EFFECT OF VIBRATION ON MICRO-ELECTRO-DISCHARGE MACHINING

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),

ISSN 0976 – 6359(Online), Volume 5, Issue 7, July (2014), pp. 80-100 © IAEME

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EFFECT OF VIBRATION ON MICRO-ELECTRO-DISCHARGE

MACHINING

Amol S. Todkar1, Dr. M.S. Sohani

2, Prashant R. Patil

3, P. N. Deshmukh

4

1, 3, 4(Department of Mechanical Engineering, TKIET, Warananagar, Kolhapur, India)

2(Professor, Department of Mechanical Engineering, AITM, Belgaum, India)

ABSTRACT

The Principal objective of the research work is decided to carryout Response Surface Methodology (RSM) based investigations into the effect of Voltage, Capacitance and work piece vibration Frequency, amplitude on different materials. The RSM based mathematical models of Material Removal Rate (MRR) and Tool Wear Rate (TWR) have been developed using the data obtained through Central Composite Design (CCD). The Analysis of Variance (ANOVA) was performed along with Fisher’s statistical test (F-test) to verify the lack-of-fit and adequacy of the developed mathematical models for the desired confidence interval. The ANOVA table includes sum of squares (SS), degrees of freedom (DF) and mean square (MS). In ANOVA, the contributions for SS is from the first order terms (linear), the second order terms (square), the interaction terms, lack of fit and the residual error. The lack of fit component is the deviation of the response from fitted surface, whereas the residual error is obtained from the replicated points at the center. The MS are obtained by dividing the SS of each of the sources of variation by the respective DF. The p-value is the smallest level of significance at which the data are significant. The Fisher’s variation ratio (F-ratio) is the ratio of the MS of the lack of fit to the MS of the pure experimental error. As per the ANOVA technique, the model developed is adequate within the confidence interval if calculated value of F-ratio of lack of fit to pure error does not exceed the standard tabulated value of F-ratio and the F-values of model should be more than the F-critical for a confidence interval. Further, conformation test was performed to ascertain the accuracy of the developed models.

The entire research work is experiment oriented and the conclusions are drawn based on graphical analysis of experimental results. The research work carried out reveals that the findings are encouraging in establishing the effect of Voltage, Capacitance and work piece vibration Frequency, amplitude on different materials µEDM drilling process performance characteristics. The results of this investigations can be adopted in deciding the optimal values of input process parameters µEDM drilling process.

INTERNATIONAL JOURNAL OF MECHANICAL ENGINEERING

AND TECHNOLOGY (IJMET)

ISSN 0976 – 6340 (Print)

ISSN 0976 – 6359 (Online)

Volume 5, Issue 7, July (2014), pp. 80-100

© IAEME: www.iaeme.com/IJMET.asp

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Keywords: Electrical Discharge Machining (EDM), Central Composite Design (CCD), Material Removal Rate (MRR), Tool Wear Rate (TWR), Response Surface Methodology (RSM). Analysis of Variance (ANOVA). Abbreviations

I Discharge current ton Pulse on time toff Pulse of time A Tool area MRR Material removal rate TWR Tool wear rate WRW Workpiece removal weight TWW Tool wear weight ρ Density T Machining time R.No Run number F Fisher ratio R2 Coefficient of determination

INTRODUCTION

The basis of controlling the micro electro-discharge machining (µEDM) process mostly relies

on empirical methods largely due to the stochastic nature of the sparking phenomenon involving both electrical and nonelectrical processes parameters. Thus the performance of micro electro-discharge machining (µEDM) process is commonly evaluated in the terms of Material Removal Rate (MRR) and Tool Wear Rate (TWR); and to compute MRR and TWR mathematical models are developed. Modeling and analysis of Material Removal Rate (MRR) and Tool Wear Rate(TWR) with the effect of processes parameters like Voltage, Capacitance & Amplitude, Frequency of Vibration on different workpiece thickness is described in this investigation. Conventional Statistical Regression analyses based mathematical models have been developed to establish the input out put relationships. Material Removal Rate (MRR) and Tool Wear Rate(TWR) mathematical models have been developed using the data obtained through Central Composite Design(CCD) The lack-of-fit and adequacy of the developed mode was verified by applying Analysis of Variance (ANOVA).Further the conformation tests were performed to ascertain the accuracy of the developed models.[1]

EXPERIMENTAL DETAILS

Experimental set-up In the present investigation, the experiments were performed in ‘Electronica machine tool

EDM Drill (Rapid drill -II)’ machine. Fig. 2 shows a photograph of EDM machine. The specifications of micro EDM machine are shown in the Table 1.1 The electrolytic copper is used as a tool material because of its higher MRR and less TWR, it also yields a better surface finish. The electrolytic copper tools with different size used to erode water quenched steel k 340 workpiece. The impulse flushing of tap water (dielectric fluid) was employed throughout the experimental investigations. The other quantitative and qualitative micro EDM processes parameters were kept constant for given set of trials.



