Mathematical modeling and machining parameter optimization for the surface roughness of face gear grinding

Xin, Ming; Gao, Qin; Yan, Hongjie; Liu, Jinhua; Liao, Cui Jiao

doi:10.1007/s00170-016-9576-2

Cited by 30 publications

(11 citation statements)

References 15 publications

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“…Ming et al [94] developed a theoretical model of tooth surface roughness based on the grinding track. Tooth surface roughness was considered as a target variable and the machining parameters were optimized by the orthogonal experimental method.…”

Section: Taguchi Methodsmentioning

confidence: 99%

New insights into the methods for predicting ground surface roughness in the age of digitalisation

Pan

Zhou

Yan

et al. 2021

Precision Engineering

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Section: Taguchi Methodsmentioning

confidence: 99%

New insights into the methods for predicting ground surface roughness in the age of digitalisation

Pan

Zhou

Yan

et al. 2021

Precision Engineering

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“…The effects of minimum quantity lubrication on MRR, surface integrity and temperature while grinding Ti-6Al-4V workpiece material were also studied. Considering the stochastic nature of a grinding process and based on orthogonal experiment method, Ming et al [33] optimized different input parameters for a five-axis blade grinding setup. Liu et al [34] integrated signal-to-noise analysis with GRA technique to optimize different grinding parameters for attaining the most desired values of MRR and grinding efficiency.…”

Section: Literature Reviewmentioning

confidence: 99%

Parametric Analysis of a Grinding Process Using the Rough Sets Theory

Agarwal

Dandge²,

Chakraborty

2020

FU Mech Eng

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With continuous automation of the manufacturing industries and the development of advanced data acquisition systems, a huge volume of manufacturing-related data is now available which can be effectively mined to extract valuable knowledge and unfold the hidden patterns. In this paper, a data mining tool, in the form of the rough sets theory, is applied to a grinding process to investigate the effects of its various input parameters on the responses. Rotational speed of the grinding wheel, depth of cut and type of the cutting fluid are grinding parameters, and average surface roughness, amplitude of vibration and grinding ratio are the responses. The best parametric settings of the grinding parameters are also derived to control the quality characteristics of the ground components. The developed decision rules are quite easy to understand and can truly predict the response values at varying combinations of the considered grinding parameters.

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“…S. Khalilpourazary et al [2] studied the surface roughness of the processed spur gears and found that the arithmetic surface roughness value of the spur gears manufactured with a lubricant containing alumina nanoparticles during the hobbing process was decreased. Xingzu Ming et al [3] established a mathematical formula of the residual height to improve the surface quality of the face gear and modi ed the grinding surface roughness model of the face gear. Sunil Pathak et al [4] used the surface nish, microstructure and microhardness to evaluate the surface quality of bevel gears, and they found that honing gears can improve the surface quality of gears and reduce the transmission noise of the machine.…”

Section: Introductionmentioning

confidence: 99%

Experimental Research on the Surface Quality of Milling Contour Bevel Gears

Zhang

et al. 2021

Preprint

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Contour bevel gears have the advantages of high coincidence, low noise and large bearing capacity, which are widely used in automobile manufacturing, shipbuilding and construction machinery. However, the quality of the tooth surface has a significant impact on the transmission accuracy of the gear, so it is of great significance to optimize the surface quality of the contour bevel gear. This paper firstly analyzes the formation process of machined surface roughness of contour bevel gears on the basis of generating machining method, and dry milling experiments of contour bevel gears are conducted to analyze the effects of cutting speed and feed rate on the machined surface roughness and surface topography of the workpiece. Then, the surface defects on the machined surface of the workpiece are studied by SEM, and the causes of the surface defects are analyzed by EDS. After that, XRD is used to compare the microscopic grains of the machined surface and the substrate material for diffraction peak analysis, and the effect of cutting parameters on the microhardness of the workpiece machined surface is investigated by work hardening experiment. The research results are of great significant for improving the machining accuracy of contour bevel gears, reducing friction losses and improving transmission efficiency.

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Mathematical modeling and machining parameter optimization for the surface roughness of face gear grinding

Cited by 30 publications

References 15 publications

New insights into the methods for predicting ground surface roughness in the age of digitalisation

New insights into the methods for predicting ground surface roughness in the age of digitalisation

Parametric Analysis of a Grinding Process Using the Rough Sets Theory

Experimental Research on the Surface Quality of Milling Contour Bevel Gears

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