Shot peening strengthening process parameter optimization method based on numerical simulation

Abstract

The invention discloses a shot peening strengthening process parameter optimization method based on numerical simulation. The method comprises the following steps that shot peening strengthening target parameters and constraint conditions are determined; a shot peening strengthening numerical simulation model is constructed, and simulation calculation is completed; establishing a response relation model of the process parameters and the target parameters; performing multi-objective optimization on the process parameters; and performing optimization verification. The DEM-FEM coupling simulation is used for replacing an actual test, a large number of workpieces and pellets are not needed, the optimization period is shortened from several weeks to several days, and the cost is greatly reduced; complex contact collision and plastic deformation are accurately simulated through the coupling model; a quantitative relation is constructed through response surface analysis, subjectivity of a cut-and-try method is avoided, an optimization result is more scientific, and precision and reliability are improved; the genetic algorithm is combined with the weighted fitness function, multiple targets are considered, the unbalance problem of single target optimization is solved, it is ensured that the comprehensive performance of a workpiece is optimal, and multi-target collaborative optimization is achieved.

Description

Technical Field

[0001] This invention relates to the field of metal surface strengthening technology, specifically to a method for optimizing shot peening process parameters based on numerical simulation. Background Technology

[0002] Shot peening is a commonly used metal surface treatment technology in fields such as machinery manufacturing and aerospace. It involves bombarding the workpiece surface with a large number of high-speed shot particles, causing plastic deformation and forming a residual compressive stress layer. This inhibits the initiation and propagation of fatigue cracks, improving the workpiece's fatigue strength, wear resistance, and corrosion resistance. The effectiveness of shot peening is mainly determined by process parameters such as shot peening pressure, peening medium, and coverage. These parameters are interrelated and closely related to the workpiece material and shot characteristics. Optimizing these process parameters is crucial for ensuring strengthening quality and reducing costs.

[0003] Currently, process parameter optimization mainly adopts traditional experimental methods, which screen the optimal parameters through multiple sets of orthogonal or single-factor experiments. However, this method has obvious drawbacks: First, the experimental cycle is long and costly, requiring the preparation of a large number of workpieces and the consumption of a large amount of pellets and energy. After parameter adjustment, repeated experiments are required, resulting in extremely low efficiency. Second, the experimental results are highly discrete and affected by equipment accuracy, environment, and operator skill, making it difficult to reflect the inherent laws between parameters and strengthening effects. Third, it cannot achieve multi-objective synergistic optimization, which easily leads to an imbalance between strengthening effect and surface quality.

[0004] With the development of numerical simulation technology, the DEM-FEM coupled method has been gradually applied to shot peening process analysis. However, existing optimization methods still have shortcomings: most do not consider the complex contact and collision between shot particles, resulting in insufficient simulation accuracy; some do not establish a quantitative relationship between parameters and strengthening objectives, relying solely on trial and error optimization, leading to poor efficiency; and they neglect practical factors such as equipment constraints, resulting in weak practicality of the optimization results. Therefore, a multi-objective collaborative optimization method that balances simulation accuracy, optimization efficiency, and production requirements is urgently needed. Thus, to address these issues, we need to design a shot peening strengthening process parameter optimization method based on numerical simulation. Summary of the Invention

[0005] The purpose of this invention is to provide a method for optimizing shot peening process parameters based on numerical simulation. This method aims to overcome the shortcomings of existing technologies, such as long testing cycles and high costs. It provides a method for optimizing shot peening process parameters based on numerical simulation, which constructs a quantitative response relationship between parameters and strengthening targets through DEM-FEM coupled simulation. It combines response surface analysis and genetic algorithms to achieve multi-objective optimization, taking into account both strengthening effect and production constraints, shortening the cycle, reducing costs, and improving the accuracy and practicality of optimization results.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for optimizing shot peening process parameters based on numerical simulation, which specifically includes the following steps: Step S1. Determine the target parameters and constraints for shot peening. Based on the workpiece's usage requirements, the core target parameters are determined as follows: surface residual compressive stress (MPa), maximum residual compressive stress (MPa), residual compressive stress layer thickness (MPa), and surface roughness (μm). Simultaneously, considering actual production conditions, the following constraints are defined: (1) Material property constraints of workpieces: Based on the material of workpieces such as 2024 aluminum alloy, determine constitutive parameters such as elastic modulus, Poisson's ratio, yield strength, strain hardening index, etc., as the basic input for simulation; (2) Shot parameter constraints: Based on the shot peening equipment and workpiece material, determine the shot material, size (0.2-0.8mm), and shape (spherical) of cast steel shot, ceramic shot, etc. (3) Equipment operating parameter constraints: Determine the threshold values ​​for shot peening air pressure (0.05-0.6MPa), spray gun distance (50-150mm), spray gun angle (45°-90°), and shot peening time.

