Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (4): 1280-1287.doi: 10.13229/j.cnki.jdxbgxb20171272

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Simulation and prediction of surface roughness of 6061 aluminum alloy workpiece after shot peening

Wu⁃jiao XU(),Cheng⁃shang LIU,Xin⁃yao LU   

  1. College of Material Science and Engineering, Chongqing University, Chongqing 400044, China
  • Received:2017-12-25 Online:2019-07-01 Published:2019-07-16

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Abstract:

The purpose of this study is to analyze the effect of shot peening on the surface finish of the workpiece, in which the AA6061 workpiece is taken as example. Firstly, a FEM model of shot peening is established, based on which the surface morphology of single shot peening and multiple shot peening are simulated. Corresponding experiments of single shot peening and multiple shot peening are carried out and the practical surface morphology of the workpiece after the shot peening is observed by the Laser Scanning Confocal Microscope, by which the validity of the established FEM model of shot peening is verified. Then, 9 groups of orthogonal simulations are executed to study the influence of the initial roughness of target material, shot size, shot speed and shot peening coverage on the final surface roughness of the workpiece described by the 3D roughness parameter S q. The prediction model of the surface roughness of the workpiece after the shot peening is established by means of the BP neural network model. Based on this prediction model the predicted surface roughness and the simulated surface roughness are compared and the mean error is 3.46%. This prediction accuracy can meet the requirement of the shot peening production. Furthermore, the controlling map of AA6061 workpiece after shot peening is constructed, which is important to control the final surface finish in the practical shot peening processing. The methodology presented in this paper can be extended to the application of other metallic material besides aluminum alloy, which would be helpful for the simulation, prediction and controlling the surface roughness of the workpiece after the shot peening processing.

Key words: metal material, shot peening, 6061 aluminum alloy, surface roughness, neural network model, prediction model

CLC Number: 

  • TG146.2

Fig.1

"

Fig.2

Surface profile of single shot peeinng"

Fig.3

Surface profile of multiple shot peeing"

Table 1

Level of orthogonal experimental factors"

水平 A B C D
1 0 0.2 50 100
2 3.2 0.6 75 200
3 6.4 1.0 100 300

Fig.1

"

Table 3

L9(34) orthogonal design and results"

试验编号 A B C D E
1 0 0.2 50 100 4.8402
2 0 0.6 75 200 11.7275
3 0 1.0 100 300 19.6275
4 3.2 0.2 75 300 3.7858
5 3.2 0.6 100 100 31.8285
6 3.2 1.0 50 200 13.7609
7 6.4 0.2 100 200 6.9918
8 6.4 0.6 50 300 7.7602
9 6.4 1.0 75 100 38.2005

Fig.5

BP neural network structure"

Fig.6

Training error curve of BP neural network model"

Fig.7

Scatter diagram of BP neural network model"

Fig.8

Surface roughness controlling map of 6061 aluminium alloy workpiece after shot peening process"

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