Development of EDM Tool for Fabrication of Microchannel Heat … · Electrical discharge machining...

IJIRST –International Journal for Innovative Research in Science & Technology| Volume 2 | Issue 09 | February 2016 ISSN (online): 2349-6010

All rights reserved by www.ijirst.org 63

Development of EDM Tool for Fabrication of

Microchannel Heat Sink and Optimization of

Single Response Parameter of EDM by Taguchi

Method

Mahesh B. Mohite Prof. V. P. Gaikwad

PG Scholar Professor

Department of Mechanical Engineering Department of Mechanical Engineering

D.K.T.E.’S TEI Ichalkaranji D.K.T.E.’S TEI Ichalkaranji

Abstract

Electrical discharge machining (EDM) is one of the advanced methods of machining. EDM is best option for extremely difficult

hard materials and complicated geometry materials which are very difficult to machine. The main aim of this work is to develop

electrode for natural leaf shape of microchannel heat sink to reduce fabrication time and investigated the single response

parameter optimization on EDM machine for microchannel heat sink of Aluminium 6061 .The performance of microchannel is

depend upon parameters of machining is selected during fabrication. The optimal set of process parameters such as current, pulse

ON and OFF time in Electrical Discharge Machining (EDM) process were used to study the response of single parameter such as

rate of material removal and surface roughness value on the Aluminium 6061 work piece material. The methodology based on

orthogonal array Taguchi’s analysis of variance (ANOVA) and signals to noise ratio (S/N Ratio) were employed to optimize the

performance characteristics like material removal rate and surface roughness of Aluminium 6061.

Keywords: Microchannel heat sink, EDM Electrode, Electrical Discharge Machining, Taguchi Method, ANNOVA,

Material Removal Rate, Surface Roughness, Orthogonal Array Signal-to-Noise ratio

_______________________________________________________________________________________________________

I. INTRODUCTION

There are different shapes like Square, triangular and trapezoidal shapes of microchannel are used for cooling purpose in several

scientific and commercial applications like Micro-Electro-Mechanical System (MEMS), Electronic cooling system, Biomedical

Engineering; Miniaturization and Biochemistry etc. In this work natural leaf shape pattern of microchannel heat sink is designed

and Electrode is developed for fabrication of designed microchannel on EDM. The main aim of this work is to develop electrode

for leaf shape microchannel to reduce fabrication time and study the single response parameter of EDM by using Taguchi

method. Many authors have presented their works on the Optimization of process parameters for various machining processes.

Raghuraman S et al. [1] have done EDM process optimization with multiple performance characteristics based on orthogonal

array with grey relational analysis for Mild Steel 2026 with copper electrode. R.Boopathi et al. [2] studied Influence of Process

Parameters for Electrical Discharge Machine Using Nano Particle and Brass Electrode. Response surface methodology has been

used by Tarun Modi et.al. [3] For Electrical Discharge Machining process with air hardening tool steel. Gaurav Raghav et.al [4]

investigated on Optimization of Material Removal Rate in Electric Discharge Machining Using Mild Steel.

II. EXPERIMENTAL DETAILS

Materials and Methods Used:

For this work Aluminium 6061 material is chosen for fabrication of microchannel heat sink. Aluminium 6061 has good ability to

absorb heat and have also good surface finish. Table 1 and Fig.1 material properties of electrode. Table - 1

Properties of Electrode Material

Properties Value

Melting point 1083ºC

Elastic modulus(E) 1.23×105 N/mm2

Poisson’s ratio 0.26

Density 8.9 gm./cm3

Development of EDM Tool for Fabrication of Microchannel Heat Sink and Optimization of Single Response Parameter of EDM by Taguchi Method (IJIRST/ Volume 2 / Issue 09/ 011)


Fig. 1: Drawing of Leaf Shape Micro channel Heat Sink

Fig. 2: 3D model of Micro channel

Development of EDM Electrode:

With the help of designed drawing of leaf shape micro channel, the electrode is developed. The details of electrode is shown in

fig.3

Fig. 3: Details of developed tool



Fig. 4: Developed Copper electrode used for experiment

Machine is provided with fixed pulse voltage. The current, pulse ON time and pulse OFF time were selected from the range.

Table 2 shows the Machine specifications and Table 2 shows the working conditions and description of EDM. Table - 2

Machine specifications

Description Details

Supply voltage 75V

Discharge current 30A

Servo system Electromechanical

Power consumption 2kW

Model ELECTRONICA

Fig. 5: EDM machine

Table – 3

Working conditions and description of EDM

Working conditions Description

Work piece aluminium (6061)

Electrode Copper

Discharge current 2,3,5 amps

Pulse ON time 30,50,90 μs

Pulse OFF time 5,7,9 μs

Dielectric medium EDM oil

Fabrication of Leaf Shape Microchannel by Using Developed Electrode:

With the help of developed electrode the microchannel is fabricated on EDM machine. During fabrication of microchannel the

different parameter is studied and finding the response of single parameter of EDM. The microchannel is fabricated by using

developed tool is shown in fig.6



Fig. 6: Image of fabricated micro channel heat sink

Defining Levels of Critical Parameters:

In this work, three factors are selected. Levels indicate variation in the factors used for research work. Three levels of each

parameter are selected for experimentation. These three levels are as follows. Table - 4

Response parameters and control parameters with their levels

Response

Parameters

Material Removal Rate (mm3 /min.)

