Prediction of residual stress fields after shot-peening of TRIP780 steel with second-order and artificial neural network models based on multi-impact finite element simulations

Daoud, Monzer; Kubler, Régis; Bemou, A.; Osmond, Pierre; Polette, Arnaud

doi:10.1016/j.jmapro.2021.10.034

Cited by 18 publications

(6 citation statements)

References 54 publications

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“…Although the shot peening conditions were different from the one used in this work, these results, show that the significant variations in the mechanical state are physical. Similar results have recently been obtained numerically (Daoud et al, 2021).…”

Section: Average and Standard Deviation For Computed Residual Stresse...supporting

confidence: 90%

Modeling of the shot peening of a nickel alloy with the consideration of both residual stresses and work hardening

Goulmy

Boyer

Retraint

et al. 2023

International Journal of Solids and Structures

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Section: Average and Standard Deviation For Computed Residual Stresse...supporting

confidence: 90%

Modeling of the shot peening of a nickel alloy with the consideration of both residual stresses and work hardening

Goulmy

Boyer

Retraint

et al. 2023

International Journal of Solids and Structures

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“…The impact radius r can be estimated by the equation [28]: The distance between the centers of adjacent impacts e was given as [28]:…”

Section: Shot Peening Simulation Modelmentioning

confidence: 99%

“…where D is the shot diameter, which is equal to 2 mm in our work; K is the impact efficiency ratio, which was characterized by the ratio between the elasto-plastic energy and the total kinetic energy, could be taken as 0.8 [28,29]; ρ s is the shot density which is equal to 7800 kg m −3 , V is the shot velocity; and E * is the reduced Young's modulus of the target and the shot. As the shot is assumed as a rigid body, E * can be calculated using the following equation:…”

Section: Shot Peening Simulation Modelmentioning

confidence: 99%

Simulations of the effect of shot peening backstress on nanoindentation

Chen,

Kanouté,

François

2024

Modelling Simul. Mater. Sci. Eng.

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Shot peening is a mechanical surface treatment that can introduce compressive residual stress and work hardening simultaneously. This work hardening, considered as a modification of the elastic region with plastic strain, can be modelled with two types of contributions: isotropic hardening and kinematic hardening. In order to characterize the mechanical properties of the treated surface using the instrumented indentation technique, the effect of the backstress associated with kinematic hardening should be studied, especially for works related to fatigue loading. In this paper, the distribution of three backstress components is obtained by shot peening simulations on a nickel-based alloy, Inconel 718, commonly used in the aerospace industry, and a series of indentation simulations are carried out using a spherical tip with different equivalent backstress levels. For Inconel 718, the third backstress component, which has the slowest evolution rate, is found to have the most significant influence on the response. However, compared to the effect of residual stress and cumulated plastic strain, the effect of backstress can be neglected.

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“…Scholars have delved into shot peening through neural networks, seeking to enhance shot peening efficiency. Daoud [14] presented a hybrid approach, employing two prediction models: the second-order model and the feed-forward artificial neural network model. Their study focused on understanding the effects of shot parameters, such as shot diameter, velocity, coverage, and impact angle, on induced residual stress distribution in TRIP780 steel.…”

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.

show abstract

Prediction of residual stress fields after shot-peening of TRIP780 steel with second-order and artificial neural network models based on multi-impact finite element simulations

Cited by 18 publications

References 54 publications

Modeling of the shot peening of a nickel alloy with the consideration of both residual stresses and work hardening

Modeling of the shot peening of a nickel alloy with the consideration of both residual stresses and work hardening

Simulations of the effect of shot peening backstress on nanoindentation

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

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