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Fig. 1: Schematic diagram of the developed vibration unit

Vibration Unit

A simple vibration device has been designed and developed. In order to create a low frequency oscillation on the work piece (Fig.4). An electromagnet is used as the actuator. The electric power is supplied periodically to the electromagnet with the help of a power transistor switch. The on-off sequence of the power transistor is controlled by a frequency controllable pulse generator. When the switch is kept on, the electricity flowing through the circuit causes the electromagnet to be energized, which triggers a pull action on the vibration pad. The flexure beams are bent at that time. Again, the electromagnet is de-energized when the transistor switch is turned off, causing the flexure beams to release and push the vibration pad in upward direction. In this way, a low frequency vibration is induced on the work piece during micro-EDM.

Fig.2: Photograph of ‘electronica machine tool EDM drill (rapid drill -ii)’



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Table 1.1: Electronica machine tool edm drill (rapid drill)

Technical Specifications

Machine Tool Rapid drill II

Work table 450 x 300 mm (Granite)

X & Y axes travel 350, 250 mm

Z axis travel 350 + 300 mm

Max. Electrode length 400 mm

Size of electrode dia. Ø 0.3-3.0 mm

Max. drill depth ≥ 300 mm

Max. coolant pressure 6 MPA

Max. weight of the workpiece 350 kg

Connected load 3 kVA

Work tank 800 x 450 mm

Input power supply 3 phase, AC 415 V*, 50Hz

Net Weight 750 kg

Machine foot print 950 x 850 x 1980 mm

Max. machining current 30 A

TECHNOLOGY

Job material Steel/Brass/Aluminium/Carbide/other conducting materials

Dielectric Tap water/ Coolant soap

Max. drilling speed 20-60mm/min (dia0.5 mm)

Materials used for the experiments

Work piece material 1) Work piece material used for the experiment was K340 steel with the density of 7.77g/cm³ and After quenching of 1040 °C and 520 ~ 530 °C high temperature tempering, the hardness of HRC up to 62 to 63. Table 4.2 depicts the chemical composition of K340 steel.

Table 1.2: Chemical Composition Of K340 Steel By Weight Percentage

C Si Mn Mo V Cr P

1.00 0.91 0.32 2.00 0.28 8.00 0.007

2) Iron sinter is the thermally agglomerated substance formed by heating a variable mixture of iron ores, finely divided coke, limestone, blast furnace dust, steelmaking dust, mill scale and other miscellaneous iron bearing materials in the temperature range 1315 to 1480°C. The product iron sinter is used exclusively as a burden material in the production of iron in the blast furnace. The



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identity of iron sinter is summarized in Table 1.The typical [sameness] specification for Iron Sinter is given in Table 2.

Table 1.3: Identity Of Iron Sinter

Chemical name Iron, sinter

IUPAC name

Other names (usual name, trade name, abbreviation)

Iron sinter

EINECS No. 265-997‐9

CAS name and CAS No. 65996-66‐9

Other identity code: Related CAS No. Hematite (Fe2O3) 1317-60‐8

Molecular formula Fe2O3

Structural information (Crystal lattice)

Minerals of identical or similar composition Hematite

MW (g/mole) MW (g/mole) 159.69

Table 1.4: Sameness Specification For Iron Sinter

Constituent Typical range, % m/m

Fe2O3 >55

FeO <23

SiO2 3-11

Al2O3 <3

CaO 4-20

MgO <4.5

Other elements [Zn, Ti, K, Cr, Mn, S] <5

Free moisture content ≤ 6

Grain size distribution

-8 mm ≥16%

-10 mm ≥26%

-20 mm ≥60%

-30 mm ≥75%

-50 mm ≥90%

-70 mm ≥99%

overall ≥ 85% in the range 5‐70 mm

It is conventional to represent the bulk composition of complex oxide materials, such as iron

sinter, iron ore pellets, minerals, ores and refractory products, in terms of the simple oxides of the constituent elements, as shown in the chemical analysis in Table 2. However, this does not imply that



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the product is composed of a mixture of such simple compounds. It is simply a convenient means of representing the overall elemental composition of the material with each element concentration expressed in the form of its stable oxide. Therefore, although the typical analysis shown for iron sinter indicates that it contains silica [SiO2] and lime [CaO], this does not mean that silica is actually present in free crystalline form, such as quartz or cristobalite, nor does the calcium oxide exist as free lime. In addition, the reference to ‘FeO’ in Table 2 should not be taken as the concentration of the wüstite phase [FeO] in iron sinter since the analysis given for ‘FeO’ is a measure of the amount of iron (II) present in sinter, most of which is present in the form of iron (II,III) oxide or magnetite, Fe3O4. Similarly, ‘Fe2O3’ represents the total iron content expressed as Fe2O3, not the actual Fe2O3 concentration.

a) Tool Electrode Material

The tool electrode material used for the experiments is a pure electrolytic copper (99.9% Cu). The physical and mechanical properties of electrolytic copper are melting point of 1,082 0C, density of 8.97g/cm³, electrical resistivity of 16.7nΩm and thermal conductivity of 393 W/m K.

INPUT PARAMETERS PROCESS OUTPUTS

Fig. 3: General scheme of the micro-edm processes for different parameters

EXPERIMENTAL PROCEDURE The top and bottom faces of k340 steel workpiece were ground to a good surface finish using

a surface grinding machine before experimentation. The initial weights of the workpiece and tool were weighted using a 1 mg accuracy digital weighing machine. The workpiece was held on the machine table using a specially designed fixture. The workpiece and tool were connected to positive and negative terminals of power supply, respectively. The dielectric fluid used was tap water with impulse flushing. The experiments were conducted in a random order to remove the effects of any unaccounted factors. At the end of each experiment, the workpiece and tool were removed, washed, dried, and weighted on digital weighing machine. A stopwatch was used to record the machining time.

