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A study on wet and near-dry electrical discharge machining process of Inconel 718.

INTRODUCTION

Electrical discharge machining (EDM) is a process and tool used for machining hard materials. Nowdays the EDM process was widely in the industry because of its ability to machine any electrically conductive material irrespective of its mechanical strength. EDM removes the work material by the process of melting and vaporizing through a series of discharging electric sparks.

Conventional EDM process uses liquid dielectric fluid for removing the work material. In particular, hydrocarbon oil the dielectric fluid is one of the main sources of pollution in die sinking electrical discharge machining. Wastes of dielectric oil are very toxic, cannot be recycled and should be disposed immediately to avoid the land and water being polluted [1]. The EDM process produces gases and fumes by the process of thermal decomposition of the dielectric material and the process consumes more energy. The energy consumed in the spark gap is less than 20% of the total input of electrical energy and highly effective for the erosion of the material. On other hand, the energy consumed by the dielectric system is 50% of the total input of electrical energy and used for low values of peak current [8].

Dry EDM is another technique, which replaces the liquid as gas as a dielectric medium for removing work material. Due to the reattachment of debris to the machined surface, dry EDM process having drawbacks of meeting the requirement of combined material removal rate (MRR) and surface roughness applications. The accuracy of surface profile deteriorates with the debris deposition. The disadvantages of dry EDM process are low stability of arc column, low material removal rate, arcing, poor surface quality compared to conventional EDM and odor of burning. Now-days the efforts have been made in the experimental investigation and optimization of parameters are carried out in order to overcome its drawbacks [11-14]. The disadvantages of dry EDM process can be reduced & overcome by replacing the gas with the mixture of gas and dielectric liquid. The liquid content in the mist media will solidify and flush away the molten debris and the debris reattachment is reduced in near-dry EDM. It does not need a bath of dielectric fluid and only a small amount of liquid dielectric fluid is used making the process environment-friendly. To meet desired performance targets, the dry EDM process has the benefit to tailor the concentration of liquid and properties of dielectric medium. It is found that near-dry EDM has the advantage of finishing operation with low discharge energy and higher MRR compared to wet EDM and better surface finish quality than dry EDM.

Experimental Setup:

In this work, INCONEL 718 was used as work material for the experimentation. The experiment setup was planned according to response surface methodology (17 runs box bhenken method). Experiments were conducted using z-axis DC servo high speed jump EDM5530 machine. Table 2.1 shows the specification of EDM machine.

In this study, the 10 mm diameter copper tool is selected as a tool electrode for conducting the experimentation and 15 x 15mm square work piece is used for each experiment. In this experiment, the depth of cut is 1mm respectively. The chemical composition of the selected work piece is shown in table 2.2

2.1 Design of Experiment:

The objective of DoE is the selection of the points where the response should be evaluated. To select the parameters and its levels for experimentations, several exploratory experiments were conduct to determine important control factors. Out of several available controllable input parameters on the EDM machine, following parameters were selected with maximum feasible range, as shown in table 2.3

In this research, the input parameters pulse on time, current, lifting time and remaining parameters are consider as constant on responses like Material Removal Rate, Tool Wear Rate and Surface Roughness were studied for both wet and near dry electrical discharge machining process respectively.

2.2 Response Surface Methodology:

In this work, response surface methodology (RSM) is used to study the relationships between explanatory variables and one or more response variables. RSM is a collection of mathematical and statistical technique useful for modelling and analysis the problems. In this study the response of interest is influenced by several variables and the objective is to optimize the response.

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

Where
Y : response
X, [PHI], [PSI]                     :quantitative variables.
[[beta].sub.1], [[beta].sub.2],     :linear effect of X, [PHI], and
  [[beta].sub.3]                       [PSI] respectively.
[[beta].sub.11], [[beta].sub.22],   :quadratic effect of X, [PHI]
  [[beta].sub.33]                     and [PSI].
[[beta].sub.12], [[beta].sub.13],   :linear by li-near interaction.
  [[beta].sub.23]


These quadratic models work quite well over the entire factor space and the regression coefficients were computed according to backward elimination method. The results obtained using Design matrix of experimentation for Material Removal Rate, Tool Wear Rate and Surface Roughness values on both wet and near dry conditions were shown in table 2.4.

