Effect of burnishing parameters on the surface quality and hardness

Çobanoğlu, Tamer; Ozturk, Sabri

doi:10.1177/0954405414527962

Cited by 24 publications

(19 citation statements)

References 30 publications

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“…To deduce the model which gives the high fitting precision between the fitted sinusoidal decay function and the measured data, the normalized values of residual stress and depth beneath surface are used. The transformation formula is expressed as shown in equation (6)…”

Section: Sinusoidal Decay Function Modelmentioning

confidence: 99%

“…By substituting in equation 5using any value of h normalized , the s normalized at this depth can be obtained. The normalized values of compressive residual stress and depth beneath surface have to be transferred to the actual values using equation (6).…”

Section: Verification Of the Modelsmentioning

confidence: 99%

“…Therefore, various surface modification by mechanical treatment such as shot peening (SP), laser shock peening, deep rolling, roller burnishing, and ultrasonic impact treatment and have been studied and applied for the improvement of fatigue property of high-strength alloy. [2][3][4][5][6][7] Among these techniques, SP is most commonly used due to its low cost and can be used on small or large areas depending on requirements.…”

Section: Introductionmentioning

confidence: 99%

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Empirical modeling of compressive residual stress profile in shot peening TC17 alloy using characteristic parameters and sinusoidal decay function

Tan

Yao

Zhang

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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This article introduces two comprehensive experimental models to predict the compressive residual stress profile induced in TC17 alloy after shot peening. Experiments are carried out utilizing one of experimental design techniques based on response surface methodology. Shot peening intensity and coverage are considered as two input parameters affecting compressive residual stress profile. The characteristic parameters model is created by regression analysis, which has the capability of predicting the four main characteristic parameters of a typical compressive residual stress profile. Based on this model, the absolute sensitivity of characteristic parameters with respect to shot peening intensity and coverage is analyzed. The sinusoidal decay function model is created with a proposition of that the compressive residual stress profile is a sinusoidal decay function of the depth beneath surface and the coefficients of this function are, in turn, functions of the two input shot peening parameters. The main advantage of sinusoidal decay function model over characteristic parameters model is that it provides the effect of shot peening parameters on the shape of the compressive residual stress profile. The two models have been checked for accuracy by two extra tests. The results show that the prediction errors of the four main characteristic parameters are within 20%, and the compressive residual stress profiles predicted by the sinusoidal decay function model are in consistent with experimental data.

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Section: Sinusoidal Decay Function Modelmentioning

confidence: 99%

Section: Verification Of the Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Empirical modeling of compressive residual stress profile in shot peening TC17 alloy using characteristic parameters and sinusoidal decay function

Tan

Yao

Zhang

et al. 2016

Proceedings of the Institution of Mechanical Engineers, Part B:

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“…In addition, machining factors of external burnishing processes have been considered for improving surface properties. 13–15…”

Section: Introductionmentioning

confidence: 99%

“…The common machining responses are the surface roughness, hardness, and residual stresses. 16 Different optimizing techniques, including response surface methodology (RSM), 11–13,15 Taguchi, 17,18 and fuzzy neural system, 19,20 were used to explore the relationships between inputs and outputs. However, the aforementioned publications for improving surface properties still have the following limitations and challenges:…”

Section: Introductionmentioning

confidence: 99%

Optimization of roller burnishing process using Kriging model to improve surface properties

Nguyen

2019

Proceedings of the Institution of Mechanical Engineers, Part B:

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The objective of this work is to investigate the influences of three machining factors (burnishing speed V, feed rate f, and depth of penetration a) on the improved rate of arithmetic average roughness Δ Ra, improved rate of maximum height roughness Δ Ry, and improved rates of surface hardness Δ SH. The internal roller burnishing experiments were conducted with the aid of the computer numerical control machining center and Box–Behnken experimental design. The Kriging models were used to render the highly nonlinear relationships between inputs and outputs. An integrative approach combining a Non-dominated Sorting Genetic Algorithm II and Technique for Order Preference by Similarity to Ideal Solution was adopted to generate a set of feasible optimal solutions and determine the best machining conditions. The scanning electron microscopy images were depicted to investigate the surface morphology at the different conditions. The X-ray diffraction was applied to measure the compressive stresses at the external surface. The results showed that the predicted values of the objectives have good agreement with the experimental ones. High surface quality is characterized by an improved average roughness of 95.80%, an enhancement in the maximum roughness of 91.98%, and an improvement in surface hardness of 45.44%, compared to pre-machined surfaces. The selection of optimum process parameters could help the burnishing operators to save the machining costs and time. The combination of Kriging model, Non-dominated Sorting Genetic Algorithm II, and Technique for Order Preference by Similarity to Ideal Solution is considered as an intelligent approach for modeling and optimization of burnishing processes.

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An artificial neural network approach to investigate surface roughness and vibration of workpiece in boring of AISI1040 steels

Rao¹,

Vidhu

Kumar

et al. 2015

Int J Adv Manuf Technol

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Effect of burnishing parameters on the surface quality and hardness

Cited by 24 publications

References 30 publications

Empirical modeling of compressive residual stress profile in shot peening TC17 alloy using characteristic parameters and sinusoidal decay function

Empirical modeling of compressive residual stress profile in shot peening TC17 alloy using characteristic parameters and sinusoidal decay function

Optimization of roller burnishing process using Kriging model to improve surface properties

An artificial neural network approach to investigate surface roughness and vibration of workpiece in boring of AISI1040 steels

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