calculations, as well as reducing the computational cost and configuration requirements for simulation calculations.

[0045] After establishing the microscopic model, materials are selected or material property parameters, such as material density, elastic modulus, and Poisson's ratio, are input according to actual needs. In the actual shot peening process, the spatial position of the projectiles along the blasting path is random. Randomly distributed projectiles, moving at high speed, form a shot stream that impacts the workpiece surface. Random coordinates of the spatial projectile centers are generated in the finite element software, ensuring that the projectiles are randomly distributed above the model and do not interfere with each other. The impact craters on the model do not overlap, and the generated projectile bundle effectively ensures that the entire model surface is impacted. Simultaneously, shot peening process parameters such as material, size, quantity, velocity, velocity direction, flow rate, shot peening pressure, distance, and angle are set. Preferably, a rigid body is used to simulate the projectiles; the strength and hardness of this rigid body should be higher than that of the standard Alman test specimen and the workpiece.

[0046] Finite element method (FEM) simulations of random multi-projectile impact models were performed using FEM software. The outermost region was defined as an infinite element region, using infinite elements as the reflecting boundary. This boundary was designed to prevent stress wave reflections from re-entering the model and causing inaccurate results. Local meshing was performed based on the impact region and the peening angle to improve computational efficiency. A millimeter-scale micro-model was established using FEM software. Based on the Johnson-Cook model, shot peening simulations were performed on the micro-model using the FEM method to obtain results such as… Figure 3 The residual stress distribution on the microscopic model is shown. Based on this distribution, the residual stress along the thickness direction is extracted using statistical methods that calculate the mean and variance to remove bias values, resulting in the following: Figure 4 The residual stress curve along the thickness direction on the microscopic model is shown. The residual stress of the microscopic model is calculated using the following formula (1):

[0047]

[0048] In the formula, δ is the yield strength of the material; A is the yield stress of the material; B is the strain power exponent coefficient of the material; ε is the equivalent plastic strain of the material; n is the strain hardening exponent; C is the strain rate sensitivity coefficient; ε k T is the strain influence factor. k is the temperature influence factor; m is the temperature sensitivity coefficient.

[0049] Next, a macroscopic model of a standard Almen specimen is established in the finite element software. The physical field of structural mechanics is selected, and the residual stress of the obtained microscopic model is used as the input source and mapped to the macroscopic model as the boundary condition. The residual stress model of the macroscopic model is obtained by simulation calculation through the finite element method.

[0050] Following industry-standard methods, a shot peening machine is used to perform shot peening strengthening tests on standard Almen specimens with identical shot peening parameters, resulting in shot-peened standard Almen specimens. Subsequently, machine vision technology is used to acquire images of the surface of the shot-peened standard Almen specimens and identify the shot peening on the surface to statistically calculate the shot peening coverage. Combining the shot peening parameters, including shot peening material, shot peening size, and shot peening speed, with the shot peening coverage, the actual residual stress data along the thickness direction on the standard Almen specimen is obtained. Alternatively, residual stress detection equipment or electrolytic corrosion peeling method can be used to detect the residual stress on the surface of the shot-peened standard Almen specimens to obtain the actual residual stress data along the thickness direction.

[0051] Then, based on general industry knowledge, including that reducing the shot peening flow rate can increase the strengthening intensity, the relationship curve between the shot peening strengthening process parameters and the shot peening strengthening intensity is corrected. The influencing factor parameters and their corresponding residual stress curves in the shot peening strengthening process simulation are adjusted until the residual stress in the simulation calculation of the shot peening strengthening process is basically consistent with the actual residual stress data. Thus, a reliable shot peening strengthening simulation and a reliable residual stress model are obtained under the shot peening strengthening process parameters.

[0052] By using this parameter correction method, a set of shot peening process parameters will correspond to a set of residual stress curves. The steps for predicting shot peening using a residual stress model also include obtaining residual stress data along the thickness direction of a workpiece of a specific thickness based on the residual stress model, calculating the arc height value corresponding to the residual stress data using a common arc height test method, and determining the shot peening intensity based on the calculated arc height value. According to the internationally accepted arc height test method for determining shot peening intensity, the arc height value is calculated using the residual stress distribution after shot peening, thereby obtaining the shot peening intensity.

[0053] According to some preferred embodiments of the present invention, the method for predicting the shot peening intensity for shot peening strengthening processes further includes using a residual stress model of a macroscopic model to predict the shot peening intensity for shot peening workpieces of different thicknesses under predetermined shot peening process parameters.

[0054] According to some preferred embodiments of the present invention, the method for predicting the shot peening intensity in a shot peening strengthening process further includes the following steps: repeatedly changing the value of one or more of the shot peening strengthening process parameters to obtain a corrected residual stress model under different shot peening strengthening process parameters; obtaining the relationship curve between the shot peening strengthening process parameters and the shot peening strengthening intensity for workpieces of the same thickness and establishing a corresponding parameter strength relationship model. Based on the user's target shot peening intensity value, and according to the parameter strength relationship model and the relationship curve between the shot peening strengthening process parameters and the shot peening strengthening intensity, recommending values ​​for one or more shot peening strengthening process parameters.