RESULT AND DISCUSSION

The total 19 experiment were conducted and to get the result for adding two or more number of trials together and divide the total number of trials from that to get an average values of MRR ([mm.sup.3]/min), TWR ([mm.sup.3]/min), Ra (ps) are presented in the table 2.4. For analysis of data, checking the lack of fit values of model is necessary.

Using design expert software the analysis of design will be conducted. In than k=1 indicate there is no transformation take place and the maximum ratio of matrix is 6.453 this indicate there is no need for system transformation. If the ratio greater than 10 usually indicate a transformation is required. At the same time the ratio less than 3 indicate the power transformations have little effect.

6.1 Analysis of MRR in Wet Condition:

The fit summary put forward that the quadratic model is statistically significant for analysis of MRR. The reduced quadratic model of ANOVA is shown in table 6.1. F value is help full to give the rank for significant factor. After selecting the quadratic model with the help of backward elimination, it is found that the model is significant. Lack of fit is a measure of the failure of the model to represent data in the experimental domain. From table 6.1 it is seen that lack of fit values is 14.76 that shows that lack of fit value is not significant. The value of R-squared for the model is calculated as 0.9816 that is very close to one. This is an indication of better general ability and accuracy of MRR in the quadratic model.

The predicted R- squared value and adjusted R- squared has an close tolerance with values of 0.9597 and 0.9732 respectively as the difference between these are less than 0.2. The adequate precision measures the signal to noise and is equal to 38.577. A ratio greater than 4 is desirable for fix the model. This values are shows the model is more significant.

The final empirical relation of MRR equation in terms of actual factors is obtained as follows

MRR= -9.95000E-003 - 5.10833E-004 * Current - 2.46042E-003 * Pulse on time + 3.66000E-004 * Current * Pulse on time - 3.83333E-005 * Current * Lifting time -6.55417E-005 * Pulse on time2

MRR can be predicted from the equation with multiple variable models. From this equation concluded that the main effect of factors A, B and C with combined factors of AB and AC gives significant effect on MRR.

At large discharge current will cause more powerful spark in between tool and work piece resulting faster MRR, and hence MRR will be increased. From this analysis, the experimental values match the predicted value is reasonably good

The interaction effect of combined factor of Ip and Ton shows that MRR will be maximum at higher values of Ip (15A) and Ton (30gs). The 3D interaction plot shows the MRR increases with decreases due to changes in factors values. Considering this is the reason that higher the peak current larger will be the discharge energy and more will be the material removal rate.

6.2 Analysis of TWR on Wet Condition:

The fit summary put forward that the quadratic model is statistically significant for analysis of TWR. The reduced quadratic model of ANOVA is shown in table 6.3. F value is help full to give the rank for significant factor. After selecting the quadratic model with the help of backward elimination, it is found that the model is significant. Lack of fit is a measure of the failure of the model to represent data in the experimental domain. From table 6.3 it is seen that lack of fit values is 16.88 that shows that lack of fit value is not significant. The values of R-squared for the model are calculated as 0.9792 that is very close to one. This is an indication of better general ability and accuracy of TWR in the quadratic model.

The predicted R- squared value and adjusted R- squared has an close tolerance with values of 0.9667 and 0.9142 respectively as the difference between these are less than 0.2. The adequate precision measure the signal to noise and is equal to 31.077. a ratio greater than 4 is desirable for fix the model. This values are shows the model is more significant.

TWR can be predicted from the equation with multiple variable model. From this equation concluded that the main effect of factors A and B with combined factors of AB and AC gives significant effect on TWR.

At large discharge current will cause more powerful spark in between tool and work piece resulting high TWR. Optimum lifting time gives the low wear rate to decrease the TWR for increasing lifting time. In ANOVA, the model reduction may improve the model accuracy.

