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Prediction method of shot peening process parameters based on genetic algorithm optimized BP neural network

A BP neural network and shot peening technology, applied in biological neural network models, genetic laws, neural architectures, etc., can solve problems such as poor practicability, and achieve the effects of good practicability, improved prediction accuracy, and improved efficiency

Active Publication Date: 2019-03-22
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 optimized BP neural network
  • Prediction method of shot peening process parameters based on genetic algorithm optimized BP neural network
  • Prediction method of shot peening process parameters based on genetic algorithm optimized BP neural network

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Embodiment Construction

[0060] refer to Figure 1-6 . The present invention is based on the genetic algorithm optimization BP neural network shot peening process parameter prediction method concrete steps are as follows:

[0061] Step 1. Select the main factors that affect shot peening for testing, including the thickness of the part, the aspect ratio, the yield strength of the material, the elastic modulus, 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] In the formula, 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 Poisson's ratio, and V is the nozzle moving speed.

[0065] Determine the data sample set according to the test results, and...

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Abstract

The invention discloses a Prediction method of shot peening process parameters based on genetic algorithm optimized BP neural network, which is used for solving the technical problem of poor practicability of the existing shot peening forming method. The technical scheme is firstly to establish the complex nonlinear mapping relationship between the shape characteristics of parts, mechanical properties of materials and shot peening process parameters by using BP neural network, and then to optimize the structure and parameters of BP neural network by using genetic algorithm, which can be used for the auxiliary design of shot peening process parameters. The BP neural network is adopted to construct the outline features of the part, the complex nonlinear mapping relationship between the mechanical properties of materials and the process parameters of shot peening, The prediction model of shot peening process parameters can be established without fully understanding the internal mechanismof shot peening, and the structure and parameters of BP neural network are optimized by using genetic algorithm, which reduces the prediction time, improves the prediction accuracy, effectively improves the efficiency of the design of shot peening process parameters, and has good practicability.

Description

technical field [0001] The invention relates to a shot peening forming method, in particular to a method for predicting shot peening process parameters based on genetic algorithm optimization of BP neural network. Background technique [0002] Shot peening is one of the main forming methods for the overall panel of the aircraft, and it is a process developed on the basis of shot peening. In addition to the ability to form thin-walled structural parts, shot peening can also improve the surface quality of parts and improve the fatigue resistance of parts. Shot peening is a moldless forming process. In industrial production, the overall wallboard with different thickness and curvature is realized by controlling different process parameters such as shot size, spray distance, spray angle, spray pressure, shot flow rate, and machine speed. of forming. In addition, the machine tool, the material to be sprayed, the state of the workpiece, etc. will also affect the effect and quali...

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Application Information

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IPC IPC(8): G06F17/50G06N3/12G06N3/04
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
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