Machining Performance Evaluation Material Removal Rate (MRR) and Tool Wear Rate (TWR) are used to evaluate machining performance, expressed as the Workpiece Removal Weight (WRW) and Tool Wear Weight (TWW) per density (ρ) over a period of machining time (T) in minutes, that is

MRR (mm³/min) = WRW/ρT (1.1)

Constant

Parameters

Micro-EDM

Process

Voltage

Capacitance

Frequency

1. MRR

2. TWR



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TWR (mm³/min) = TWW /ρT (1.2)

Relative Tool Wear (RTW), defined as the ratio of material Removal Rate (MRR) to Tool Wear Rate (TWR) and expressed as a percentage, that is

RTW(%) = TWR/MRR X 100 (1.3)

Higher the MRR is the better, where as smaller the TWR and RTW is the better machining performance in EDM process. Therefore, MRR is higher the- better, where as TWR is lower-the-better the better performance characteristics in EDM process. The experimental results are given in table 4.5.

Development of Rsm Based Mathematical Models

The following steps were used for developing RSM based mathematical models 1. Identifying the important process parameters. 2. Developing the design matrix and finding upper and lower limits of process parameters. 3. Conducting the experiments as per the design matrix and recording the responses. 4. Evaluating the regression coefficients and developing the mathematical models for MRR and

TWR. 5. Checking the adequacy of the mathematical models.

Identification of Process Parameters

The independently controllable µEDM parameters affecting the MRR and TWR were identified as voltage (V), Capacitance (C), Amplitude(A) and Frequency of vibration(f) shown in Table 4.4 The other quantitative and qualitative EDM parameters were kept constant for given set of trials.

Developing The Design Matrix And Finding Upper And Lower Limits Of Process Parameters

RSM is used in the design matrix formation which is an empirical modeling approach using polynomial as local approximations to obtain the true input/output relationships. The most popular of the many classes of RSM design is the CCD, which can be naturally partitioned into two subsets of points; the first subset estimates linear and two parameter interaction effects while second subset estimates curvature effects. CCD is a very efficient method for providing much information on parameter effects and overall experimental error in a minimum number of required runs [3, 4]. Thirty–one sets of coded and natural conditions are used to form the design matrix of full factorial central composite design shown in Table 4.5 The design compromises a 24 full factorial Central Composite Design for four independent parameters each at five levels with sixteen cube point plus eight star points and seven replicates at center points [3]. All parameters at the intermediate (0) level constitute the centre points and the combinations of each of the process parameters at either its lowest (-2) or highest (+2) with the other three parameters of the intermediate levels constitute the star points. Run indicates the sequence of trials under the consideration Table 4.5 X1, X2, X3 and X4 represents the notation used for the controllable parameters as shown in Table 4.4. Intermediate levels of coded values were calculated from from the following relationship.

Xi = 2[2X – ( Xmax + Xmin )]/ Xmax - Xmin

Where Xi: required coded values of parameter X



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X any value of the parameter from Xmin to Xmax Xmin and Xmax: lower and upper levels of the parameter X

Table 4.4: Parameter and Range and Levels

Parameters Notation

Units Range and levels

Natural coded -2 -1 0 1 2

Voltage V X1 V 80 100 120 140 160

Capacitance C X2 PF 1000 1200 4700 10000 15000

frequency f X4 f 500 650 675 700 750

Amplitude A X3 A 0.8 1.2 1.5 1.8 2.5

Conducting The Experiments As Per The Design Matrix And Recording The Responses Thirty-one experimental runs were conducted as per the design matrix at the random to avoid

any systematic error creeping into the system. The observed and calculated values of MRR and TWR for different materials and tools are as indicated in design matrix Table 4.5

Evaluating the Regression Coefficients and Developing the Mathematical Models for MRR and

TWR The values of the regression coefficients of the linear, Quadratic and interaction terms of the

models were determined by the following formula:

b= (XT X)-1XTY (1.5) Where, B: matrix of Parameter estimates X: calculation matrix XT: transpose of X Y:matrix of measured response

Response surface modeling was used to establish the mathematical relationship between the

response (Yn) and the various machining parameters [159,164]. The general second order polynomial response surface mathematical model, which analysis the parametric influences on the various response criteria, could be described as follows:

∑ ∑

∑ ∑

(1.6)

Where Yn: responses under study e.g. MRR and TWR Xi: coded values for i= V, C, A and f bo, bi, bii, bij : second order regression coefficients

The second term under the summation sign of this polynomial equation is attributable to linear effect, whereas the third term corresponds to the higher-order effect. The fourth term of the equation includes the interactive effects of the process parameters. Design of Experiments (DOE) features of MINITAB statistical software [7] were utilized to obtain the central composite second order rotatable design and also to determine the coefficients of the mathematical modeling best on the response surface regression model. MINITAB software can also produce ANOVA tables to test the lack-fit of the RSM based models, and offers the “graphic option” to obtain a response surface plot for the selected parametric ranges of the developed response