[0007] Step S2. Construct a numerical simulation model for shot peening and complete the simulation calculation. A model is constructed using the DEM-FEM coupling method, combining the advantages of DEM in simulating particle motion and FEM in simulating solid deformation to accurately simulate the pellet impact process. Specific steps include: (1) Geometric model construction: Use Catia to build a three-dimensional model of the workpiece and simplify the workpiece's irrelevant small structure; generate spherical pellet models according to pellet parameters, with the number set to 1000-10000 according to coverage and simulation accuracy; (2) Model import and parameter assignment: Import the model into Abaqus software. The workpiece adopts the Johnson-Cook constitutive model with high strain rate and large deformation characteristics. Then, manually write the INP file and assign the corresponding material parameters to the pellets. When defining the contact relationship, the Coulomb friction model is used between the pellets and the workpiece, and the friction coefficient ranges from 0.1 to 0.3. The Hertz contact model is used between the pellets. (3) Boundary conditions and solution parameter settings: Fix the non-shot-peened surface of the workpiece and restrict all degrees of freedom; set the shot blasting speed, angle and spray gun distance according to the initial values ​​of the equipment parameters; use an explicit solver to solve the problem; (4) Simulation calculation and result output: Start the simulation to simulate the whole process of pellet injection, impact and workpiece plastic deformation, and output the residual compressive stress distribution and surface roughness as the basic data for modeling.

[0008] Step S3. Establish a response relationship model between process parameters and target parameters. Using shot peening process parameters as input and strengthening targets as response variables, a quantitative relationship is constructed using response surface methodology. The steps are as follows: (1) Variable determination: The input variables are shot peening pressure, shot size, shot peening angle, and coverage; the response variables are surface residual compressive stress Y1, maximum residual compressive stress Y2, residual compressive stress layer thickness Y3, and surface roughness Y4. (2) Experimental design: The Box-Behnken design was adopted, with 3 levels for each input variable to generate experimental groups. There are usually 30 groups for 4 variables, which takes into account both efficiency and data representativeness. (3) Simulation test: Substitute the test plan into the simulation model in sequence and record the values ​​of each group of response variables; (4) Model building and validation: The response equation Y=a0+ΣaᵢXᵢ+ΣaᵢᵢXᵢ was constructed by using Design-Expert software for quadratic multiple regression fitting. 2 +ΣaᵢⱼXᵢXⱼ (Y is the response variable, X is the input variable, and a is the coefficient); using R... 2 Verify the fitting accuracy, R 2 A value ≥0.9 is considered acceptable; otherwise, adjust the simulation parameters or repeat the experiment.

[0009] Step S4. Multi-objective optimization of process parameters Guided by the goal of optimizing the parameters and constrained by the conditions, a genetic algorithm is used to solve the problem. The steps are as follows: (1) Fitness function construction: The optimization direction is to maximize Y1, maximize Y2, maximize Y3, and minimize Y4; the fitness function F=w1×Y1'+w2×Y2'+w3×Y3'+w4×(1-Y4') is constructed by weighted summation method, where Y' is the normalized value and w is the weight (sum is 1). (2) Algorithm parameter settings: Genetic algorithm implemented in MATLAB, population size 50-100, crossover probability 0.6-0.8, mutation probability 0.01-0.05, number of iterations 50-100; (3) Optimization solution: Based on the response relationship model and the constraints, start iterative solution and output the optimal combination of process parameters.

