Typical component shot peening strengthening process design method

Abstract

The invention provides a typical component shot peening strengthening process design method. The typical component shot peening strengthening process design method mainly comprises the steps that 1, typical component shot peening strengthening parameter design technical requirements are determined; 2, a reasonable shot blasting material is selected based on typical component technical requirements; 3, establishing a shot blasting process parameter optimization model based on the neural network; and 4, optimizing and solving shot blasting process parameters on the basis of the surface strengthening requirements of the part. The method is suitable for shot peening strengthening process design with the purpose of improving fatigue strength and stress corrosion breaking strength of typical components, and other parts can be used for reference.

Description

Technical Field

[0001] The invention relates to a shot peening strengthening process, and in particular to a shot peening strengthening process design method for a typical component. Background Art

[0002] The integrated transmission is an irreplaceable part of the vehicle, and its performance directly affects the vehicle's maneuverability, reliability and other performance. Typical components such as gears, transmission shafts, friction plates, etc. are important components of the integrated transmission, and how to improve their fatigue performance is crucial.

[0003] Shot peening uses high-speed shot to hit the workpiece surface, causing elastic-plastic deformation of the workpiece surface. After the shot rebounds, the stress and strain on the workpiece surface recover unevenly, causing the residual stress and roughness of the workpiece surface to change. The shot peening process can change the fatigue strength of metal structural parts by introducing a residual compressive stress field on the surface. It is widely used in the automotive, aviation and other industries because it significantly improves the fatigue life of mechanical structural parts. At present, there is no shot peening specification for typical components in China. How to design shot peening parameters to improve the fatigue performance of typical components needs to be solved urgently. Summary of the invention

[0004] The invention provides a typical component shot peening strengthening process design method, which is used for the shot peening strengthening process for the purpose of increasing and improving the fatigue strength and stress corrosion cracking strength of the typical component.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is: A typical component shot peening process design method includes the following steps: Step 1: Determine the design technical requirements for shot peening parameters of typical components; Step 2: Select reasonable shot peening materials based on the technical requirements of typical components; Step 3: Establish a shot peening process parameter optimization model based on neural network; Step 4: Optimize the shot peening process parameters based on the surface strengthening requirements of the parts.

[0006] Furthermore, in step 1, typical components include gears, transmission shafts, friction plates, etc., and the technical requirements for shot peening parameter design include residual compressive stress and roughness requirements of typical components.

[0007] Furthermore, in step 2, the shot peening material refers to the shot material processed for shot peening of typical components, a single-shot shot peening simulation model with different materials as projectiles is established, and single-shot shot peening simulation calculation and analysis is carried out.

[0008] Furthermore, in the step 3, a shot peening process parameter optimization model is established, which includes shot size, shot peening speed, coverage and shot peening intensity as inputs, and shot peening residual stress field and roughness as outputs. The shot peening parameter optimization model is solved using a neural network model, and the shot peening process parameters are optimized and solved according to the residual stress field and surface roughness requirements of the parts.

[0009] Furthermore, in step 4, the optimization and solution of the shot peening process parameters include shot peening indirect parameter design and shot peening direct parameter design, wherein the indirect parameter design is performed first and then the direct parameter design, the shot peening indirect control parameters include shot peening speed, shot peening coverage and shot peening intensity, and the shot peening direct control parameters include shot peening angle, shot size, shot peening time and shot flow rate.

[0010] The beneficial effects of the present invention are: (1) By adopting the above shot peening process, the surface integrity of typical components can be controlled and the fatigue life of typical components can be improved.

[0011] (2) The proposed method can improve the fatigue life of typical components and can be extended to other typical rotating components made of other materials to enhance the surface integrity and improve the fatigue life. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a diagram of the working principle of the neuron of the present invention; Figure 2 It is a shot peening parameter design flow chart of the present invention; Figure 3 is a neural network modeling flow chart of the present invention; Figure 4 It is a comparison diagram of the original data and the training output data of the present invention. DETAILED DESCRIPTION

[0013] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the specific implementation methods of the present invention are described in detail below with reference to the accompanying drawings.

[0014] A typical component shot peening process design method involves the following steps: Step 1: The typical gear parts material is 20Cr2Ni4A, the typical transmission shaft parts are 300M steel, and the typical friction plate material is copper-based powder metallurgy. The residual compressive stress of typical components after shot peening should be greater than 300MPa. The non-peened areas should be well protected to avoid the impact of shot peening pellets on the non-peened areas, and the roughness of the shot peening areas should be strictly controlled.

[0015] Step 2: A single-shot shot peening simulation model with different materials as projectiles is established. In the simulation process, except for the density and modulus corresponding to the shot materials, as shown in Table 1, the other parameters are designed to be completely consistent, and the single-shot shot peening simulation calculation and analysis are carried out. The influence of shot peening materials on the residual stress and roughness of the shot peening target surface is analyzed, and reasonable shot peening materials are selected in combination with relevant standards.

[0016] Table 1 Basic material parameters

[0017]

[0018] Step 3: Using MATLAB's BP neural network toolbox, based on historical test data, establish a shot peening process parameter optimization model that includes shot size, shot peening speed, coverage and shot peening intensity as inputs and shot peening residual stress field and roughness as outputs. Figure 1 As shown, From neurons Input to the neuron signal, represents the corresponding connection weight, Representing neurons The output, = -1, = .

