Mathematical Model and Optimization of Surface Roughness During Electrical Discharge Machining of Ti–5Al–2.5Sn with Graphite Electrode

Khan, Md. Ashikur Rahman; Rahman, Md. Mustafizur; Kadirgama, K.

doi:10.1166/asl.2012.4211

Cited by 6 publications

(7 citation statements)

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“…The goodness of the model can be confirmed by the coefficients of determination R 2 which are close to 1, which are very high and indicate a high correlation between the experimental and predicted values. Figure 3(b) shows the result of single pass and multi pass conventional and water based silicon oxide nanocoolant grinding (Khan et al, 2012a). The predicted values are found to be in good agreement with the experimental readings.…”

Section: Mathematical Modelingsupporting

confidence: 62%

“…Statistical experimental designs (response surface designs (RSM)) are most widely used in optimization experiments (Box & Draper, 1987;Khan et al, 2012a;Rahman, Khan, Kadirgama, Noor, & Bakar, 2010). The central composite design (CCD) is the most popular of the many classes of RSM designs due to the following three properties (Rahman, Khan, Kadirgama, Noor, & Bakar, 2011b …”

Section: Design Of Experimentsmentioning

confidence: 99%

“…The experimental data was utilized to build the mathematical model for a first-and-second order model using the regression method. (Kalidass, Palanisamy, & Muthukumaran, 2012;Khan, Rahman, & Kadirgama, 2012a)stated that when the response can be defined by a linear function of independent variables, then the approximating function is a first-order model. An artificial neural network is a system based on the operation of biological neural networks; in other words, it emulates a biological neural system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Rahman¹,

Kadirgama²,

Salwani³

2014

Int J Automot Mech Eng

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This paper presents optimization of the grinding progress of ductile cast iron using water-based SiO 2 nanocoolant. Conventional and water-based nanocoolant grinding was performed using a precision surface grinding machine. The study is aimed to investigate the effect of table speed and depth of cut on the surface roughness and material removal rate (MRR). Mathematical modeling is developed using the response surface method. An artificial neural network model is developed for predicting the surface roughness and MRR. Multi-layer perception and a batch back propagation algorithm are used. MLP is a gradient descent technique to minimize the error through a particular training pattern in which it adjusts the weight by a small amount at a time. From the experiment, the depth of cut is directly proportional to the surface roughness, but the table speed is inversely proportional to the surface roughness. The higher the value of the depth of cut, the lower the value of MRR, and vice versa for the table speed. It is concluded that the surface quality together with the material removal rate are the most affected by the depth of cut(s) and table speed.

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Section: Mathematical Modelingsupporting

confidence: 62%

Section: Design Of Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Rahman¹,

Kadirgama²,

Salwani³

2014

Int J Automot Mech Eng

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“…The electrical discharge machining has various process parameters that affect the performance parameters surface roughness and recasts layer thickness and material removal rate [73,[104][105][106]. The quality of component is solely affected by the process parameters.…”

Section: Influence Of Process Parametersmentioning

confidence: 99%

A review on machining of metal matrix composites using nanoparticle mixed dielectric in electro-discharge machining

Mohanty¹,

Routara²

2016

Int. J. Automot. Mech. Eng.

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The growth in the new advanced materials that are harder till date have led to the improvement of technologies used to machine them. Nowadays, new materials have been developed to be used in different sectors where lighter and harder materials are in vast requirement. Moreover, materials machined with high precision machine tools have to provide the greatest surface finish such that it catches the pace with other competitive materials. Research has shown that the electro-discharge machining (EDM) is the only machine tool that has created history in machining hard materials. In the recent years, metal matrix composites (MMCs) have an increasing demand in the industries since they are hard materials to machine and a good finish is required in the aerospace, marine, aircraft, nuclear and other similar industries. Thus, EDM has gained fame in machining MMCs. With due course, the powder mixed electro-discharge machining (PMEDM) has created efficient EDM, with the use of powders in the dielectric. The dielectric mixed EDM has been used for efficient material removal rate (MRR). With the change in technologies, the sizes of powders that have to be added in the dielectric fluid go on decreasing. In this review, studies by numerous researchers on the MMCs that are machined with the use of PMEDM are mentioned. The powders used in the experimentation are of nano size. Furthermore, the optimization technique used by the researchers is discussed in the present study.

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“…If a higher surface finish is required, it is essential that before the process starts the setting of cutting parameters is done properly [20,21]. The mechanical properties of the workpieces that have to be machined, the rotational speed of the cutter, velocity of traverse and feed rate are all factors that yield the final surface, but the machining process is responsible for the development of surface roughness [22].…”

Section: Regression Analysismentioning

confidence: 99%

Machining Performance of Aluminum Alloy 6061-T6 on Surface Finish Using Minimum Quantity Lubrication

Najiha

Rahman²,

Kadirgama³

2015

Int. J. Automot. Mech. Eng.

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This paper presents an experimental investigation of coated carbide cutting tool performance on the surface roughness of aluminum alloy 6061-T6 machining through end mill processes using the minimum quantity lubrication technique. Process parameters including the cutting speed, depth of cut and feed rate are selected. The central composite design method is used for design of experiments. Two types of coated carbide tool are used in this experiment -an uncoated tungsten carbide insert and TiAlN+TiN-coated carbide insert. The analysis of variance method is utilized to validate the experimental data and to check for adequacy. The response surface method was used to develop the mathematical models and to optimize the machining parameters. Second-order regression models are developed based on the surface roughness results. It is observed that the surface roughness depends significantly on depth of cut and feed rate, followed by spindle speed for both the coated carbide inserts. The performance of the dual-layered coating of TiAlN+TiN is competent as compared to the surface quality obtained with TIAlN-coated inserts. The results can be used as an example of MQL applied to the machining of aluminum alloys, providing economic advantages in terms of reduced lubricant costs and better machinability.

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Mathematical Model and Optimization of Surface Roughness During Electrical Discharge Machining of Ti–5Al–2.5Sn with Graphite Electrode

Cited by 6 publications

References 0 publications

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

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

A review on machining of metal matrix composites using nanoparticle mixed dielectric in electro-discharge machining

Machining Performance of Aluminum Alloy 6061-T6 on Surface Finish Using Minimum Quantity Lubrication

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