The 3D surface interaction plot shows the result of TWR according to changes happen in discharge current may cause the TWR. at high discharge current with high pulse on time gives the high wear rate to reduce the wear by means of reducing the discharge current. For increasing lifting time gradually to increase the tool indentation this may reduce the tool life.

6.3 Analysis of Surface Roughness in Wet Condition:

The fit summary put forward that the quadratic model is statistically significant for analysis of Ra. The reduced quadratic model of ANOVA is shown in table 6.5. F value is help full to give the rank for significant factor. After selecting the quadratic model with the help of backward elimination, it is found that the model is significant. Lack of fit is a measure of the failure of the model to represent data in the experimental domain. From table 6.5 it is seen that lack of fit values is 24.35 that shows that lack of fit value is not significant. The values of R-squared for the model are calculated as 0.9749 that is very close to one. This is an indication of better general ability and accuracy of Ra in the quadratic model.

The predicted R- squared value and adjusted R- squared has an close tolerance with values of 0.9298 and 0.9665 respectively as the difference between these are less than 0.2. The adequate precision measures the signal to noise and is equal to 37.827. A ratio greater than 4 is desirable for fix the model. This values are shows the model is more significant.

Ra can be predicted from the equation with multiple variable model. From this equation concluded that the main effect of factors A, B and C with combined factors of AB and AC gives significant effect on surface roughness.

At large discharge current will cause more powerful spark in between tool and work piece resulting higher surface roughness values, and hence surface roughness will be decreased. At low discharge current it will give the good surface finish that is for attain low surface roughness value 4.02. From this analysis, the experimental values match the predicted value is reasonably good.

The 3D surface interaction plot shows the result of Surface Roughness according to changes happen in discharge current. It may cause the Ra at high discharge current with high pulse on time gives the high wear rate of both tool and work piece and to reduce the wear by means of reducing the discharge current. For increasing lifting time gradually to reduce the material removal rate, this may reduce the surface roughness values.

6.4 Analysis of MRR in near dry Condition:

The fit summary put forward that the quadratic model is statistically significant for analysis of MRR. The reduced quadratic model of ANOVA is shown in table 6.7. F value is help full to give the rank for significant factor. After selecting the quadratic model with the help of backward elimination, it is found that the model is significant. Lack of fit is a measure of the failure of the model to represent data in the experimental domain. From table 3.7 it is seen that lack of fit values is 23.50 that shows that lack of fit value is not significant. The value of R-squared for the model is calculated as 0.9790 that is very close to one. This is an indication of better general ability and accuracy of MRR in the quadratic model.

The predicted R- squared value and adjusted R- squared has an close tolerance with values of 0.9255 and 0.9664 respectively as the difference between these are less than 0.2. The adequate precision measures the signal to noise and is equal to 31.815. A ratio greater than 4 is desirable for fix the model. This values are shows the model is more significant.

MRR can be predicted from the equation with multiple variable models. From this equation concluded that the main effect of factors A, B and C with combined factors of AB and AC gives significant effect on MRR.

At large discharge current will cause more powerful spark in between tool and work piece resulting faster MRR, and hence MRR will be increased. From this analysis, the experimental values match the predicted value is reasonably good.

The interaction effect of combined factor of Ip and Ton shows that MRR will be maximum at higher values of Ip (15A) and Ton (30ps). The 3D interaction plot shows the MRR increases with decreases due to changes in factors values. Considering this is the reason that higher the peak current larger will be the discharge energy and more will be the material removal rate.

6.5 Analysis of TWR on near dry Condition:

The fit summary put forward that the quadratic model is statistically significant for analysis of TWR. The reduced quadratic model of ANOVA is shown in table 6.9. F value is help full to give the rank for significant factor. After selecting the quadratic model with the help of backward elimination, it is found that the model is significant. Lack of fit is a measure of the failure of the model to represent data in the experimental domain. From table 6.9 it is seen that lack of fit values is 29.27 that shows that lack of fit value is not significant. The values of R-squared for the model are calculated as 0.9792 that is very close to one. This is an indication of better general ability and accuracy of TWR in the quadratic model.