[0010] Step S5. Optimization and Verification To ensure the reliability of the results, simulation verification was performed: (1) Simulation verification: Substitute the optimal parameters into the simulation model, re-simulate and output the response variable values; (2) Result judgment: The error standard is ±5% for surface residual compressive stress, ±5% for maximum residual compressive stress, ±5% for residual compressive stress layer thickness, and ±8% for surface roughness. If the standard is met, the optimal parameters will be output for production. (3) Repeated optimization: If the error exceeds the standard, adjust the response model coefficients or genetic algorithm parameters, and repeat steps S3-S5 until it is qualified.

[0011] Compared with the prior art, the beneficial effects of the present invention are: This invention optimizes an existing method for shot peening strengthening process parameters based on numerical simulation. By replacing actual experiments with DEM-FEM coupled simulation, it eliminates the need for a large number of workpieces and shot, reducing the optimization cycle from weeks to days and significantly lowering costs. The coupled model accurately simulates complex contact collisions and plastic deformation. Response surface analysis constructs quantitative relationships, avoiding the subjectivity of trial-and-error methods, resulting in more scientific optimization results and improved accuracy and reliability. The combination of genetic algorithms and weighted fitness functions addresses the imbalance problem of single-objective optimization, ensuring optimal overall workpiece performance and achieving multi-objective collaborative optimization. Considering production constraints such as materials and equipment, the optimized parameters are adapted to actual production, making it widely applicable to shot peening strengthening of various metal workpieces. Implemented using mature software, the process is standardized, parameters can be flexibly adjusted according to workpiece requirements, and it offers strong scalability and convenient operation. Attached Figure Description

[0012] Figure 1 This is a flowchart of a method for optimizing shot peening process parameters based on numerical simulation, as described in this invention. Detailed Implementation

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0014] The present invention provides a method for optimizing shot peening process parameters based on numerical simulation. The overall implementation process follows the core steps of target determination, model construction, response relationship establishment, multi-objective optimization, and optimization verification. Furthermore, a feedback adjustment mechanism is set in the response relationship model establishment and optimization verification stages to ensure that the final output process parameter combination meets the actual production and performance requirements of shot peening.

[0015] In actual implementation, the first step, S1, is to determine the target parameters and constraints for shot peening. This requires considering the actual service environment of the workpiece and the core requirements for its surface performance in engineering applications to select the core target parameters for shot peening. Simultaneously, based on the workpiece's basic material characteristics, the rated operating performance of the accompanying shot peening equipment, and the selection specifications for the shot peening medium, the range of constraints for each dimension must be clearly defined. The selection of target parameters must align with the workpiece's usage requirements, and the setting of constraints must balance production feasibility and equipment operational safety, thus defining clear boundaries for subsequent numerical simulations and parameter optimization.

[0016] After completing step S1, proceed to step S2 to construct the shot peening numerical simulation model and complete the simulation calculation. First, a geometric model of the workpiece is built using professional 3D modeling software. Small structures on the workpiece that do not affect the shot peening effect are reasonably simplified to reduce the complexity of subsequent simulation calculations. The constructed geometric model is imported into professional finite element simulation software. A constitutive model suitable for the plastic deformation characteristics of its material is assigned to the workpiece. The INP file is edited to assign the corresponding physical parameters to the shot particles. According to the actual contact characteristics of the shot peening process, contact models between the shot particles and the workpiece, and between shot particles themselves, are defined, and contact-related parameters are set appropriately. Based on the tooling fixing method in actual production, fixed constraints are set on the non-shot-peened surfaces of the workpiece to restrict their corresponding degrees of freedom. Then, based on the initial values ​​of the equipment process parameters set in step S1, the shot peening-related parameters are configured. After completing the model construction and parameter settings, a suitable solver is selected to start the simulation calculation, simulating the complete process from shot peening to bombarding the workpiece surface and causing plastic deformation of the workpiece surface. After the simulation, the simulation result data corresponding to the target parameters is extracted and output as the basic data for subsequently constructing the response relationship model.

