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
7. 85
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 16.7nm and thermal conductivity of 393 W/m K.
8. 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 () over a period of machining time (T) in minutes, that is
MRR (mm³/min) = WRW/T (1.1)
11. 87
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:
13. 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