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surfaces. Furthermore, MINITAB, software also has features enabling data and file management, basic statistics and optimization analysis. Based on Eq. 1.6, the effects of the above mentioned process parameters on the magnitude of the MRR and TWR has been evaluated by computing the values of various constants using MINITAB statistical software and the relevant experimental data from the Table 1.5. Regression coefficients for the Material Removal Rate (MRR) and Tool Wear Rate (TWR) mathematical models were calculated using the coded units. Regression analysis (refer to Table 4.6) indicates the individual and higher order effects of parameters such as Voltage (V), Capacitance (C), Amplitude (A) and frequency(f) with the interaction terms. Predictors with significant contributions in mathematical models are indentified with their p-values less than 0.05. In significant Predictors were eliminated to adjust the fitted mathematical models. R² is another important coefficient called the determination coefficient in the resulting ANOVA test, defined as the ratio of the explained variation to the total variation and as measure of goodness of fit. Hewidey, et. al.,[8]. The R² value is always between 0 and 1. Values of R², R² (pred) and R² (adj) were also calculated (refer to Table 1.7 for the MRR and TWR mathematical models, as R² value approaches unity, the better the response model fit the actual data. Lee and Li [9]. It also indicates the difference between the predicated and actual values.

Table 1.5: Experimental Layout Plan As Per Ccd And Responses

Sr. No.

Run No.

Coded values Natural values Responses for different materials

X1

X2

X3

X4

V

C f A MRR-mm3/mm TWR-%

Y1 Y2 Y3 Y4

1 6 1 -1 1 -1 160 1000 750 0.8 0.000584 0.003084 19 30.19

2 14 1 -1 1 1 160 1000 750 2.5 0.000212 0.002212 19 33.03

3 17 -2 0 0 0 40 8000 625 1.65 0.000348 0.002348 22 32.44

4 12 1 1 -1 1 160 15000 500 2.5 0.000204 0.002204 27 33.05

5 18 2 0 0 0 200 8000 625 1.65 0.000432 0.002432 23 32.54

6 4 1 1 -1 -1 160 15000 500 0.8 0.000576 0.002576 28 31.53

7 28 0 0 0 0 120 8000 625 1.65 0.0036 0.0056 24 24

8 13 -1 -1 1 1 80 1000 750 2.5 0.00019 0.00217 18 32.98

9 10 1 -1 -1 1 160 1000 500 2.5 0.00039 0.002204 28 33.05

10 27 0 0 0 0 120 8000 625 1.65 0.0036 0.0056 24 24

11 1 -1 -1 -1 -1 80 1000 500 0.8 0.000534 0.002534 28 31.47

12 7 -1 1 1 -1 80 15000 750 0.8 0.000542 0.002542 18 31.46

13 23 0 0 0 -2 120 8000 625 -0.05 0.000728 0.002694 26 30.04 14 30 0 0 0 0 120 8000 625 1.65 0.0036 0.0056 24 24 15 22 0 0 2 0 120 8000 875 1.65 0.00098 0.002398 14 32.46 16 15 1 1 1 1 80 15000 750 2.5 0.00017 0.00217 17 32.98 17 29 0 0 0 0 120 8000 625 1.65 0.0036 0.0056 24 24 18 21 0 0 -2 0 120 8000 375 1.65 0.000382 0.002382 30 32.51 19 5 -1 -1 1 -1 80 1000 750 0.8 0.000542 0.00258 18 31.46 20 24 0 0 0 2 120 8000 625 3.35 -0.00016 0.00195 23 33.08 21 8 1 1 1 -1 160 15000 750 0.8 0.000584 0.002584 19 31.51 22 20 0 2 0 0 120 22000 625 1.65 0.00039 0.00239 23 32.49 23 16 1 1 1 1 160 15000 750 2.5 0.000212 0.002212 18 33.03 24 9 -1 -1 -1 1 80 1000 500 2.5 0.000162 0.002162 27 33 25 31 0 0 0 0 120 8000 625 1.65 0.0036 0.0056 24 24 26 2 1 -1 -1 -1 160 1000 500 0.8 0.00039 0.002576 28 31.53 27 19 0 -2 0 0 120 -6000 625 1.65 0.00039 0.00239 24 32.49 28 3 -1 1 -1 -1 80 15000 500 0.8 0.000534 0.002534 28 31.48 29 11 -1 1 -1 1 80 15000 500 2.5 0.000162 0.002162 27 33.03 30 26 0 0 0 0 120 8000 625 1.65 0.0036 0.0056 24 24 31 25 0 0 0 0 120 8000 625 1.65 0.0036 0.0056 24 24



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Table 1.6: Regression Coefficients For Mrr And Twr Mathematical Models

