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Prediction method of shot peening process parameters based on genetic algorithm optimization of bp neural network

A BP Neural Network, Shot Peening Technology

Active Publication Date: 2022-08-02
NORTHWESTERN POLYTECHNICAL UNIV +1
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Problems solved by technology

[0007] In order to overcome the shortcomings of poor practicability of the existing shot peening forming method, the present invention provides a method for predicting shot peening process parameters based on genetic algorithm optimization BP neural network

Method used

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  • Prediction method of shot peening process parameters based on genetic algorithm optimization of bp neural network
  • Prediction method of shot peening process parameters based on genetic algorithm optimization of bp neural network
  • Prediction method of shot peening process parameters based on genetic algorithm optimization of bp neural network

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Embodiment Construction

[0060] refer to Figure 1-6 . The specific steps of the method for predicting the parameters of the shot peening process based on the genetic algorithm optimization of the BP neural network are as follows:

[0061] Step 1: Select the main factors affecting shot peening for testing, including the thickness of the part, the aspect ratio, the yield strength of the material, the elastic modulus, the Poisson's ratio and the moving speed of the nozzle, so as to obtain the corresponding radius of curvature of the part.

[0062] The relationship between the radius of curvature of the part and the main factors affecting shot peening can be expressed as:

[0063] R=f(h,r,E,σ s ,ν,V)

[0064] where R is the radius of curvature, h is the thickness of the target part, r is the aspect ratio, E is the elastic modulus of the material, σ s is the yield strength, ν is the Poisson's ratio, and V is the nozzle moving speed.

[0065] The data sample set is determined according to the test res...

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Abstract

The invention discloses a method for predicting process parameters of shot peening forming based on genetic algorithm optimization of BP neural network, which is used to solve the technical problem of poor practicability of the existing shot peening forming method. The technical solution is to first use the BP neural network to establish a complex nonlinear mapping relationship between the shape characteristics of the parts, the mechanical properties of the material and the parameters of the shot peening process, and then use the genetic algorithm to optimize the structure and parameters of the BP neural network, which can be used for shot peening. Aided design of process parameters. Because the BP neural network is used to construct the complex nonlinear mapping relationship between the part shape features, material mechanical properties and shot peening process parameters, the shot peening process parameter prediction model can be established without fully understanding the internal mechanism of shot peening. , and use the genetic algorithm to optimize the structure and parameters of the BP neural network, which reduces the prediction time, improves the prediction accuracy, effectively improves the efficiency of shot peening process parameter design, and has good practicability.

Description

technical field [0001] The invention relates to a shot peening forming method, in particular to a shot peening forming process parameter prediction method based on genetic algorithm optimization of BP neural network. Background technique [0002] Shot peening is one of the main forming methods of aircraft integral panels, and it is a process method developed on the basis of shot peening. In addition to its ability to form thin-walled structural parts, shot peening can improve the surface quality of parts and improve the fatigue resistance of parts. Shot peening is a dieless forming process. In industrial production, it is mainly achieved by controlling different process parameters such as projectile specifications, spray distance, spray angle, spray pressure, projectile flow rate, machine speed, etc. forming. In addition, the machine tool, sprayed material, workpiece state, etc. will also affect the forming effect and quality to a certain extent. Therefore, the shot peeni...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F30/17G06F30/15G06F30/27G06N3/12G06N3/04
CPCG06N3/126G06F2113/22G06F30/15G06F30/17G06N3/044
Inventor 王桐王俊彪张贤杰刘闯高国强李京平
Owner NORTHWESTERN POLYTECHNICAL UNIV