[0055] According to some preferred embodiments of the present invention, the method for predicting the shot peening intensity in a shot peening strengthening process further includes encapsulating both the modified residual stress model and the parametric strength relationship model in the application software, thereby facilitating the prediction of the strengthening intensity magnitude under predetermined shot peening strengthening process parameters or the recommendation of shot peening intensity process parameters under a target strengthening intensity value.

[0056] This invention, targeting shot peening strengthening processes, pioneers a reverse iterative optimization method that combines simulation analysis with experimental data. This method ensures that the simulation analysis predictions match the engineering practice results. Furthermore, it extracts the shot peening strengthening mechanism and encapsulates it into software, thereby achieving accurate prediction of shot peening strengthening and precise recommendation of process parameters.

[0057] While specific embodiments of the present invention have been described above, those skilled in the art should understand that these are merely illustrative examples, and the scope of protection of the present invention is defined by the appended claims. Those skilled in the art can make various changes or modifications to these embodiments without departing from the principles and essence of the present invention, but all such changes and modifications fall within the scope of protection of the present invention.

Claims

1. A method for predicting shot peening intensity in a shot peening strengthening process, the prediction method comprising: A millimeter-scale micro-model of a standard Almen specimen is established, and a shot peening simulation calculation is performed on the micro-model based on predetermined shot peening process parameters to obtain the residual stress along the thickness direction on the micro-model; the millimeter-scale micro-model refers to the corresponding micro-model established in finite element software according to the shape and size of the millimeter-scale standard Almen specimen. A macroscopic model of a standard Almen specimen is established. The residual stress of the obtained microscopic model is mapped to the macroscopic model as a boundary condition. The residual stress model along the thickness direction on the macroscopic model is obtained through simulation calculation. According to the predetermined shot peening strengthening process parameters, an actual shot peening strengthening test is carried out on the standard Almen specimen to obtain the actual residual stress data along the thickness direction on the standard Almen specimen. Based on the actual residual stress data, the residual stress model along the thickness direction on the macroscopic model is corrected to obtain the corrected residual stress model. Then, the corrected residual stress model is used to predict the shot peening intensity of workpieces of different thicknesses under the predetermined shot peening strengthening process parameters.

2. The prediction method according to claim 1, characterized in that, The prediction method further includes: The residual stress model along the thickness direction on the macroscopic model is used to predict the shot peening intensity of workpieces of different thicknesses under the predetermined shot peening strengthening process parameters.

3. The prediction method according to claim 1, characterized in that, The shot peening strengthening process parameters include one or more of the following: shot peening material, shot peening size, shot peening speed, shot peening direction, shot peening flow rate, shot peening pressure, shot peening distance, and shot peening angle.

4. The prediction method according to claim 3, characterized in that, The prediction method further includes: By repeatedly changing the value of one or more of the shot peening process parameters, a modified residual stress model under different shot peening process parameters can be obtained. Obtain the relationship curve between shot peening process parameters and shot peening intensity for workpieces of the same thickness, and establish the corresponding parameter-intensity relationship model.

5. The prediction method according to claim 4, characterized in that, The prediction method further includes: Based on the user's target shot peening intensity value, and according to the parameter intensity relationship model and the relationship curve between the shot peening strengthening process parameters and the shot peening strengthening intensity, one or more shot peening strengthening process parameter values ​​are recommended.

6. The prediction method according to claim 5, characterized in that, The prediction method further includes: Both the modified residual stress model and the parametric strength relationship model are encapsulated in the application software.

7. The prediction method according to claim 1, characterized in that, The steps to obtain the residual stress along the thickness direction on the microscopic model include: The millimeter-scale micro-model was established using finite element software. Based on the Johnson-Cook model, shot peening simulation calculations were performed on the micro-model using the finite element method to obtain the residual stress distribution on the micro-model.

8. The prediction method according to claim 7, characterized in that, The steps for obtaining the residual stress along the thickness direction on the microscopic model also include: Based on the residual stress distribution of the micro-model, the residual stress along the thickness direction of the micro-model is extracted using statistical methods.

9. The prediction method according to claim 1, characterized in that, The steps to obtain actual residual stress data include: The machine vision technology is used to acquire images of the surface of a standard Almen specimen after shot peening strengthening test, and the shot peening coverage is statistically calculated by identifying the shot peening on the surface of the standard Almen specimen. The actual residual stress data along the thickness direction on the standard Almen specimen is obtained by combining the shot peening parameters, including shot peening material, shot peening size, and shot peening velocity, with the shot peening coverage.

10. The prediction method according to claim 1, characterized in that, The steps to obtain actual residual stress data include: The residual stress on the surface of the standard Almen specimen after shot peening is detected using residual stress detection equipment or electrolytic corrosion peeling method to obtain the actual residual stress data along the thickness direction on the standard Almen specimen.

11. The prediction method according to any one of claims 1-10, characterized in that, The steps involved in using a residual stress model to predict shot peening strengthening include: The residual stress data along the thickness direction of a workpiece of a specific thickness is obtained based on the residual stress model. The arc height value corresponding to the residual stress data is calculated using the general arc height test method. The shot peening intensity of the corresponding shot peening strengthening process is obtained based on the calculated arc height value.

12. The prediction method according to claim 1, characterized in that, The standard Almen test pieces include standard Almen test piece A, standard Almen test piece C, and standard Almen test piece N.

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