1.6.1: Estimated Regression Coefficients For First Tool

Predictor Y1-MRR model Y2-MRR model Y3-TWR model Y4-MRR model

Coefficient p-value Coefficient p-value Coefficient p-value Coefficient p-value

Constant

X1

X2

X3

X4

X1 x X1

X2 x X2

X3 x X3

X4 x X4

X1 x X2

X1 x X3

X1 x X4

X2 x X3

X2 x X4

X3 x X4

0.003386

0.000113

-0.000001

0.000053

-0.000182

-0.000813

-0.000813

-0.000740

-0.000839

0.000001

-0.000001

0.000022

-0.000001

-0.000025

-0.000022

0.000 *

0.373

0.970

0.028*

0.000*

0.000*

0.000*

0.000*

0.000*

0.964

0.964

0.426

0.964

0.377

0.426

0.005600

0.000119

-0.000022

0.000026

-0.000208

-0.000797

-0.000797

-0.000797

-0.000814

-0.000029

-0.000029

-0.000029

-0.000034

0.000034

-0.000034

0.000 *

0.024

0.184

0.121

0.000*

0.000*

0.000*

0.000*

0.000*

0.163

0.163

0.163

0.108

0.108

0.108

24.000

-0.31598

-0.20

-4.458

-0.458

-0.4063

-0.1563

-0.5313

0.0937

-0.062

0.187

0.063

-0.062

-0.188

0.063

0.000*

0.012*

0.060

0.000*

0.000*

0.001*

0.118

0.000*

0.336

0.627

0.157

0.627

0.627

0.157

0.627

24.0000

0.0435

0.183

-0.0283

0.7783

2.1183

2.1183

2.1171

1.8858

0.0225

-0.0237

0.0225

0.0225

-0.0238

0.0225

0.000*

0.595

0.203

0.057*

0.000*

0. 000*

0.000*

0.000*

0.000*

0.202

0.180

0.202

0.202

0.180

0.202

*Indicates the significant term Hence, the mathematical models in coded form for correlating the Material Removal Rate (MRR) and Tool Wear Rate (TWR) with the considered µ-EDM processes parameters for different materials are given below. Material Removal Rate (MRR)

Y1 = 0.003386 + 0.000113 X1 + 0.000056 X3 + 0.003460 X4- 0.001088 X42 + 0.000001 X1X4

(1.7)

Y2 = 0.005600 + 0.000119 X1 + 0.000064 X3 + 0.003727 X4 - 0.001126 X4

2- 0.000001 X1X4

(1.8) Tool Wear Rate (TWR)

Y3 = 24 + 0.0435 X1+ 0.000145 X2 + 0.00193 X3 - 1.304 X4 - 0.000254 X12- 0.000034 X3

2

+ 0.130 X42+ 0.000037 X1 X3 + 0.00184 X1 X4- 0.000032 X2 X4+ 0.00059 X3 X4

(1.9) Y4= 24 - 0.31598 X1 - 0.000713 X2 - 0.16953 X3- 7.918 X4 + 0.001323 X1

2 + 0.000135 X32

+ 2.6087 X42- 0.000006 X1X3+ 0.000846 X1 X4- 0.000005 X2X4+ 0.000271 X3 X4

(1.10) These developed mathematical models are used to analyze the effect of materials along with considered µ-EDM process parameters on the Material Removal Rate (MRR) and Tool Wear Rate (TWR) values



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Checking the Adequacy of the Mathematical Models for MRR and TWR

The Analysis of Variance (ANOVA) [159,160] was performed along with Fisher’s statistical test (F-test) to verify the lack-of-fit and adequacy of the developed mathematical models for the desired confidence interval. The ANOVA table includes sum of squares (SS), degrees of freedom (DF) and mean square (MS). In ANOVA, the contributions for SS is from the first order terms (linear), the second order terms (square), the interaction terms, lack of fit and the residual error. The lack of fit component is the deviation of the response from the fitted surface, whereas the residual error is obtained from the replicated points at the centre. The MS are obtained by dividing the SS of each of the sources of variation by the respective DF. The p-value is the smallest level of significance at which the data are significant. The Fisher’s variance ratio (F-ratio) is the ratio of the MS of the lack of fit to the MS of the pure experimental error. As per the ANOVA technique, the model developed is adequate within the confidence interval if the calculated value of F-ratio of lack of fit to pure error does not exceed the standard tabulated value of F-ratio and the F-values of model should be more than the F-critical for a confidence interval. Table 1.7 presents the ANOVA for Material Removal Rate (MRR) and Tool Wear Rate (TWR) Mathematical models. It is found that the F-values for MRR and TWR models are greater than the F-critical for a significance level of α = 0.05 and their calculated p-values lack-of-fit are found to be insignificant, as it is desired. Hence, this indicates that the developed second order regression models that link the various machining parameters with MRR and TWR for different materials are adequate at 95% confidence level.

Table 1.7: Anova for mrr and twr mathematical models

Response surface regression: mrra versus A, B, C, D

Analysis of Variance Y1

Source DF Adj SS Adj MS F-Value P-Value Model 14 0.000050 0.000004 92.17 0.000 Linear 4 0.000001 0.000000 5.67 0.005 Square 4 0.000049 0.000012 316.78 0.000 Interaction 6 0.000000 0.000000 0.11 0.005 Error 16 0.000001 0.000000 Lack-of-Fit 10 0.000000 0.000000 0.26 0.970 Pure Error 6 0.000000 0.000000 Total 30 0.000050 Model Summary S R-sq R-sq(adj) R-sq(pred) 0.0001959 98.78% 97.70% 96.70% Analysis of Variance Y2 Source DF Adj SS Adj MS F-Value P-Value Model 14 0.000057 0.000004 649.76 0.000 Linear 4 0.000001 0.000000 44.40 0.000 Square 4 0.000056 0.000014 2226.00 0.000 Interaction 6 0.000000 0.000000 2.51 0.046 Error 16 0.000000 0.000000 Lack-of-Fit 10 0.000000 0.000000 1.7 0.98 Pure Error 6 0.000000 0.000000 Total 30 0.000057



