Prediction of residual stress, surface roughness, and grain refinement of 42CrMo steel subjected to shot peening by combining finite element method and artificial neural network

Huang, Haiquan; Wang, Senhui; Wang, Cheng; Li, Kun; Zhou, Yijun; Wang, Xiaogui

doi:10.1007/s00170-023-11716-3

Cited by 6 publications

(1 citation statement)

References 58 publications

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“…The results demonstrated consistent predictions from both models concerning the process parameters, rendering them suitable for shot peening parameter optimization due to their responsiveness. Similarly, Huang [15] put forth a numerical prediction framework, integrating the Finite Element Method (FEM) and Artificial Neural Network (ANN) algorithms. Their investigation revealed that the prediction results of the GA-BP-ANN algorithm (Genetic Algorithm Optimized Backpropagation Artificial Neural Network algorithm) closely aligned with FEM simulation results in terms of SP-induced residual stresses, equivalent plastic strains, grain refinement, and surface roughness.…”

Section: Introductionmentioning

confidence: 99%

Optimization of parameters for the best shot peening effect based on surface response and neural network model

Wang,

Kim

2024

Mater. Res. Express

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To optimize the peening effect of different shot peening process parameters on metal surfaces, the mapping relationship between different shot peening process parameters and metal surface integrity was obtained. In this paper, ABAQUS software was used to establish a DE-FE (Discrete element-Finite element) random multi-shot analysis model to simulate shot peening, then optimize the shot peening process parameters based on the surface response method(RSM), and finally validate it through experiments and BP(back propagation) neural network model. The result shows that when the shot velocity is 70 m/s, the impact angle of shot is 61.45°, and the shot diameter is 0.78 mm, the shot peening effect is the best, the surface roughness value is reduced by 101.84%, and the arc height value is increased by 54.66%; the error between the predicted results of BP neural network and the results of numerical analysis is less than 8%. Therefore, the optimized process parameters significantly improve the shot peening effect, but also shows that the BP neural network prediction model can more accurately predict the mapping relationship between the input parameters of shot velocity, shot diameter, and impact angle of shot and the output parameters of roughness value and arc height value.

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Section: Introductionmentioning

confidence: 99%