The predicted R- squared value and adjusted R- squared has an close tolerance with values of 0.9667 and 0.9142 respectively as the difference between these are less than 0.2. The adequate precision measures the signal to noise and is equal to 31.077. A ratio greater than 4 is desirable for fix the model. This values are shows the model is more significant.

TWR can be predicted from the equation with multiple variable models.

From this equation concluded that the main effect of factors A and B with combined factors of AB and AC gives significant effect on TWR.

At large discharge current will cause more powerful spark in between tool and work piece resulting high TWR. Optimum lifting time gives the low wear rate to decrease the TWR for increasing lifting time. In ANOVA, the model reduction may improve the model accuracy

The 3D surface interaction plot shows the result of TWR according to changes happen in discharge current may cause the TWR. at high discharge current with high pulse on time gives the high wear rate to reduce the wear by means of reducing the discharge current. For increasing lifting time gradually to increase the tool indentation this may reduce the tool life.

6.6 Analysis of Surface Roughness in near dry Condition:

The fit summary put forward that the quadratic model is statistically significant for analysis of Ra. The reduced quadratic model of ANOVA is shown in table 6.11. F value is help full to give the rank for significant factor. After selecting the quadratic model with the help of backward elimination, it is found that the model is significant. Lack of fit is a measure of the failure of the model to represent data in the experimental domain. From table 6.11, it is seen that lack of fit values is 13.69 that shows that lack of fit value is not significant. The values of R-squared for the model are calculated as 0.9786 that is very close to one. This is an indication of better general ability and accuracy of Ra in the quadratic model.

The predicted R- squared value and adjusted R- squared has an close tolerance with values of 0.9200 and 0.9688 respectively as the difference between these are less than 0.2. The adequate precision measures the signal to noise and is equal to 35.124. A ratio greater than 4 is desirable for fix the model. This values are shows the model is more significant.

Ra can be predicted from the equation with multiple variable model. From this equation concluded that the main effect of factors A, B and C with combined factors of AB and AC gives significant effect on surface roughness.

At large discharge current will cause more powerful spark in between tool and work piece resulting higher surface roughness values, and hence surface roughness will be decreased. At low discharge current it will give the good surface finish that is for attain low surface roughness value 3.97. From this analysis, the experimental values match the predicted value is reasonably good

The 3D surface interaction plot shows the result of Surface Roughness according to changes happen in discharge current. It may cause the Ra at high discharge current with high pulse on time gives the high wear rate of both tool and work piece and to reduce the wear by means of reducing the discharge current. For increasing lifting time gradually to reduce the material removal rate, this may reduce the surface roughness values.

Conclusion:

In this project, influence of process parameters on metal removal rate, tool wear rate and surface roughness is investigated. The parameters and their combinations affecting the process were obtained using ANOVA, interaction effect of combined factors and lack of fit test. From the table 7.15, it is concluded that The predicted values match the experimental values reasonably well. In this table shows the model accuracy and quality of being true of the experiment.

The response surface methodology shows that the better performance obtained for MRR in discharge current of 15 Amps, pulse on time of 30 [micro]s and optimum value of lifting current of 4s for both wet and near dry condition. At the same time discharge current of 5 Amps, pulse on time of 10 [micro]s and optimum value of lifting current 4s gives the minimum value of TWR and Ra.

At near dry condition low discharge current, pulse on time and optimum lifting time gives the better result that is higher MRR and lower Ra value compare to wet condition. In this, for machining the complicated parts using near dry EDM processes is more preferable.

During experimentation, it was observed that near-dry EDM does not produce health hazards.

REFERCENCES

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[3.] Fujiki, Masahiro, Jun Ni and J. Albert, A. Shih, 2009. "International Journal of Machine Tools & Manufacture Investigation of the Effects of Electrode Orientation and Fluid Flow Rate in near-Dry EDM Milling." International Journal of Machine Tools and Manufacture, 49(10): 749-58.

[4.] Gostimirovic, Marin, Pavel Kovac, Milenko Sekulic, and Branko Skoric, 2012. "Influence of Discharge Energy on Machining Characteristics in EDM f." 26(1): 173-79.