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Model Summary S R-sq R-sq(adj) R-sq(pred) 0.0000791 99.82% 99.67% 98.99% Analysis of Variance Y3 Source DF Adj SS Adj MS F-Value P-Value Model 14 499.336 35.667 139.76 0.000 Linear 4 485.167 121.292 475.27 0.000 Square 4 12.794 3.199 12.53 0.000 Interaction 6 1.375 0.229 0.90 0.020 Error 16 4.083 0.255 Lack-of-Fit 10 4.083 0.408 1.79 1.2 Pure Error 6 0.000 0.000 Total 30 503.419 Model Summary S R-sq R-sq(adj) R-sq(pred) 0.505181 99.19% 98.48% 95.33% Analysis of Variance Y4 Source DF Adj SS Adj MS F-Value P-Value Model 14 383.660 27.404 3899.52 0.000 Linear 4 14.733 3.683 524.12 0.000 Square 4 368.845 92.211 13121.28 0.000 Interaction 6 0.082 0.014 1.94 0.000 Error 16 0.112 0.007 Lack-of-Fit 10 0.112 0.011 1.77 0.92 Pure Error 6 0.000 0.000 Total 30 383.772 Model Summary S R-sq R-sq(adj) R-sq(pred) 0.0838308 99.97% 99.95% 99.83% CONFORMITY EXPERIMENTS OF MATHEMATICAL MODELS In order to determine the accuracy of developed mathematical models, the conformity experiments were conducted using the same experimental set up. The process parameters were assigned the intermediate values other than that used in design matrix and the validation test runs where carried out. The responses were computed and compared with the predicted values and are given in Table 1.8 and Table 1.9 for MRR and TWR mathematical models respectively. The percentage error of the developed RSM based mathematical models is found to be within ±5%, which clearly indicates the accuracy of developed mathematical models. The experimental and the predicated values of MRR and TWR for Validation data set are illustrated in Fig.3 and 4 respectively.



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Table 1.8: Conformity Experiments for MRR Mathematical Models

Run Natural values Experimental Values -MRR

V C f A MRRA MRRB

1 60 800 400 0.6 0.003001 0.009050

2 120 900 450 0.7 0.002731 0.01870

3 75 1200 470 0.5 0.004820 0.01515

4 110 1300 450 0.9 0.004216 0.01770

5 90 1100 420 0.8 0.003400 0.01650

Predicted Values % Error

MRR – mm3/min Experimental – predicted/Experimental x 100

0.002923 0.008832 2.60 2.41

0.002651 0.01935 2.93 -3.48

0.005005 0.01485 -3.84 1.98

0.004125 0.01853 2.16 -4.48

0.003504 0.01599 -3.06 3.00

54321

0.020

0.018

0.016

0.014

0.012

0.010

Run no

Mate

rial Removal Rate

(mm3/mm)

Experimental Values

Predicted Values

Variable

Experimental Values, Predicted values

Fig. 3: Comparison of the experimental and predicted values for MRR



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Table1.9: Conformity experiments for twr mathematical models

Run Natural values Experimental Values -TWR

V C f A EWRA EWRB

1 60 800 400 0.6 29.10 38.16

2 120 900 450 0.7 28.70 30.85

3 75 1200 470 0.5 28.12 33.10

4 110 1300 450 0.9 26.36 15.30 5 90 1100 420 0.8 27.60 34.83

Predicted Values % Error

TWR in % Experimental – predicted/Experimental x 100

30.03 39.75 -3.19 -4.17

29.69 31.70 -3.45 -2.76

28.82 34.20 -2.48 -3.32 25.27 14.81 4.13 3.20

28.59 33.95 -3.58 2.52

54321

30

29

28

27

26

25

Run no

Tool W

ear Rate

in %

Experimental Values

Predicted Values

Variable

Experimental Values,Predicted Values

Fig. 4: Comparison of the experimental and predicted values for TWR

EXPERIMENTAL RESULTS AND DISCUSSION The graphical analysis is the most useful approach to predict the response for different values of the test parameters and to identify the type of interaction between test variables [160]. Hence, analysis of the parametric influences along with effect of different material as well as amplitude and frequency of vibration was done based on Response Surface Methodology (RSM) and presented in a graphical form. The consolidated graphs are drawn based on the computed response value for the analysis of parametric influences. Direct Effect of process parameters on MRR and TWR

Effect of voltage on MRR and TWR Experimentally it is found that increasing voltage increases the Material Removal rate (MRR) and Tool Wear Rate (TWR) (Table 1.10 and 1.11) (Fig.5 and 6). It can be seen (Fig.5) that the Material Removal Rate (MRR) increases almost linearly with increasing voltage. Whereas the Tool



94

Wear Rate (TWR) (Fig. 6) increases rapidly at the beginning and then slow down with increase in voltage. The increase in voltage increases discharge current that means pulse energy, which leads to an increase in the rate of heat energy, which is subjected to both of the electrodes, and in the rate of melting and evaporation hence the Material Removal rate (MRR) and Tool Wear Rate (TWR) increases with voltage, but after certain limit Tool Wear Rate(TWR) decreases because discharge current and hence melting and evaporation. [10, 11].

