Surface quality and geometric accuracy control of fuel nozzle single-pass honing

Yang, Chuanfang; Gao, Shaowu; Fu, Yucan; Ding, Wenfeng; Xu, Jie

doi:10.1007/s00170-021-07103-5

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Lower surface roughness with increased linear speed was presented in the works of. 1,2,15,18,19 Supplementary Figure 7c, higher honing pressure causes an increased surface roughness. When the honing pressure rises from 3 MPa to 5 MPa, the surface roughness is relatively increased by 43.8%.…”

Section: Parametric Influencesmentioning

confidence: 95%

“…18 The authors stated that the R a was decreased by 25.0% with the aid of the optimizing approach, as compared to the original honing parameters. The impacts of the spindle speed ( S ), L , and retracting speed ( R ) on the R a , cutting force, bore diameter, and shape accuracy for the flexible single-pass honing having small tools were explored by Yang et al 19 The empirical models of the honing responses were proposed in terms of the optimizing inputs. The authors stated that the S of 5000 RPM, the L of 100 mm/min, and the R of 23.85 mm/min was employed to obtain the R a of 0.1 μm.…”

Section: Introductionmentioning

confidence: 99%

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An energy efficiency-based optimization of the rough honing process of hardened 5150 alloy

Van

Nguyen

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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The honing operation is maned as an effective and efficient finishing solution to achieve better surface properties and machining efficiency. However, quality and productivity indicators are primary considerations of published investigations. In this study, the rough honing operation of the hardened 5150 alloy has been addressed and optimized to decrease the energy efficiency ( EF), surface roughness ( SR), and material removal rate ( MRR). The optimizing factors include tangential speed ( T), linear speed ( L), honing pressure ( P), and honing depth ( D). The artificial neural network (ANN) method is utilized to present the non-linear relations between the process parameters and performance measures. The Taguchi method is applied to select the best architecture of ANN models. The Entropy and the vibration and communication particle swarm optimization (VCPSO) algorithm is applied to compute the weight factors of response and find optimal data. The total honing cost ( THC) model is proposed to compute the honing expenses. The results indicated that the optimal values of the T, L, P, and D are 32.0 m/min, 8.0 m/min, 3.3 MPa, and 0.01 mm, respectively, while the improvements in the EF, SR, MRR, and THC were 15.17%, 44.7%, 134.19%, and 25.0%, as compared to the initial values. The optimizing technique comprising the ANN, Entropy, and VCPSO could be named as an effective and efficient method to solve complicated optimization issues. The optimal data could help the machine operator to improve the honing performances with regard to saving trial costs and essential efforts.

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Section: Parametric Influencesmentioning

confidence: 95%

Section: Introductionmentioning

confidence: 99%

An energy efficiency-based optimization of the rough honing process of hardened 5150 alloy

Van

Nguyen

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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“…Muratov and Muratov et al [12] studied the honing technology of 95Cr18 and 12CrNi3N hydraulic components, analyzed the effect of honing track mesh density on surface roughness, and determined the control mechanism of mesh density. Yang and Su et al [13] applied the honing technology to the essential parts of the fuel nozzles of aero-engines, established the honing cutting force model, and obtained the optimal surface honing process.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Honing Surface Roughness of Carburized Holes Based on GRA-RSM

2021

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The carburized holes processed by ordinary internal grinding are prone to burn, crack, and low efficiency. Honing has a superior machining efficiency and cooling effect compared to traditional internal grinding. In this paper, we innovatively apply honing to carburizing hole grinding and propose an effective optimization scheme to enhance the surface finish of carburized holes. We set up an experimental system to explore the influence law of honing head rotation speed, axial reciprocating speed, grain size, and single grinding depth on surface roughness. Based on the grey correlation and response surface method, we propose a method to optimize the honing parameters of carburized holes and establish a prediction model, which has an R2 value of 0.9887, indicating that the model fits well. We verify the validity of the model by the root mean square error of 0.012 between the measured and calculated values. Based on the model, the optimal parameters of roughness (Ra) is obtained and verified by experiments. Compared with the original honing parameters, the surface roughness quality is improved by 25.8%. It shows that the optimized honing process based on the GRA-RSM method improves the surface quality of carburized holes significantly.

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