Constrained probabilistic multi-objective optimization of shot peening process

Bhuvaraghan, Baskaran; Srinivasan, Sivakumar M.; Maffeo, Bob; Prakash, Om

doi:10.1080/0305215x.2010.508523

Cited by 12 publications

(7 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It has proven to be highly efficient for better understanding of the peening mechanisms [21]. Consequently, a significant number of research works focused on the use of FEM to analyse the distribution of residual stresses under the effect of shot size [22] [23] [24] [25], shot velocity [22] [23] [25], coverage [26] [27] [28],…”

Section: Introductionmentioning

confidence: 99%

“…impact angle [24] [25] [29] [30], and material behavior of the target component [25] [31]. Based on these works, it was shown that the increase in the shot size and the shot velocity results in an increase in the depth of the maximum compressive residual stress as well as the affected depth while the surface and the maximum sub-surface compressive residual stresses are almost not sensitive to these two shot-peening parameters but are mainly related to the mechanical characteristics of the target component material.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Kubler

Bemou

et al. 2021

Journal of Manufacturing Processes

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Kubler

Bemou

et al. 2021

Journal of Manufacturing Processes

View full text Add to dashboard Cite

“…The results showed that RS increased with the increase of the shot diameter d p . Bhuvaraghan et al [22] investigated the effects of v 0 and the mass flow rate r m on the compressive RS by DEM-FEM coupling method. The results showed that v 0 has a greater effect on the compressive RS than r m , and a large v 0 can induce a deep compressive RS field.…”

Section: Introductionmentioning

confidence: 99%

DEM-FEM coupling analysis of shot′s energy distribution and target′s residual stress of pneumatic shot peening

Wu,

Shao,

et al. 2023

Mater. Res. Express

View full text Add to dashboard Cite

Pneumatic shot peening is a widely used surface strengthening method. During the peening process, shots often collide with each other, resulting in large energy loss and small compressive residual stress. In order to achieve the optimum compressive residual stress with as little energy loss as possible, firstly the collision mechanism of shots and the forming and coupling mechanism of the target’s residual stress are revealed, and then pneumatic shot peening is simulated by using DEM-FEM coupling model. Then, the effects of impact angle θ, initial shot velocity v 0, shot diameter d p, and mass flow rate r m on the percentage η of shots with different ratios of the impact velocity to initial shot velocity v m/v 0, the energy loss (EL), the energy transferred from shots to the target (ET), the residual energy (ER) and the compressive residual stress (RS) are investigated. The results show that as many random shots successively impact the target, the RS field induced by each shot couples with some adjacent RS fields induced by other shots, so that disperse RS fields are gradually transformed into a continuous RS layer with the compressive RS in the surface and the tensile RS in the subsurface. With the increase of θ and r m and with the decrease of v 0 and d p, the collision probability of shots increases, so EL also increases and η of shots with a large v m/v 0 decreases. While, ET increases with the increase of v 0 and d p, decreases with the increase of r m, and first increases and then decreases with the increase of θ. ET does not entirely determine but greatly affects the compressive RS field. So, the surface compressive RS and the maximum compressive RS first increase and then decrease with the increase of θ and r m, while the two parameters increase with the increase of v 0 and d p. The optimum parameters of shots are θ = 75°, v 0 = 60 m s−1, d p = 0.25 mm and r m = 2 kg min−1, in which ET reaches 45%, the surface compressive RS of S11 and S33 reach 512 MPa and 510 MPa respectively, and the maximum compressive RS of S11 and S33 reach 665 MPa and 746 MPa respectively.

show abstract

“…For the three effects of shot peening on the target material, residual stresses on the top layer is beneficial to improve the fatigue resistance of the material, hence it should be maximised. While, the material hardening may lead to stress relaxation of compressive residual stress on one hand [15]; and on the other hand surface roughness, more exactly the height of the cavities caused by impacts has a potential to weaken the benefits of compressive residual stresses and cause corrosive cracks [18]. Further, the material hardening could be calculate by the measurement of the material plastic strain at the yield point of the material and the cold work could be calculate by the measurement of the plastic strain of the peened surface [19].…”

Section: Formulation Of Optimisationmentioning

confidence: 99%

“…Miao, et al [14] explored the relationship between parameters and residual stresses and surface roughness, and independently considered the surface integrity as the optimisation objective to optimise a shot peening process. Later, using the design and analysis of computer experiments method, Maximisation of residual compressive stress, minimisation of surface roughness and cold work were set as objectives of optimisation function by Bhuvaraghan, et al [15]. They firstly considered friction between the shot and its corresponding surface and resulted in determination of the optimum area under compressive residual stress curve while keeping the cold work and roughness below the specification limits.…”

Section: Introductionmentioning

confidence: 99%

Numerical Optimisation of Shot Peening Process on a Steam Turbine Blade

Chen¹,

Desai²,

Heyns³

et al. 2017

Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)

View full text Add to dashboard Cite

Abstract. This article provides a broad and extensive literature survey on optimisation of shot peening process that have been developed and applied in the past decades and summarises the knowledge that has been gained and then points out the shortages of the important investigations in optimisation of shot peening process. On this basis, optimisation function is formulated and genetic algorithm is chosen to conduct the optimisation. This optimisation screens out the influential parameters and provides the weights of different parameters in terms of their influence of shot peening outputs.

show abstract

Constrained probabilistic multi-objective optimization of shot peening process

Cited by 12 publications

References 6 publications

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

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

DEM-FEM coupling analysis of shot′s energy distribution and target′s residual stress of pneumatic shot peening

Numerical Optimisation of Shot Peening Process on a Steam Turbine Blade

Contact Info

Product

Resources

About