Table 1.10: Effect of Voltage (V) On Mrr

Voltage Y1 Y2

40 0.000348 0.002348

80 0.000534 0.002534

120 0.0036 0.0056

160 0.000584 0.003084

200 0.00393 0.002432

2001751501251007550

0.006

0.005

0.004

0.003

0.002

0.001

0.000

Voltage (V)

Mate

rial Removal Rate

(MRR) -m

m3/mm

MRR Y1

MRR Y2

Variable

Materia Removal Rate MRR (MRR) mm3/mm for Y1and Y2

Fig 5: Effect of voltage (v) on mrr

Table 1.11: Effect of voltage (v) on twr

Voltage Y3 Y4

40 22 32.44

80 28 31.47

120 24 24

160 19 30.19

200 23 32.54



95

2001751501251007550

34

32

30

28

26

24

22

20

Voltage (V))

Tool W

ear Rate

(TW

R) in %

TWR Y3

TWR Y4

Variable

Tool Wear Rate in % for Y3 & Y4 Vs Voltage

Fig.6: Effect of voltage on TWR

Effect of capacitance on MRR and TWR

In the µEDM drilling process, for Electronica Rapid Drill Machine Tool for micro Drilling between 0.3mm to 0.5mm drilling process. Best possible capacitance rang for micro drilling is 8000 C to 20000 C (Table 1.12) (Fig.7) below this capacitance there is not sufficient energy between electrodes between anode and cathode and less melting and evaporation of the material. Hence Less Material Removal Rate (MRR) above 20000 also as there is high energy between anode and cathode and flow of melted materials solidifies their only and less evaporation. Same case is there with Tool Wear Rate (TWR) best possible capacitance for tool wear rate is 8000 C to 20000 C (Table 1.13) (Fig. 8) Minimum Tool Wear Rate is in between 8000 C to 20000 C because of optimum rate of tool material melting and evaporation in that zone . Above and below of that zone there is no optimum melting and evaporation of tool material so in that zone there is high Tool Wear Rate (TWR).

Table 1.12: Effect of Capacitance (C) on MRR

Capacitance Y1 Y2

1000 0.000212 0.002212

8000 0.000348 0.002348

15000 0.0036 0.0056

22000 0.00039 0.00239

-6000 0.00039 0.00239



96

2500020000150001000050000-5000-10000

0.006

0.005

0.004

0.003

0.002

0.001

0.000

Capacitance (C)

Mate

ria

l R

em

ova

l R

ate

(M

RR

) m

m3

/mm

MRR Y1

MRR Y2

Variable

Material Removal Rate (MRR) mm3/mm vs Capacitance

Figure 7: Effect of capacitance (c) on MRR

Table 1.13: Effect of capacitance (c) on TWR

Capacitance Y3 Y4

1000 19 33.03

8000 22 32.44 15000 24 24

22000 23 32.49

-6000 24 32.49

2500020000150001000050000-5000-10000

34

32

30

28

26

24

22

20

Capacitance (C)

Tool

Wear R

ate

in

%

TWR in % for Y3

TWR in % for Y4

Variable

Tool Wear Rate in % (TWR) vs Capacitance (C)

Fig.8: Effect of capacitance on tool wear rate (TWR)

Effect of frequency on MRR and TWR Experimentally it is found that the Material Removal Rate (MRR) almost increases linearly with increasing frequency particularly in steel materials as frequency increases debris entrapped in



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between tool and work piece material removed easily because of this micro work piece vibration frequency. Best possible vibration frequency is 700 f to 900 (Table 1.14) (Figure 9). Above and below this vibration frequency there is not appropriate debris and scrap removal between tool and work piece hence not best Material Removal Rate (MRR). Same the case in Tool Wear Rate (TWR) minimum Tool Wear Rate in between 600 f to 900 f (Table 1.15) (Fig.10).

Table 1.14: Effect of Frequency (F) on MRR

Frequency Y1 Y2

375 0.000382 0.002382

500 0.00039 0.002576

625 0.000432 0.002432

750 0.000542 0.00258

875 0.00098 0.002398

900800700600500400

0.0025

0.0020

0.0015

0.0010

0.0005

Frequency (f)

Mat

eria

l rem

ova

l R

ate

in

mm

3/m

m

MRR of Y1

MRR of Y2

Variable

Material Remova Rate in mm3/mm of Y1 & Y2 vs frequency (f)

Figure 9: Effect of frequency (f) on MRR

Table 1.15: Effect of frequency (f) on TWR

Frequency Y3 Y4

375 30 32.51

500 28 31.53

625 23 32.54

750 18 31.46

875 14 32.46



98

900800700600500400

35

30

25

20

15

Frequency (f)

Tool W

ear Rate

in %

TW R for Y3

TW R for Y4

Variable

Tool We ar Rate (TWR) in % for Y3 & Y4 vs Frque ncy

Fig.10: effect of frequency (f) on tool wear rate (twr)

Effect of amplitude on mrr and twr Experimentally it is found that Material Removal Rate (MRR) increases as amplitude goes on increases (Table 1.16) (Figure 11) up to certain limit afterwards again it decreases because gap between tool and work piece increases and material removal rate again decreases. Optimum Material Removal Rate (MRR) occurs in between 0.8 A to 2.5A. Tool Wear Rate (TWR) decreases as Amplitude goes on increase up to certain limit afterwards again it increases (Table 1.17) (Fig.12). Optimum Tool Wear Rate (TWR) occurs in between 0.8 A to 2.5A.