[0017] Next, step S3 is executed to establish a response relationship model between process parameters and target parameters. First, parameters that significantly influence the target parameters of shot peening are selected from the shot peening process parameters as input variables, and the target parameters of shot peening determined in step S1 are used as response variables. The Box-Behnken design method is used to set reasonable horizontal gradients for each input variable, generating a simulation test plan that balances experimental efficiency and data representativeness. The number of test groups is adjusted according to the number of input variables. Following the generated test plan, each group of process parameters is sequentially substituted into the numerical simulation model constructed in step S2 to conduct simulation experiments, accurately recording the values ​​of each response variable corresponding to each test group. After organizing the input and response variable data for all test groups, the data is imported into professional experimental design and data analysis software for quadratic multiple regression fitting, constructing a quadratic regression equation between the input variables and each response variable, i.e., the response relationship model between process parameters and target parameters. The coefficient of determination R is used. 2 The fitting accuracy of the model is verified. If the fitting accuracy does not reach the preset threshold, the relevant solution parameters and modeling parameters of the numerical simulation are adjusted, and the simulation calculation is carried out again in step S2. The relevant operations in this step are executed again until the fitting accuracy of the constructed response relationship model meets the requirements.

[0018] The subsequent step, S4, involves multi-objective optimization of process parameters. Based on the optimization directions of each objective parameter determined in step S1, a fitness function is constructed using a weighted summation method, transforming the multi-objective optimization problem into a single-objective optimization problem. The weight coefficients of each objective parameter are set according to the actual needs and priorities of shot peening, with the sum of the weight coefficients being 1. A genetic algorithm solution model is built in professional algorithm simulation software. Based on optimization requirements and computational efficiency, algorithm parameters such as population size, crossover probability, mutation probability, and number of iterations are reasonably set within a preset range. The response relationship model constructed in step S3 is used as the basis for the algorithm solution, and the constraints set in step S1 are used as boundary limits for the input variables. The genetic algorithm is then started for iterative solution. Through multiple iterations of the algorithm, the optimal combination of shot peening process parameters is output.

[0019] Finally, step S5 is executed for optimization and verification. The optimal combination of shot peening process parameters output in step S4 is completely substituted into the shot peening strengthening numerical simulation model constructed in step S2, keeping other parameters of the model unchanged. The simulation calculation is carried out again, and the simulation results of each target parameter under this parameter combination are extracted and output. The simulation results are compared with the target parameter requirements determined in step S1. The simulation results of each target parameter are verified according to the preset error standard to see if they meet the requirements. If the simulation results of all target parameters are within the error allowable range, the optimal process parameter combination is determined to be effective and can be used as the final optimization result for actual shot peening strengthening production. If the simulation results of some target parameters exceed the error allowable range, the regression coefficients of the response relationship model or the core parameters of the genetic algorithm are adjusted, and the process is returned to step S3 to construct the response relationship model. The subsequent steps are then executed sequentially until the simulation verification results meet the target parameter requirements.

[0020] The method of this invention can accurately optimize the shot peening strengthening process parameters for workpieces of different materials, taking into account both the strengthening effect and actual production constraints. It has high optimization accuracy and strong practicality, and can be widely applied to the shot peening strengthening production of various metal workpieces.

