Application of Nanofluids in Minimum Quantity Lubrication Grinding

Shen, Bin; Shih, Albert J.; Tung, Simon C.

doi:10.1115/ijtc2007-44160

Cited by 39 publications

(55 citation statements)

References 3 publications

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“…Shen [30] experimented and concluded that the application of nanofluid in MQL grinding was evaluated for the first time. It is concluded that nanofluid are not able to provide superior cooling capacity in MQL grinding process.…”

Section: Novel Machining Techniquesmentioning

confidence: 99%

A Comprehensive Review on Machining of Ti6al4v

2017

IJAERD

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Section: Novel Machining Techniquesmentioning

confidence: 99%

A Comprehensive Review on Machining of Ti6al4v

2017

IJAERD

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“…They verified the benefits of nanofluids in terms of reducing grinding forces, improving surface roughness, and preventing burning of the workpiece. In contrast to dry grinding, MQL grinding could substantially lower the grinding temperature [1].…”

Section: Introductionmentioning

confidence: 99%

“…Grinding is a precision machining process which is widely used in the manufacture of components requiring fine tolerances and smooth finishes [1]. Therefore, the negative effect of high temperature on these parameters should be prevented [2].…”

Section: Introductionmentioning

confidence: 99%

Neural Network Modeling of Grinding Parameters of Ductile Cast Iron Using Minimum Quantity Lubrication

Sahid

Rahman²,

Kadirgama³

2015

Int. J. Automot. Mech. Eng.

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This paper presents the optimization of the grinding parameters of ductile cast iron in wet conditions and with the minimum quantity lubrication (MQL) technique. The objective of this project is to investigate the performance of ductile cast iron during the grinding process using the MQL technique and to develop artificial neural network modeling. In this project we used the DOE method to perform the experiments. Analysis of variance with the artificial neural network method is used to investigate significant effects on the performance characteristics and the optimal cutting parameters of the grinding process. Ductile cast iron was used in this experiment and the ethanol glycol was applied in the conventional method and compared with the MQL method. During conventional grinding, a dense and hard slurry layer was formed on the wheel surface and the performance of the ductile cast iron was very low, threatening the ecology and health of the workers. In order to combat the negative effects of conventional cutting fluids, the MQL method was used in the process to formulate modern cutting fluids endowed with user-and eco-friendly properties. Aluminum oxide was used as the grinding wheel (PSA-60JBV). This model has been validated by the experimental results of ductile cast iron grinding. Each method uses two passessingle-pass and multiple-pass. The prediction model shows that depth of cut and table speed have the greatest effect on the surface roughness and material removal rate for the MQL technique with multiple-passes by showing improved surface roughness, preventing workpiece burning and enabling a more friendly environment. Thus, various other parameters need to be added for further experiments, such as the wheel speed, distance from the wheel to the workpiece zone contact, and the geometry of the nozzle.

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“…However, research on the application of nanoparticles as a water-based cooling / lubricating medium is very rare [56]. The application of water-based Al2O3 and diamond nanofluids in MQL grinding shows promising improvements in surface roughness, a reduction in the grinding force, and an improved G-ratio with high concentrations of nanofluids as compared to pure water application [57,58]. Research was carried out to investigate the wheel wear and the tribological characteristics in wet, dry and MQL grinding of cast iron.…”

Section: Introductionmentioning

confidence: 99%

Parametric optimization of end milling process under minimum quantity lubrication with nanofluid as cutting medium using pareto optimality approach

Najiha¹,

Rahman²,

Kadirgama³

2016

Int. J. Automot. Mech. Eng.

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In this paper a genetic algorithm based multi-objective optimization approach is applied in order to predict the optimal machining parameters for the end milling process of aluminium alloy 6061 T6 combined with minimum quantity lubrication (MQL) conditions using waterbased TiO2 nanofluid as cutting fluid. The optimization is carried out employing a parametric model (in terms of input cutting parameters, i.e., cutting speed, feed rate, depth of cut, MQL flow rate and % volume concentration of nanofluid) and exploiting the capabilities of the MOGA-II algorithm applied to the constrained machining problem. The objective functions selected to optimize are: to minimize the surface roughness; to maximize the material removal rate; and to minimize the flank wear of the cutting tool. The output of the optimization includes several alternative optimal solutions, i.e., Pareto frontier, and the best compromised configuration of the cutting parameters is selected subject to weighted preference.

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Application of Nanofluids in Minimum Quantity Lubrication Grinding

Cited by 39 publications

References 3 publications

A Comprehensive Review on Machining of Ti6al4v

A Comprehensive Review on Machining of Ti6al4v

Neural Network Modeling of Grinding Parameters of Ductile Cast Iron Using Minimum Quantity Lubrication

Parametric optimization of end milling process under minimum quantity lubrication with nanofluid as cutting medium using pareto optimality approach

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