Quality Engineering research and design of experiment

1. Design of Experiments on metal removal rate of
Aluminum composite in electrochemical
machining
1
Lecturer:. Prof. Dr. Noordin
Quality Engineering
Prepared By:
Ali Karandish
Ali tolooie
Fatemeh niazi
Amin soleimani

2. 2
OVER
VIEW

3. 3
Introduction

4. 4
Introduction
Electrochemical machining
The metal is removed by the anodic dissolution in an
electrolytic cell in which work piece is the anode and the
tool is cathode. The electrolyte is pumped through the gap
between the workpiece and the tool, while direct current is
passed through the cell, to dissolve metal from the work
piece.
Ruszaj and Zyburaskrabalak developed a mathematical
model for ECM utilizing a flat ended universal electrode [1].
It was observed that better material removal rates and low
surface waviness can be achieved when compared with the
ball ended electrodes.
Later on, Hocheng et al. used the concept of redistribution
of electric energy to erode a hole in the thin metal of sheet
[2].

5. 5
Literature Review

6. 6
Literature review
It is very difficult to identify the optimal process parameters of ECM with
this type of experimental study. Therefore, the establishment of the
mathematical models is essential to correlate the input-output parameters
using statistical regression analysis. Non-linear regression models for ECM
were developed by Ravikumar et al. with voltage, current, flow rate of
electrolyte and gap between the electrode and workpiece as input
parameters, and metal removal rate (MRR), surface roughness (SR) are
treated as responses [3].
Later on, Senthilkumar et al. used response surface methodology (RSM) to
study the characteristics of ECM of Al/15% SiCp composites. Contour plots
were constructed between the responses MRR and SR, and process
parameters, namely applied voltage, electrolyte concentration, electrolyte
flow rate and tool feed rate [4].

7. 7
Literature review
Ashokan et al. used multiple regression analysis and artificial neural
networks (ANN) for the multi-objective optimization of ECM process [5].
Moreover, in [6] also the authors used ANN for the prediction of ECM
process parameters. The output of the NN contains two outputs, such as
MRR and SR, whereas the input layer is provided with three inputs,
namely applied voltage, feed rate and electrolyte flow rate. Fuzzy logic had
also been used by Ramarao et al. to model the ECM process with voltage,
current, electrolyte flow rate and gap between the electrodes as inputs and
MRR and SR as outputs [7].
It is also important to note that evolutionary algorithms, such as genetic
algorithms [8, 9, 11], particle swarm optimization [10] and differential
evolution [11] were also used for the parametric optimization of ECM
process by different authors. More over, Non-dominated sorting genetic
algorithm (NSGA-II) was also used by Senthil kumar at al. for parametric
optimization of electrochemical machining[12].

8. 8
Material and
methods

9. 9
Material and
methods
The base material used in the present work is Al/15% SiCp composites
with the chemical composition which mentioned below.
In this study an attempt is made to establish the input-output relationship of
electro chemical machining (ECM) of Al/15% SiCp composites. It is
important to note that selection of the range of operating parameters is an
important consideration. A pilot study has been conducted to determine the
appropriate working ranges of the parameters. The levels of the process
parameters selected are given below.

10. 10
Material and
methods
For the four variables the design required 27experiments with 16 factorial
points, eight axial points to form central composite design with α=1 and
three center points for replication to estimate the experimental error. The
design was generated and analyzed using (Desing Expert 7). statistical
package. The levels of each factor were chosen as −1, 0, 1 in coded form
to have a central composite design as shown in next page table.
Response surface methodology (RSM) is used for establishing the
mathematical relationship between the response (Yu) and various input
process parameters. In order to study the effect of the ECM input process
parameters on the metal removal rate, a second-order polynomial
response can be fitted into the following equation:

11. 11
Material and
methods

12. 12
Experimental
design

13. 13
Material and
methods
The fabrication of Al/15% SiCp composites was carried out by stir casting
process. 25mm diameter and 20mm length specimens were prepared from
these castings. The circular cross section tool made up of copper is used.
The electrolyte used for experiment was fresh NaCl solution with different
concentrations, because of the fact that NaCl electrolyte has no
passivation effect on the surface of the job. Electrolyte was axially fed to
the cutting zone through the central hole of the tool. The MRR was
measured from the mass loss.

14. 14
Analyze of Variance
(ANOVA) of MRR

15. 15
Analyze of Variance of
MRR

16. 16
Analyze of Variance of
MRR
Selecting the significant factors/interactions…

17. 17
Analyze of Variance of
MRR
As it is obvious from Normal plot the factors A, B, C and D have the
potential to be significant due to far distance from the other factors and
observations.

18. 18
Analyze of Variance of
MRR
The value of the R2 is over 92% which indicates that the developed model
shows the good relationship between the input parameters and output
response (MRR) according to the "Predicted R-Squared" of 0.8894 is in
reasonable agreement with the "Adj R-Squared" of 0.9096.

