Effect of grain size and number of strokes on Rk parameters and emptiness coefficient in honing process

Barros, Guilherme Henrique Caetano; Schramm, Cristiano Rafael; Franco, Sinésio Domingues; Arantes, Luciano José; Arencibia, Rosenda Valdés

doi:10.1007/s00170-019-03810-2

Cited by 12 publications

(6 citation statements)

References 17 publications

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“…This behavior is similar to that of the (Rk) parameter. These results are in accordance with those of Caetano Barros et al [6], who found a great influence of grain size on (Rpk) parameter. On the other hand, Figure 10 depicts the main effects plot and the interaction effects plot for (Rvk).…”

Section: Discussion and Resultssupporting

confidence: 93%

“…As for the interactions, most significant ones are related to parameter GS, for example, GS-DE, GS-PR, GS-TV and GS-LV. Caetano Barros et al observed that (Rk) in honing processes is greatly dependent on grain size [6]. They used mesh sizes of 400 and 800, respectively.…”

Section: Discussion and Resultsmentioning

confidence: 99%

“…These authors found that Ra can be replaced effectively by Rk in finishing honing operations to control the final surface topography. Barros et al [6] studied the influence of grain size and of the number of strokes on the bearing area parameters in flexible honing. They observed that grain size influenced mainly parameters (Rk), (Rpk), (Mr2), and (Rp/Rt), while the number of strokes influenced (Rpk).…”

Section: Introductionmentioning

confidence: 99%

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Modeling of Surface Roughness in Honing Processes by Using Fuzzy Artificial Neural Networks

Buj-Corral

Sender

Luis-Pérez

2023

JMMP

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Honing processes are abrasive machining processes which are commonly employed to improve the surface of manufactured parts such as hydraulic or combustion engine cylinders. These processes can be employed to obtain a cross-hatched pattern on the internal surfaces of cylinders. In this present study, fuzzy artificial neural networks are employed for modeling surface roughness parameters obtained in finishing honing operations. As a general trend, main factors influencing roughness parameters are grain size and pressure. Mean spacing between profile peaks at the mean line parameter, on the contrary, depends mainly on tangential and linear velocity. Grain Size of 30 and pressure of 600 N/cm2 lead to the highest values of core roughness (Rk) and reduced valley depth (Rvk), which were 1.741 µm and 0.884 µm, respectively. On the other hand, the maximum peak-to-valley roughness parameter (Rz) so obtained was 4.44 µm, which is close to the maximum value of 4.47 µm. On the other hand, values of the grain size equal to 14 and density equal to 20, along with pressure 600 N/cm2 and both tangential and linear speed of 20 m/min and 40 m/min, respectively, lead to the minimum values of core roughness, reduced peak height (Rpk), reduced valley depth and maximum peak-to-valley height of the profile within a sampling length, which were, respectively, 0.141 µm, 0.065 µm, 0.142 µm, and 0.584 µm.

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Section: Discussion and Resultssupporting

confidence: 93%

Section: Discussion and Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling of Surface Roughness in Honing Processes by Using Fuzzy Artificial Neural Networks

Buj-Corral

Sender

Luis-Pérez

2023

JMMP

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“…The impacts of the G and NS on the roughness parameters for the flexible honing operation of the grey cast iron were analyzed with the aid of the full factorial design. 13 The results indicated that the G was named as the most effective parameter, followed by the NS . Moreover, the G of 800 mesh and the NS of 5 resulted in similar surfaces produced by the plateau honing operation.…”

Section: Introductionmentioning

confidence: 97%

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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“…Buj-Corral et al [8] investigated the effect of abrasive density on process parameters and concluded that the oilstone density was selected according to the acoustic emission of the grinding. The authors of [9][10][11] investigated the application of honing techniques in hardened steel, CuNiCr copper-nickel alloy, analyzed the effects of honing angle and abrasive size on roughness and residual stress, and finally obtained the optimized process parameters. 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.…”

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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Effect of grain size and number of strokes on Rk parameters and emptiness coefficient in honing process

Cited by 12 publications

References 17 publications

Modeling of Surface Roughness in Honing Processes by Using Fuzzy Artificial Neural Networks

Modeling of Surface Roughness in Honing Processes by Using Fuzzy Artificial Neural Networks

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

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

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