Surface Roughness (μm)

Control Parameters Levels

1 2 3

Discharge current (A) 2 3 5

Pulse ON time (μs) 30 50 90

Pulse OFF time (μs) 5 7 9

Design of Experiment:

Design of experiment is technique used for experimentation. There are various forms of DOE viz. Response surface method,

Taguchi method, and Factorial design methods are used for experimentation. Taguchi technique is used for this work. In

experimentation, according to Taguchi design of L9 array, total 9 trials are experimented.

Analysis of Samples by Simulation Method:

After creating design by using Taguchi technique all experimental trials are carried according to Taguchi experimentation plots,

samples are segregated for testing. This includes Surface roughness (Ra value in µm) using surface roughness tester as per the

tagging applied to the samples, and evaluating the MRR value by using equation 1.

Finding Optimal Solution:

After Experimentation carried, all the results obtained are plotted according to their trial numbers. Analysis of optimal solution is

based on requirement. There are three conditions at which we get optimal solution.

- Larger is better = -10log 10 (sum (1/Y2)/n)

- Nominal the better = -10log 10 (Y2)

- Smaller the better = -10log 10 (sum (Y2/N))

For this research work, Larger the better condition is used for response variable Material removal rate needs to maximize to

get good results. And Smaller the better condition is used for response variable surface roughness needs to minimize to get good

results.

Validation:

Validation of result is very important to know our obtained results are true or wrong Hence validation gives evidence behind

every experimental result. Validation can be either way. One way, is comparing both experimental and analytical result. Other

way is to set target value and experimenting other value to ensure not to cross targeted value.

Conclusion:

Conclusion is based on result obtained and validation part. Conclusion is what we have observed with results and graph obtained.

It tells about effect and contribution of input parameters on response value.



III. EXPERIMENTATION

Design of Experiments:

The experimental layout for the process parameters using the L9 orthogonal array was used in this study. This array consists of

three control parameters and three levels, as shown in table 3. In the Taguchi method, most all of the observed values are

calculated on the ‘higher the better’ and the ‘smaller the better’. Thus in this study, the observed values of Material Removal

Rate were set to maximum as desired values and surface roughness were set to minimum desired values. Table - 5

Design Matrix of L9 Orthogonal Array

Exp. No Parameters

A B C

1 1 1 1

2 1 2 2

3 1 3 3

4 2 1 2

5 2 2 3

6 2 3 1

7 3 1 3

8 3 2 1

9 3 3 2

Material Removal Rate:

The material removal rate of the work piece is the volume of the material removed per minute. It can be calculated using the

following relation.

MRR = (𝑊𝑖 − 𝑊𝑓) × 1000

(𝐷𝑤 × 𝑡) (1)

MRR – Material Removal Rate (mm3/min)

Wi - Initial weight of work piece (gm.)

Wf - Final weight of work piece (gm.)

Dw - Density of the work piece (gm. /cm3)

t - Period of trial (min)

Surface Roughness:

The parameter mostly used for general surface roughness is Ra. It measures average roughness by comparing all the peaks and

valleys to the mean line, and then averaging them all over the entire cut-off length. The surface roughness is measured by surface

roughness tester machine, which is shown in Fig.7

Fig. 7: Equipment for Surface Roughness Measurement

IV. EXPERIMENTAL RESULTS

Determination of Optimal Process Parameters for Material Removal Rate:

In this section, L9 orthogonal array is used to determine the optimal process parameters. The results are reported in S/N ratio and

ANOVA analysis. In Taguchi method, there are three performance characteristics such as higher-is-better, nominal-is-better and



lower-is-better. Here higher-is-better characteristics are used to find the optimal process parameter for material removal rate. The

experimental value of MRR and S/N ratio for MRR is listed in table Table - 6

Signal-to-Noise ratios for MRR

Exp.

No

Current

(A)

Pulse ON

Time (μs)

Pulse OFF

Time (μs)

MRR

(mm3/min) S/N RATIO FOR MRR (dB)

1 2 30 5 0.0105 -39.5762

2 2 50 7 0.0085 -41.4116

3 2 90 9 0.0082 -41.7237

4 3 30 7 0.0105 -39.5762

5 3 50 9 0.0102 -39.8279

6 3 90 5 0.0138 -37.2024

7 5 30 9 0.0105 -39.5762

8 5 50 5 0.0140 -37.0774

9 5 90 7 0.0105 -39.5762

Fig. 8: Graph 1 Main Effect Plot for SN ratio (MRR)

Above graph is obtained from table 6. Hence from above figure it is clear that, best combination obtained for MRR if Current

of level-3, T ON of level-2, and T OFF of level-1.