[5.] Habib, Sameh S., 2009. "Study of the Parameters in Electrical Discharge Machining through Response Surface Methodology Approach." Applied Mathematical Modelling 33(12): 4397-4407, 2009. Retrieved (http://dx.doi.org/10.1016/j.apm.2009.03.021).

[6.] Hasgalik, Ahmet., 2008. "Modeling and Analysis of Electrode Wear and White Layer Thickness in Die-Sinking EDM Process through Response Surface Methodology." pp: 1148-56.

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[9.] Li, Anhai, Jun Zhao, Guangming Zheng and Zhiqiang Pei., 2011. "Machined Surface Roughness and Chip Morphology in High Speed Side Milling of Inconel 718." pp: 442: 45.

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[11.] Muthuramalingam, T., and B. Mohan, 2014. "Science Direct A Review on in Fl Uence of Electrical Process Parameters in EDM Process." Archives of Civil and Mechanical Engineering, 1-8. Retrieved (http://dx.doi.org/10.1016/j.acme.2014.02.009).

[12.] Padhee, Soumyakant and Niharranjan Nayak, 2012. "Multi-Objective Parametric Optimization of Powder Mixed Electro-Discharge Machining Using Response Surface Methodology and Non-Dominated Sorting Genetic Algorithm." 37: 223-40.

[13.] Rajesh, R., and M. Dev Anand, 2012. "The Optimization of the Electro-Discharge Machining Process Using Response Surface Methodology and Genetic Algorithms." Procedia Engineering, 38: 941-50. Retrieved (http://dx.doi.org/10.1016/j.proeng.2012.06.451).

[14.] Roth, R., F. Kuster and K. Wegener, 2013. "Influence of Oxidizing Gas on the Stability of Dry Electrical Discharge Machining Process." Procedia--Social and Behavioral Sciences, 6: 338-43. Retrieved (http://dx.doi.org/10.1016/j.procir.2013.03.029).

[15.] Saha, Sourabh K., and S.K. Choudhury, 2009. "Multi--Objective Optimization of the Dry Electric Discharge Machining Process.

[16.] Selc, Yusuf Keskin H., 2006. "An Experimental Study for Determination of the Effects of Machining Parameters on Surface Roughness in Electrical Discharge Machining (EDM)." pp: 1118: 21.

[17.] Sharma, Neeraj, Rajesh Khanna and Rahuldev Gupta, 2013. "Multi Quality Characteristics Of WEDM Process Parameters With RSM." Procedia Engineering, 64: 710-19. Retrieved (http://dx.doi.org/10.1016/j.proeng.2013.09.146).

[18.] Shih, Albert J., 2008. "Experimental Study of the Dry and Near-Dry Electrical Discharge." 130: 1-9.

[19.] Trinh, Thuy Khanh, and Lim Seok Kang, 2010. "Application of Response Surface Method as an Experimental Design to Optimize Coagulation Tests." 15(2): 63-70.

[20.] Zhuang, Kejia, Dahu Zhu, Xiaoming Zhang and Han Ding, 2014. "Notch Wear Prediction Model in Turning of Inconel 718 with Ceramic Tools Considering the in Fl Uence of Work Hardened Layer." Wear 313(1-2): 63-74.

(1)K. P. Senthilkumar, (2) P. Janagarathinam, (3) A. Bala Kumaran, (4) S. P. Rajesh

(1) Assistant Professor, Department of Mechanical Engineering, Excel College of Engineering and Technology, pallakapalayam-637303,

(2) Assistant Professor, Department of Mechanical Engineering, SNS College of Technology, Coimbatore.

(3) Assistant Professor, Department of Mechanical Engineering, Excel College of Engineering and Technology, pallakapalayam-637303,

(4) Assistant Professor, Department of Mechanical Engineering, Excel College of Engineering and Technology, pallakapalayam-637303,

Received 28 February 2017; Accepted 22 March 2017; Available online 25 April 2017

Address For Correspondence: Senthilkumar.k.p, Assistant Professor, Department of Mechanical Engineering, Excel College of Engineering and Technology, pallakapalayam-637303,

E-mail: kpsenthil92@gmail.com.