Table 4.16: Effect of Amplitude (A) on MRR

Amplitude Y1 Y2

-0.05 0.000728 0.002694

0.8 0.000584 0.003084

1.65 0.0036 0.0056

2.5 0.000292 0.002212

3.35 -0.00016 0.00195

3.53.02.52.01.51.00.50.0

0.006

0.005

0.004

0.003

0.002

0.001

0.000

Amplitude (A)

Mate

ria

l R

em

ova

l R

ate

mm

3/m

m

MRR o f Y1

MRR o f Y2

Variab le

M ate rial Re moval Rate o f ( M RR) Y1 & Y2 vs Amplitude (A)

Figure 11: Effect of amplitude (a) on MRR



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Table 1.17: Effect of amplitude (a) on TWR

Amplitude Y3 Y4

-0.05 26 30.04

0.8 19 30.19

1.65 24 24

2.5 18 33.03

3.35 33 33.08

3.53.02.52.01.51.00.50.0

35

30

25

20

Amplitude (A)

Tool

Wear R

ate

in

%

TWR of Y3

TWR of Y4

Variable

Tool Wear Rate (TWR) of Y3, Y4 vs Amplitude (A)

Fig.12: Effect of Amplitude (A) on Tool Wear Rate (TWR)

REFERENCES

1. M.S. Sohani, V.N. Gaitonde, B.Siddeswarappa, A.S.Despande, “Investigation into the effect

of tool shapes with size factor consideration in sink electrical discharge machining (EDM) process.”Int.J.Adv.Manuf.Technol.Doi 10.1007/S00170-009-2044-5.

2. M. P. Jahan ,T. Saleh, M. Rahman, Y. S. Wong,Oct.2010, “Development, Modeling, and Experimental Investigation of Low Frequency Workpiece Vibration-Assisted Micro-EDM of Tungsten Carbide.” Journal of Manuf. Sci.& Engg., Vol 132 ,54503 pp 1-3.

3. FT. Weng, M.G. Her, Study of the batch production of micro parts using the EDM process, Int. J. Adv. Manuf. Technol. 19 (4) (2002) pp. 266-270.

4. K.P. Rajurkar, Z.Y. Yu, 3D micro-EDM using CAD/CAM, Ann. CIRP 49(1) (2000), pp. 127-130.

5. Cochran WG, Cox GM (1992), Experimental Designs. John Wiley and Sons, New York. 6. Cogun C, Akaslan S (2002), The effect of machining parameters on tool electrode wear and

machining performance in electric discharge machining. KSME Int J 16(1): pp. 46-59. 7. Minitab Inc (2006) Minitab user manual version 13, Quality Plaza, 1829 Pine Hall Road, State

College, PA 16801-3008, USA. 8. Hewidy MS, El-Tawee! TA, El-Safty MF (2005), Modeling the machining parameters of wire

electrical discharge machining of Inconel 601 using RSM. J Mater Process Technol 169: pp. 328-336.



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9. Lee SH, Li XP (2001), Study of the effect of machining parameters on the machining characteristics in electrical discharge machining of tungsten carbide. J Mater Process Technol 115: pp. 344-358.

10. J.A. Sanchez, I. Cabanes, L.N. Lopez de Lacalle, A. lamikiz, Development of optimum electro discharge machining technology for advanced ceramics, Int. J. Adv. Manuf. Technol. 18 (12) (2001) pp. 897-905.

11. T.C. Lee, J.H. Zhang, W.S. Lau, Machining of engineering ceramics by ultrasonic vibration assisted EDM method, J. Mater. Manuf. Processes 13 (1) (1998) pp. 133-146.

12. S. K. Sahu and Saipad Sahu, “A Comparative Study on Material Removal Rate by Experimental Method and Finite Element Modelling in Electrical Discharge Machining”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 5, 2013, pp. 173 - 181, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

13. Mane S.G. and Hargude N.V., “An Overview of Experimental Investigation of Near Dry Electrical Discharge Machining Process”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 3, Issue 2, 2012, pp. 22 - 36, ISSN Print: 0976-6480, ISSN Online: 0976-6499.

14. Rodge M.K, Sarpate S.S and Sharma S.B, “Investigation on Process Response and Parameters in Wire Electrical Discharge Machining of Inconel 625”, International Journal of Mechanical Engineering & Technology (IJMET), Volume 4, Issue 1, 2013, pp. 54 - 65, ISSN Print: 0976 – 6340, ISSN Online: 0976 – 6359.

15. A. Parshuramulu, K. Buschaiah and P. Laxminarayana, “A Study on Influence of Polarity on the Machining Characteristics of Sinker EDM”, International Journal of Advanced Research in Engineering & Technology (IJARET), Volume 4, Issue 3, 2013, pp. 158 - 162, ISSN Print: 0976-6480, ISSN Online: 0976-6499.

EFFECT OF VIBRATION ON MICRO-ELECTRO-DISCHARGE MACHINING

Engineering

Transcript of EFFECT OF VIBRATION ON MICRO-ELECTRO-DISCHARGE MACHINING