19. 19
Analyze of Variance of
MRR
The Model F-value of 63.87 implies the model is significant. There is only a
0.01% chance that a "Model F-Value" this large could occur due to noise.
Values of
"Prob > F" less than 0.0500 indicate model terms are significant. In this
case A, B, C, D are significant model terms. Values greater than 0.1000
indicate the model terms are not significant. If there are many insignificant
model terms (not counting those required to support hierarchy), model
reduction may improve your model.
The "Curvature F-value" of 1.83 implies the curvature (as measured by
difference between the average of the center points and the average of the
factorial points) in the design space is not significant relative to the noise.
There is a 19.00% chance that a "Curvature F-value" this large could occur
due to noise.
The "Lack of Fit F-value" of 13.57 implies there is a 7.08% chance that a
"Lack of Fit F-value" this large could occur due to noise. Lack of fit is bad -
- we want the model to fit. This relatively low probabiity (<10%) is troubling.

20. 20
Analyze of Variance of
MRR
The P value of the model is lower than 0.05(i.e. level of significance
α=0.05, or 95% confidence), which indicates that the developed model is
statistically significant. The results prove that all the input parameters, i.e.
voltage, feed rate, electrolyte concentration and percentage of
reinforcement have their influence on the metal removal rate.

21. 21
Result and
Discussion

22. 22
Result and
discussion
The surface plots for the response of MRR were drawn. Figure 4 shows
that functional dependence of MRR on the voltage and feed rate for the
invariable electrolyte concentration value of 20 g/lit and B4C value of 5
wt%. From figure 4, the MRR increases with increase in voltage and feed
rate. With increase in applied voltage, the machining current in the inter
electrode gap (IEG) increases, which leads to the enhancement of MRR. It
is also interesting to note that increased feed rate reduces the IEG that
leads to increase in the current density in the gap. This effect causes rapid
anodic dissolution which increases the MRR.

23. 23
Figure 5 shows that functional dependence of MRR on the electrolyte
concentration and percentage of reinforcement for the invariable voltage value
of 16 volts and feed rate value of 0.6 mm/min. From Fig 5, it is observed that
increase in electrolyte concentration increases the MRR. With increasing the
electrolyte concentration the electrical conductivity of the electrolyte increases
and also that releases large number of ions in IEG, which results in higher
machining current in IEG and causes higher MRR. Moreover, from Fig 6 the
MRR decreases with an increase in percentage of reinforcement. This may be
due to the fact that by increasing the percentage of reinforcement, the electrical
conductivity of the work piece decreases, because the reinforced particles are
poor conductors than the base material. Thus increase in the percentage of
reinforcement leads to lower metal removal rate.
Result and
discussion

24. 24
Figure 6 shows the scatter plots for the prediction of MRR for the non-
linear statistical model. From Fig. 6, it can be observed that the predicted
values for MRR are seen to be very close with the experimental values. It
is clear from the fact that the points are scattered very close to the best fit
line.
Result and
discussion

25. 25
Conclusion

26. 26
In the present study, aluminum MMC was fabricated with the help of stir
casting method. It is interesting to note that percentage of reinforcement
has been considered as one of the process parameter that influences the
quality of the parts produced using ECM. Later on, the electrochemical
machining of aluminum MMC has been modeled with the help of non-linear
regression model. The performance characteristic viz. MRR is considered
as response and various machining parameters, namely voltage, feed rate,
electrolyte concentration and percentage of reinforcement are treated as
inputs of the model.
Mathematical model was developed for the response MRR using response
surface methodology and the model was analyzed using ANOVA. In the
present study, surface plots are constructed to study the influence of input
process parameters on the response of non-linear models. MRR
decreases with the increase in percentage of reinforcement and increases
with increase in voltage, feed rate and electrolyte concentration. It is to be
noted that the findings of the experimentation are matching with the results
available in the literature. Further, the developed models are tested for
their prediction accuracy using twenty experimental test cases. The
predicted values are closely related with the experimental values.
Conclusion

27. 27
References

28. 28
[1] Ruszaj M, Zybura-Skrabalak M, 2001, J Mater Process Technol, 109:
333-338
[2] Hocheng H, Kao PS, Lin SC, 2005, Int J of Adv Manuf Technolo, 25:
1105-1112
[3] Ravikumar R, Ashokan P, Singh PN, 2008, J Manuf Eng, 3(2): 104-114
[4] Senthilkumar C, Ganesan G, and Karthikeyan R, 2009, Int J of Adv
Manuf Technolo, 43: 256-263
[5] Ashokan P, Ravikumar R, Jayapaul R, Santhi M, 2008, Int J of Adv
Manuf Technolo, 39: 55-63
[6] Abuzeid HH, Awad MA, Senbel HA, 2012, Int J Comp Sci Eng, 4(1):
125-132
[7] Ramarao S, Sravan CRM, Pandu RV, Padmanabhan G, 2009, Fuzzy
logic based forward modeling of electrochemical machining process, IEEE
Proceedings of the World Congress on Nature and Biologically Inspired
Computing, Chennai, India, pp. 1431-1435
[8] Jain NK, Jain VK, 2007, Int J Mach Sci Technolo, 11(2): 235-258
[9] Datta D, Das AK, 2010, LNCS, 6457: 485-493
[10] Rao RV, Pawar PJ, Shankar R, 2008, Part B: J Eng Manuf, 222(8):
949-958
[11] Ramarao S, Padmanabhan G, Surekha B, Pandu RV, 2009, J Mach
Form Technolo, 1(3/4): 265-278
[12] Senthilkumar C, Ganesan G, Karthikeyan R, 2011, Transactions of
nonferrous meterial socity of China, 21: 2294-2300
Refferences

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