Caption: Fig. 3.1: Residual and interaction effect on MRR

Caption: Fig. 3.2: Residual and interaction effect on TWR

Caption: Fig. 3.3: Residual and interaction effect on Ra

Caption: Fig. 3.4: Residual and interaction effect on MRR

Caption: Fig. 3.5: Residual and interaction effect on TWR

Caption: Fig. 3.6: Residual and interaction effect on Ra
Table 2.1: Specification of EDM machine

Machine name               Z-axis DC servo with high
                             speed jump EDM 5530
Table size                 550 x 300
Dielectric fluid used      EDM oil
Dielectric tank capacity   250 lit
Machine weight             1000 kg

Table 2.2: Work material composition

Element          Result in %   Element          Result in %

Carbon (C)       0.076         Molybdenum(Mo)   3.91
Silicon (Si)     0.22          Niobium (Nb)     5.32
Sulphur (S)      0.006         Tantalum(Ta)     0.62
Phosphorous(P)   0.16          Nickel (Ni)      51.60
Manganese (Mn)   0.11          Titanium(Ti)     0.94
Chromium (Cr)    17.77         Iron(Fe)         18.10

Table 2.3: Parameters of EDM machine with maximum range

S. No   Machining Parameter   Unit       Levels

                                         -1   0    1

A       Current               A          5    10   15
B       Pulse On Time         [micro]s   10   20   30
C       Lifting Time          S          3    4    5

Table 2.4: Experimental Design Matrix and Result in Near Dry
Machining Condition.

STD   RUN   current   Pulse on time   Lifting time

            A         [micro]s        s

1     6     15        20              3
2     8     15        20              5
3     9     10        10              3
4     5     5         20              3
5     17    10        20              4
6     10    10        30              3
7     11    10        10              5
8     13    10        20              4
9     7     5         20              5
10    12    10        30              5
11    4     15        30              4
12    15    10        20              4
13    16    10        20              4
14    2     15        10              4
15    3     5         30              4
16    14    10        20              4
17    1     5         10              4

      WET CONDITION

STD   MRR              TWR              Ra

      [mm.sup.3]/min   [mm.sup.3]/min   [micro]m

1     0.0981           0.0063           4.79
2     0.1284           0.0090           5.55
3     0.0458           0.0020           4.23
4     0.0601           0.0021           4.63
5     0.0826           0.0033           4.77
6     0.1217           0.0069           5.27
7     0.0369           0.0021           4.38
8     0.0782           0.0028           4.71
9     0.0363           0.0019           4.19
10    0.1005           0.0063           5.18
11    0.1665           0.0114           5.79
12    0.0849           0.0035           4.81
13    0.0882           0.0037           4.89
14    0.0597           0.0022           4.54
15    0.0557           0.0021           4.49
16    0.0872           0.0036           4.79
17    0.0221           0.0005           4.02

      NEAR DRY CONDITION

STD   MRR              TWR              Ra

      [mm.sup.3]/min   [mm.sup.3]/min   [micro]m

1     0.0973           0.0070           4.87
2     0.1192           0.0096           5.71
3     0.0446           0.0028           4.44
4     0.0603           0.0029           4.68
5     0.0846           0.0040           4.92
6     0.1223           0.0077           5.41
7     0.0380           0.0028           4.54
8     0.0779           0.0035           4.88
9     0.0365           0.0027           4.23
10    0.0997           0.0071           5.32
11    0.1584           0.0122           5.95
12    0.0867           0.0042           4.97
13    0.0905           0.0046           5.03
14    0.0561           0.0029           4.72
15    0.0568           0.0027           4.66
16    0.0892           0.0045           4.93
17    0.0233           0.0009           3.97

Table 3.1: Performance Measure of Reduced Quadratic Model of MRR.

Std. Dev.        6.091E-003
Mean             0.080
C.V. %           7.65
PRESS            8.923E-004
R-Squared        0.9816
Adj R-Squared    0.9732
Pred R-Squared   0.9597
Adeq Precision   38.577

Table 3.2: Performance Measure of Reduced Quadratic Model of TWR.

