Fatigue Life Prediction for SAE9254 Steel Treated by Shot Peening Based on Finite Element Simulation Combining with Neural Network

SHEN Jianguo; WANG Zhou; LU Wei; LUO Suhui; WANG Xiaoli; LUO Xiong; ZHENG Wenwen; WANG Fanxing; ZHANG Xu

doi:10.11973/jxgccl230168

Materials and Mechanical Engineering > 2024 > 48(7): 77-84. > DOI: 10.11973/jxgccl230168

SHEN Jianguo, WANG Zhou, LU Wei, LUO Suhui, WANG Xiaoli, LUO Xiong, ZHENG Wenwen, WANG Fanxing, ZHANG Xu. Fatigue Life Prediction for SAE9254 Steel Treated by Shot Peening Based on Finite Element Simulation Combining with Neural Network[J]. Materials and Mechanical Engineering, 2024, 48(7): 77-84. DOI: 10.11973/jxgccl230168

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Fatigue Life Prediction for SAE9254 Steel Treated by Shot Peening Based on Finite Element Simulation Combining with Neural Network

SHEN Jianguo^{1, 2},
WANG Zhou^{1, 2, 3},
LU Wei⁴,
LUO Suhui⁴,
WANG Xiaoli^{1, 2, 3},
LUO Xiong⁴,
ZHENG Wenwen⁴,
WANG Fanxing^{1, 2},
ZHANG Xu^{1, 2}

1.
School of Automotive Engineering Wuhan 430070
2.
Hubei Key Laboratory of Advanced Technology for Automotive Components Wuhan 430070
3.
Hubei Collaborative Innovation Center for Automotive Components Technology, Wuhan University of Technology,Wuhan 430070, China
4.
Guangzhou Huade Automobile Spring Co., Ltd., Guangzhou 511339, China

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Received Date: April 10, 2023
Revised Date: April 08, 2024

Graphical Abstract

Abstract

Abstract

A stochastic multiple shot peening model based on Python scripts was established by ABAQUS finite element software. The residual stress distribution and surface roughness of SAE9254 steel for suspension springs after shot peening under different shot diameters, different shot velocities, and different shot peening coverage rates were predicted and compared with test results. Based on the finite element simulation results and neural network model, the fatigue life of the test steel was predicted, and experimental verification was carried out. The results show that the simulated curves of residual stress along the depth direction of SAE9254 steel were in good agreement with the test results, the relative error of the maximum residual compressive stress was about 14.77%, and the relative error of surface roughness was about 3.18%, which indicated that the established stochastic multiple shot peening model could accurately predict the residual stress distribution and surface roughness of SAE9254 steel after shot peening. The average relative error between the fatigue life prediction values obtained by the method combining finite element simulation and neural network and the experimental values was 6.85%, indicating that this method could accurately predict the fatigue life of SAE9254 steel.
- SAE9254 steel,
- shot peening,
- surface roughness,
- finite element simulation,
- neural network,
- fatigue life

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References (28)

References

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Fatigue Life Prediction for SAE9254 Steel Treated by Shot Peening Based on Finite Element Simulation Combining with Neural Network

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