Artificial Neural Network Modeling of Grinding of Ductile Cast Iron using Water Based SiO2 Nanocoolant

Rahman, M. M.; Kadirgama, K.; Salwani, Ab Aziz Azma

doi:10.15282/ijame.9.2013.15.0137

Cited by 22 publications

(21 citation statements)

References 23 publications

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“…As the correlation coefficient approaches 1, the accuracy of the prediction advances. Thus, the correlation coefficient range is very close to 1, which consequently indicates excellent agreement between the experimental and the ANN predicted results [13].…”

Section: Resultssupporting

confidence: 64%

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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Section: Resultssupporting

confidence: 64%

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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“…Additionally, a study by Javadi et al [12] found that the types of nanoparticles used also contributed to the thermal conductivity enhancement whereby Al 2 O 3 and TiO 2 were found to have higher enhancement than SiO 2 . Also, thermal conductivity was affected by particle size and the stability of the nanofluid [13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Effect of Temperature on Heat Transfer Coefficient of Titanium Dioxide in Ethylene Glycol-Based Nanofluid

Hamid¹,

Azmi²,

Mamat³

et al. 2015

J. Mech. Eng. Sci.

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Nanofluid as a coolant has potential for use in the heat transfer field because of its augmentation in thermal properties that offers advantages in heat transfer. Research on various working temperatures is still ongoing in the nanofluid field. This study focused on the effect of temperature on heat transfer behavior using titanium dioxide or TiO 2 nanofluid as the working fluid in forced convection. The heat transfer coefficient was determined for flow in a circular tube under constant heat flux boundary conditions. The experiment was conducted with a Reynolds number less than 25000 with concentrations of TiO 2 nanofluid at 0.5%, 1.0% and 1.5%. At 30 o C, the maximum enhancement of 9.72% for 1.5% volume concentration was observed. Enhancements of 22.75% and 28.92% were found at 50 o C and 70 o C, respectively under similar nanofluid concentrations. The nanofluid performance was significantly influenced by working temperature. The heat transfer enhancement of TiO 2 nanofluid was considerably improved at higher working temperature and high concentration because of the improvement of thermal properties.

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“…The applicability of nanofluids as coolants is mainly because of their enhanced thermal conductivity due to solid particles inclusions [43][44][45][46] and the convection heat transfer coefficient of the fluid can be greatly improved by nanoparticles suspensions [47][48][49][50][51]. Nanofluids can be conveyed to the cutting zone in a machining process through nozzles like flood cooling systems, but the higher manufacturing costs of nanofluids and large wastage during machining application [52][53][54][55] have prompted researchers to explore the greater potential of nanofluids incorporated with the principles of MQL. A lot of research on the application of nanocutting fluids has been reported in the literature about the use of nanoparticles as additives to traditional oil-based lubricants and the improved machining performance in terms of reduced wear and decreased friction.…”

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

2022

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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Artificial Neural Network Modeling of Grinding of Ductile Cast Iron using Water Based SiO2 Nanocoolant

Cited by 22 publications

References 23 publications

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

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

Effect of Temperature on Heat Transfer Coefficient of Titanium Dioxide in Ethylene Glycol-Based Nanofluid

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

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