[0021] Although the present invention has been described above with reference to embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of the invention. In particular, as long as there is no structural conflict, the features in the disclosed embodiments can be combined with each other in any manner. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for optimizing shot peening process parameters based on numerical simulation, characterized in that, Includes the following steps: S1. Determine the target parameters and constraints for shot peening strengthening. The target parameters include residual compressive stress on the workpiece surface, maximum residual compressive stress, thickness of the residual compressive stress layer, and surface roughness. The constraints include workpiece material properties, shot parameter range, and equipment operating parameter thresholds. S2. Construct a numerical simulation model for shot peening strengthening. Based on the coupling principle of discrete element method and finite element method, input the workpiece geometric model, material constitutive parameters and initial values ​​of shot parameters, define the contact relationship and boundary conditions between shot and workpiece, and between shot and shot, set the simulation solution parameters and complete the simulation calculation, and output the simulation results of residual compressive stress, maximum residual compressive stress, residual compressive stress layer thickness and surface roughness of the workpiece surface. S3. Establish a response relationship model between process parameters and target parameters. Using shot peening process parameters as input variables and shot peening target parameters as response variables, design an experimental scheme using the Box-Behnken design method. Combine the numerical simulation results from step S2, construct a quadratic regression equation between input variables and response variables through response surface analysis to verify the fitting accuracy of the response relationship model. If the fitting accuracy is lower than the preset threshold, adjust the relevant simulation parameters and repeat steps S2-S3 until the fitting accuracy meets the requirements. S4. Multi-objective optimization of process parameters: Taking the optimization of the target parameters in step S1 as the optimization objective and the constraint conditions as boundary restrictions, a genetic algorithm is introduced to solve the response relationship model established in step S3 and output the optimal combination of shot peening process parameters. S5. Optimization and verification: Substitute the optimal process parameter combination output in step S4 into the numerical simulation model for simulation verification. If the simulation results meet the target parameter requirements, output the optimal process parameter combination as the final optimization result. If the requirements are not met, adjust the parameters of the response relationship model or optimize the algorithm parameters, and repeat steps S3-S5 until the simulation results meet the target parameter requirements.

2. The method for optimizing shot peening process parameters based on numerical simulation according to claim 1, characterized in that, In step S1, the workpiece material properties include elastic modulus, Poisson's ratio, yield strength and strain hardening index, the shot parameters include shot material, shot size and shot shape, and the equipment process parameters include shot peening air pressure, shot gun angle and shot peening time.

3. The method for optimizing shot peening process parameters based on numerical simulation according to claim 1, characterized in that, In step S2, the material constitutive parameters are described using the Johnson-Cook constitutive model. In the contact relationship, the Coulomb friction model is used to define the relationship between the pellets and the workpiece, with the friction coefficient ranging from 0.1 to 0.

3. The Hertz contact model is used between the pellets. The simulation solution parameters include the solver type and the total simulation time, wherein the solver type is an explicit solver.

4. The method for optimizing shot peening process parameters based on numerical simulation according to claim 1, characterized in that, In step S3, the preset fitting accuracy threshold is 0.9, and the coefficient of determination R is used. 2 To verify the fitting accuracy of the response relationship model, when R... 2 When the value is ≥0.9, the model fitting accuracy is deemed to meet the requirements. The input variables of the shot peening process parameters include shot peening air pressure, shot size, shot peening angle and coverage. Each input variable is set with 3 levels. Test groups are generated based on the Box-Behnken design method, and each test group corresponds to one numerical simulation.

5. The method for optimizing shot peening process parameters based on numerical simulation according to claim 1, characterized in that, In step S4, the parameters of the genetic algorithm are set as follows: population size 50-100, crossover probability 0.6-0.8, mutation probability 0.01-0.05, number of iterations 50-100, fitness function constructed based on weighted summation of target parameters, and the weight of each target parameter is set according to the actual shot peening strengthening requirements.

6. The method for optimizing shot peening process parameters based on numerical simulation according to claim 1, characterized in that, In step S5, the verification criteria for the simulation results are: the surface residual compressive stress error does not exceed ±5%, the maximum residual compressive stress error does not exceed ±5%, the residual compressive stress layer thickness error does not exceed ±5%, and the surface roughness error does not exceed ±8%. If the error exceeds the above range, the quadratic regression coefficient of the response relationship model is adjusted, or the crossover probability and mutation probability of the genetic algorithm are optimized, and the optimization solution is performed again.

7. A method for optimizing shot peening process parameters based on numerical simulation according to any one of claims 1-6, characterized in that, The numerical simulation model was constructed using Abaqus simulation software, and the fitting and optimization of the response relationship model were achieved using Design-Expert software and MATLAB software in a collaborative manner.

8. A method for optimizing shot peening process parameters based on numerical simulation according to any one of claims 1-6, characterized in that, In step S2, the initial values ​​of the pellet parameters are set as follows: the pellet shape is spherical and the pellet radius is 0.2-0.8 mm; the initial values ​​of the equipment process parameters are set as follows: shot peening air pressure is 0.05-0.6 MPa, spray gun distance is 50-150 mm, and spray gun angle is 45°-90°.

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