Std. Dev.        5.366E-004
Mean             4.829E-003
C.V. %           11.11
PRESS            1.187E-005
R-Squared        0.9792
Adj R-Squared    0.9667
Pred R-Squared   0.9142
Adeq Precision   31.077

Table 3.3: Performance Measure of Reduced Quadratic Model of Ra.

S                0.087
Mean             4.77
C.V. %           1.82
PRESS            0.25
R-Squared        0.9749
Adj R-Squared    0.9665
Pred R-Squared   0.9298
Adeq Precision   37.827

The final empirical relation of Ra equation in terms of actual
factors is obtained as follows

Ra= +3.8214 - 0.038056 * Current + 5.5000E-003 * Pulse on time +
3.90000E-003 * Current * Pulse on time + 0.010889 * Current * Lifting
time.

Table 3.4: Performance Measure of Reduced Quadratic Model of MRR.

Std. Dev.        6.482E-003
Mean             0.079
C.V. %           8.21
PRESS            1.492E-003
R-Squared        0.9790
Adj R-Squared    0.9664
Pred R-Squared   0.9255
Adeq Precision   31.815

The final empirical relation of MRR equation in terms of actual
factors is obtained as follows

MRR = +0.095625 - 9.66750E-003 * Current + 3.02667E-003 * Pulse on
time - 0.026738 * Current * Pulse on time - 2.28500E-003 * Current *
Lifting time - 7.56667E-005 * pulse on [time.sup.2].

Table 3.5: Performance Measure of Reduced Quadratic Model of TWR.

Std. Dev.        5.366E-004
Mean             4.829E-003
C.V. %           11.11
PRESS            1.187E-005
R-Squared        0.9792
Adj R-Squared    0.9667
Pred R-Squared   0.9142
Adeq Precision   31.077

The final empirical relation of TWR equation in terms of actual
factors is obtained as follows

TWR = + 4.74848E-003 - 9.71015E-004 * Current - 1.21250E-004 * Pulse
on time + 3.75000E-005 * Current * pulse on time + 8.81131E - 005 *
Current * Lifting time + 2.15531E-005 * Current [conjunction]
2 - 7.39091E -005 * Lifting time [conjunction] 2 . `

Table 3.6: Performance Measure of Reduced Quadratic Model of MRR.

Std. Dev.        0.088
Mean             4.90
C.V. %           1.81
PRESS            0.32
R-Squared        0.9786
Adj R-Squared    0.9688
Pred R-Squared   0.9200
Adeq Precision   35.124

The final empirical relation of Ra equation in terms of actual
factors is obtained as follows

Ra = +3.27667 + 0.063528 * Current + 0.018875 * Pulse on time +
2.70000E-003 * Current * Pulse on time + 0.011611 * Current * Lifting
time - 3.56111E-003 * [Current.sup.2]

Table 7.15: comparison of actual and predicted values for both
conditions

s.no   Ip(A)   Ton          Lt(s)   MRR ([mm.sup.3]/
               ([micro]s)           min)

WET CONDITION                       Actual   Predicted

1      15      30           4       0.1665   0.164
2      5       10           4       0.0221   0.025

NEAR DRY CONDITION                  Actual   Predicted

1      15      30           4       0.1584   0.159
2      5       10           4       0.0233   0.026

s.no                 TWR                  Ra([micro]s)
                     ([mm.sup.3]/min)

WET CONDITION        Actual   Predicted   Actual   Predicted

1                    0.0114   0.011       5.79     5.82
2                    0.0005   0.00067     4.02     4.10

NEAR DRY CONDITION   Actual   Predicted   Actual   Predicted

1                    0.0122   0.012       5.95     5.91
2                    0.0009   0.001       3.97     4.01
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Author:Senthilkumar, K.P.; Janagarathinam, P.; Kumaran, A. Bala; Rajesh, S.P.
Publication:Advances in Natural and Applied Sciences
Article Type:Report
Geographic Code:1USA
Date:Apr 1, 2017
Words:5209
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