[0019] (1) In the formula Indicates the threshold. When the external stimulus reaches a certain threshold, the neuron will be activated and affect the next neuron. is the activation function. The derivative function of the bipolar Sigmoid function is continuous and is often used to approximate highly nonlinear problems. Its function form is: (2) The neural network model is used to solve the shot peening parameter optimization model. The indirect control parameters of shot peening include shot peening speed, shot peening coverage and shot peening intensity. The direct control parameters of shot peening include shot peening angle, shot size, shot peening time and shot flow rate. According to the residual stress field and surface roughness requirements of parts, the indirect parameter design is carried out first and then the direct parameter design. The shot peening strengthening parameter design process is as follows: Figure 2 shown.

[0020] Step 4: (1) Shot peening indirect parameter design based on neural network A multi-shot shot peening simulation model for the transmission shaft is established, and the influence of shot peening coverage is considered. The influence of shot peening speed, shot peening coverage, shot peening size, shot peening intensity, etc. on the surface after shot peening during multi-shot shot peening is analyzed. Historical shot peening strengthening data is collected, combined with experimental data, and the simulation data is fitted with historical data to obtain the shot peening residual stress distribution under different shot peening process parameters. For certain residual compressive stress requirements, the neural network-genetic algorithm is used to design the shot peening parameters, and the shot peening indirect parameters that meet the residual stress and roughness requirements are obtained by training the samples. The modeling process is as follows: Figure 3 As shown, the original data and the training output data are compared. Figure 4 shown.

[0021] (2) Direct parameter design of shot peening based on multi-parameter optimization Based on the determination method of shot peening speed, shot peening coverage and shot peening intensity, a multi-parameter optimization model based on shot peening angle, shot size, shot peening time and shot flow rate control is constructed. The requirements of part residual stress field and roughness on shot peening speed, shot peening coverage and shot peening intensity are taken as the optimization target. The shot peening angle, shot size, shot peening time, shot flow rate and other parameters are determined through optimization.

[0022] The gamultiobj function based on the NSGA-II algorithm is a multi-objective optimization genetic algorithm developed in MATLAB software. It uses fast non-inferior sorting and a new diversity retention strategy to greatly improve the calculation efficiency. At the same time, the crowding ratio operator is introduced to make the calculated Pareto frontier distribution more uniform. Combined with crack analysis, the maximum life is taken as the optimization goal, and the shot peening process parameters are taken as the optimization variables to compile a shot peening process parameter optimization program.

[0023] Optimized shot peening process parameters of typical components: 1) Shot peening of gears According to the research report on shot peening of surface integrity of typical components, the final process parameter optimization results of gear materials are shot peening particle size of 0.5mm, shot peening pressure of 0.5, and coverage of 100%.

[0024] 2) Shot peening of spline drive shaft According to the research report on shot peening of surface integrity of typical components, the final process parameter optimization results of the drive shaft material are shot peening particle size of 0.6mm, shot peening pressure of 0.55, and coverage of 100%.

[0025] 3) Shot peening of friction plate The friction plate matrix material is copper-based powder metallurgy material, the shot peening area is the outer circumferential gear teeth, the shot peening process parameters are set to be consistent with the gear shot peening parameters, the shot peening particle size is 0.5mm, the shot peening pressure is 0.5, and the coverage rate is 100%.

[0026] The parts not elaborated in detail in the present invention belong to the known technologies in the art. Any technical solutions formed by equivalent transformation or equivalent replacement shall fall within the protection scope of the present invention.

Claims

1. A typical component shot peening process design method, characterized in that: The following steps are involved: Step 1: Determine the design technical requirements for shot peening parameters of typical components; Step 2: Select reasonable shot peening materials based on the technical requirements of typical components; Step 3: Establish a shot peening process parameter optimization model based on neural network; Step 4: Optimize the shot peening process parameters based on the surface strengthening requirements of the parts.

2. A typical component shot peening process design method as claimed in claim 1, characterized in that: In step 1, typical components include 20Cr2Ni4A gears, 300M steel drive shafts and copper-based powder metallurgy friction plates, and the technical requirements for shot peening parameter design include residual compressive stress and roughness requirements of typical components.

3. A typical component shot peening process design method as claimed in claim 1, characterized in that: In step 3, the shot peening process parameter optimization model based on the neural network includes the shot peening process parameter optimization model with shot size, shot peening speed, coverage and shot peening intensity as inputs and shot peening residual stress field and roughness as outputs; the neural network is specifically: From neurons Input to the neuron signal, represents the corresponding connection weight, Representing neurons The output, = -1, = , （1） In the formula, Indicates the threshold. When the external stimulus reaches a certain threshold, the neuron will be activated and affect the next neuron. is the activation function, the derivative function of the bipolar Sigmoid function is continuous, and its function form is: （2）。 4. A typical component shot peening process design method as claimed in claim 1, characterized in that: In step 4, the shot peening process parameters are optimized and solved, including the shot peening indirect parameter design based on neural network and the shot peening direct parameter design based on multi-parameter optimization. The shot peening indirect control parameters include shot peening speed, shot peening coverage and shot peening intensity. The shot peening direct control parameters include shot peening angle, shot size, shot peening time and shot flow rate. The shot peening process parameters of typical components after optimization: the final process parameter optimization result of gear material is shot peening particle size of 0.5mm, shot peening pressure of 0.5, and coverage of 100% is the best; the final process parameter optimization result of transmission shaft material is shot peening particle size of 0.6mm, shot peening pressure of 0.55, and coverage of 100% is the best; the final process parameter optimization result of friction plate material is shot peening particle size of 0.5mm, shot peening pressure of 0.5, and coverage of 100%.

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