Analysis Means of S/N Ratio for Material Removal Rate:

As the experimental design is orthogonal, so it is possible to separate out the effect of each process parameter at different levels. Table - 7

Means of S/N ratio for MRR

Level Current T ON T OFF

1 -40.9038 -39.5762 -37.952

2 -38.8688 -39.4389 -40.188

3 -38.7432 -39.5007 -40.3759

Delta 2.1606 0.1373 2.429

Rank 1 2 3

Total Mean of S/N ratio -38.7113 dB

From table 6 the Means of S/N ratio for MRR is calculated and the process parameters are obtained such Current of level-3, T

ON of level-2, and T OFF of level-1.

Anova for Material Removal Rate:

The purpose of the ANOVA is to find the statistical significance of process parameters on the response shown in table

according to F value T OFF is most significant effect on MRR

Table - 8

ANNOVA for Material Removal Rate

Source DF Adj. SS Adj.MS F-Value P- Value % Contribution

current 2 0.0000127 0.0000064 23.73 0.040 40.32

321

-38

-39

-40

-41

321

321

-38

-39

-40

-41

A

Me

an

of

SN

ra

tio

s

B

C

Main Effects P lot for S N ratios

Data Means

S igna l-to-noise: Larger is better



T -ON 2 0.0000003

0.0000001

0.51

0.660

0.96

T -OFF 2 0.0000185

0.0000092

34.47

0.028

58.74

Error 2 0.0000005 0.0000003 0.02

Total 8 0.0000315

Determination of Optimal Process Parameters for Surface Roughness (Ra):

In this process, S/N ratio for surface roughness is obtained based on response of surface roughness, ‘Lower is better’ criteria are

chosen for surface roughness. Results obtained are plotted in tabular form, which is given in table 9. Table - 9

Signal-to-Noise ratios for Surface Roughness

TRIAL A B C SURFACE ROUGHNESS IN µm S/N RATIO FOR SR(DB)

1 1 1 1 1 0

2 1 2 2 1.01 -0.0864

3 1 3 3 0.955 0.3999

4 2 1 2 1.025 -0.2144

5 2 2 3 0.995 0.0435

6 2 3 1 1.04 -0.3406

7 3 1 3 1.085 -0.7085

8 3 2 1 1.09 -0.7485

9 3 3 2 1.1 -0.8278

Analysis Means of S/N Ratio for Surface Roughness:

Similarly, the S/N ratio for surface roughness is calculated. Here, lower-is-better characteristics are used to find the optimal

process parameter for surface roughness (Ra). From the means of response of S/N ratio for Ra from table, the optimal process

parameters are obtained such as Current of level-1, T- ON of level-3 and T- OFF of level-3. Table - 10

Means of S/N ratio for SR

Level Current T ON T OFF

1 0.1333 -0.3076 -0.3630

2 -0.1703 -0.7914 -0.3762

3 -0.7016 -0.3013 -0.1335

Delta 0.8349 0.4901 0.2427

Rank 1 2 3

Total Mean of S/N ratio -0.1004 dB

Fig. 9: Graph 2 Main Effect Plot for SN ratio (SR)

From the above graph, it is clear that, Surface Roughness with lower value is better results for good combination. Hence, from

above figure we conclude that, Current of level-1, T -ON of level-3 and T- OFF of level-3 is best combination result.

321

0.0

-0.2

-0.4

-0.6

-0.8

321

321

0.0

-0.2

-0.4

-0.6

-0.8

A

Me

an

of

SN

ra

tio

s

B

C

Main Effects P lot for S N ratios

Data Means

S igna l-to-noise: S m alle r is better



ANOVA for Surface Roughness (Ra):

ANOVA for surface roughness (Ra) is listed in the table XII. From the table it is clearly found that Current with highest

contributing parameter for surface roughness. Therefore, Current is the most significant parameter for Ra followed by T -OFF

and T- ON. Table - 11

ANNOVA for Surface Roughness (Ra)

Source DF Adj. SS Adj.MS F-Value P- Value %Contribution

Current 2 0.0168167 0.0084083 23.47 0.041 88.59

T ON 2 0.0000500 0.0000250 0.07 0.935 0.26

T OFF 2 0.0021167 0.0010583 2.95 0.253 11.15

Error 2 0.0007167 0.0003583 0

Total 8 0.0189834

V. CONCLUSIONS

1) With the help of developed electrode the fabrication process of leaf shape micro channels is become very easy and time

consuming.

2) According to result obtained by parameter optimization of material removal rate is Current of level-3, T -ON of level-2, and

T -OFF of level-1, T -OFF is most significant effective parameter for MRR as compare to Current and T- ON.

3) According to responses of S/N ratio for Surface roughness Current of level-1, T -ON of level-3 and T-OFF of level-3.

Current is most significant effective parameter for surface roughness, followed by T-OFF and T-ON.

4) According to ANOVA table for MRR, T-OFF is most significant effective parameter for MRR.

5) According to ANOVA table for Surface roughness, Current is most significant effective parameter for surface roughness.

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Development of EDM Tool for Fabrication of Microchannel Heat … · Electrical discharge machining...

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