2019
DOI: 10.1016/j.ijfatigue.2019.105231
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Combining the finite element method and response surface methodology for optimization of shot peening parameters

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Cited by 39 publications
(17 citation statements)
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“…In order to describe the peening coverage ratio of ordered dimple pattern and stochastic dimple pattern, two different coverage description methods were carried out. Order dimple pattern was realized by the method proposed by Wang et al [39], stochastic dimple pattern was realized via a Python program from our group previous work [40]. Figure 2a,b show 100% peening coverage ratio of order dimple pattern in 4-shots and 16-shots SP models, respectively.…”
Section: Coverage Calculation and Residual Stress Evaluation Methodsmentioning
confidence: 99%
“…In order to describe the peening coverage ratio of ordered dimple pattern and stochastic dimple pattern, two different coverage description methods were carried out. Order dimple pattern was realized by the method proposed by Wang et al [39], stochastic dimple pattern was realized via a Python program from our group previous work [40]. Figure 2a,b show 100% peening coverage ratio of order dimple pattern in 4-shots and 16-shots SP models, respectively.…”
Section: Coverage Calculation and Residual Stress Evaluation Methodsmentioning
confidence: 99%
“…The notable remark from this study is that a significant increase in RCS in sub-surface is observed with higher peening coverage than higher shot velocity. Wang et al [ 56 ] combined FEM with RSM to optimize the shot parameters for the effective peening 42CrMo specimen, primarily used in river-sea going ships. The optimized shot peening parameters include the shot velocity of 88 m/s, the shot diameter of 0.8 mm, and the coverage ratio of 170%.…”
Section: Peening Techniquesmentioning
confidence: 99%
“…Nam et al [24] used the response surface methodology for peening process optimization of aircraft structure part under the effects of nozzle distance, pressure, impact angle, and exposure time on the Almen intensity and coverage using quadratic approximations. Wang et al [25] employed the response surface methodology to optimize the response variables (maximum compressive residual stress, the depth of residual stress field, and concentration factor) with dependent variable of shot velocity, shot diameter and coverage ratio based on the obtained FEM results using quadratic polynomial function.…”
Section: Introductionmentioning
confidence: 99%