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IJISET - International Journal of Innovative Science, Engineering & Technology, Vol. 1 Issue 6, August 2014.

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ISSN 2348 – 7968

Optimization Of Process Parameters In Cold Chamber Die Casting Process Using Taguchi Method

1*Senthiil, P. V., 2M.Chinnapandian and 3Aakash Sirusshti

1*Head, Director Advance Manufacturing Technology, Mechanical Engineering, St.Peters University, Chennai-600054, Tamilnadu, India

2Head of Aeronautical Engineering, SPCET,Chennai, Tamilnadu, India 3Department of Mech, SRM University Vadapalani Campus, Chennai, Tamilnadu, India.

Abstract:

High Pressure die casting process is a

process ideally suited to manufacture mass

produced metallic parts of complex shapes

requiring precise dimensions. In this process,

molten metal is forced in to empty cavity of

desired shape and is allowed to solidify under

high holding pressure. Die casting is a complex

process which is controlled by many parameters

such as die related parameters, machine related

parameters, which has direct impact on casting

quality. Improper settings of these parameters

end up in producing quality related issues in the

casting. One such issue is blow holes and

porosity which is caused by improper setting of

process parameters. In this paper an industrial

component having blow hole problem has been

taken. This study proposes application of

Taguchi methodology in identifying the optimum

process parameters in order to improve the

casting quality. Process parameters such as

holding furnace temperature, slow shot, Fast

shot, Die holding time, Intensification pressure

has been selected for optimization. The response

factor (quality characteristic) chosen is density of

the casting. Density of the casting is chosen as a

quality characteristic since it has direct

relationship with casting defects. Generally

denser the casting lesser the internal defects such

as porosity, blow holes etc. Besides all this, final

results were validated by both experimental and

casting simulation. [9]

1. Introduction

In manufacturing processes, there are

various parameters with different adjustment

levels, which may influence the final

characteristics of the product. To optimize a

manufacturing process, the trial and error

method is used to identify the best parameters to

manufacture a quality product. However, this

method demands extensive experimental work

and results in a great waste of time and money.

Thus, optimization methods appear to be an

important tool for continuous and rapid

improvements. These experimental methods may

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be employed to solve problems related to a

manufacturing process, and understand the

influence of various factors on the final quality

of a given product.

The die casting process is controlled by

several parameters. When properly determined

and adjusted, they result in an improvement in

quality of the die casting parts. Usually, the main

controlled variables are metal temperature, slow

shot, and fast shot, intensification pressure, die

holding time, chemical composition of metal.

G.O. Verran et al. (2008) has analysed the effects

of slow shot, Fast shot, and Intensification

pressure on casting density. The results indicate

that the injection parameters affect the casting

density to a greater extent. Similarly V.D.

Tsoukalas (2008) has analysed effects of die

casting process parameters on porosity formation

using genetic algorithm. The results reveal that

under optimized conditions porosity formation is

very less. P. Vijian et al. (2006) has applied

Taguchi method for optimizing the parameters in

squeeze casting in order to minimize surface

roughness.

In this study optimization of die casting

parameters using Taguchi methodology was

carried out to solve the blow hole problem in an

aluminium component named ‘Front Wheel Hub

I’ . The material is AlSi9MnMg alloy. The

component has high rejection rate due to the

occurrence of blow hole problem. Especially the

critical area of the casting shown in Fig.1 has

more number of blow hole occurrence compared

to the other areas of the casting. { 4]

Figure.1 Blow holes in ‘Front Wheel Hub I casting’

2. Taguchi parameter design

Taguchi parameter design provides a

means of both reducing cost and improving

quality by making effective use of experimental

design methods. This involves the determination

of parameter values that are least sensitive to

noise .When the goal is to design a process or

product with high stability and reliability,

parameter design is the most important step in

which the functional non linearity is used to best

advantage. Initially parameters that are to be

optimized are chosen carefully. A cause and

effect diagram can be used for identifying the

parameters that affects the response. In this

study, casting density has been chosen as a

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response, since if the casting density is higher,

lower the internal defects such as blow holes and

porosity. A cause and effect diagram drawn for

blow holes in casting is shown in figure 2

Figure 2 Cause and effect diagram for blow holes

After analyzing the cause and effect diagram,

and checking the various parameters, die casting

machine parameters such as Metal temperature

(A), Slowshot(B), Fast shot(C),Intensification

pressure(D), Die holding time(E), have been

selected for the analysis of the blow hole

problem in casting. Interactions were also

considered between parameters Metal

temperature, slow shot, Fast Shot. The ranges for

the chosen factors were selected based upon

preliminary trials conducted during process

validation. The ranges for the selected factors are

shown in Table 1.

Table 1 Selected Factors and their ranges

The selection of orthogonal array

depends upon the degrees of freedom of the

factors and the three interactions that have been

considered. We get 10 degrees of freedom for

five factors at three level and 12 degrees of

freedom for three interactions. Thus smallest

orthogonal array that can be used must have at

least 22 degrees of freedom. Hence from the

standard orthogonal arrays we choose L27

orthogonal array which can accommodate 13

factors and has 26 degrees of freedom. A L27

Orthogonal array is shown below in table 2.

S.No Parameters Destination Parameters Range Level

1 Level

2 Level

3

1 A Metal

temperature (°C)

640-680 640 660 680

2 B Slow Shot (m/s)

0.12-0.22 0.12 0.17 0.22

3 C Fast shot (m/s)

1.92-2.42 1.92 2.17 2.42

4 D Die holding

time (seconds)

8-10 8 9 10

5 E Intensification

pressure (kg/cm2)

270-290 270 280 290

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Trail NoA B C D E F G H I J K L M

1 1 1 1 1 1 1 1 1 1 1 1 1 12 1 1 1 1 2 2 2 2 2 2 2 2 23 1 1 1 1 3 3 3 3 3 3 3 3 34 1 2 2 2 1 1 1 2 2 2 3 3 35 1 2 2 2 2 2 2 3 3 3 1 1 16 1 2 2 2 3 3 3 1 1 1 2 2 27 1 3 3 3 1 1 1 3 3 3 2 2 28 1 3 3 3 2 2 2 1 1 1 3 3 39 1 3 3 3 3 3 3 2 2 2 1 1 1

10 2 1 2 3 1 2 3 1 2 3 1 2 311 2 1 2 3 2 3 1 2 3 1 2 3 112 2 1 2 3 3 1 2 3 1 2 3 1 213 2 2 3 1 1 2 3 2 3 1 3 1 214 2 2 3 1 2 3 1 3 1 2 1 2 315 2 2 3 1 3 1 2 1 2 3 2 3 116 2 3 1 2 1 2 3 3 1 2 2 3 117 2 3 1 2 2 3 1 1 2 3 3 1 218 2 3 1 2 3 1 2 2 3 1 1 2 319 3 1 3 2 1 3 2 1 3 2 1 3 220 3 1 3 2 2 1 3 2 1 3 2 1 321 3 1 3 2 3 2 1 3 2 1 3 2 122 3 2 1 3 1 3 2 2 1 3 3 2 123 3 2 1 3 2 1 3 3 2 1 1 3 224 3 2 1 3 3 2 1 1 3 2 2 1 325 3 3 2 1 1 3 2 3 2 1 2 1 326 3 3 2 1 2 1 3 1 3 2 3 2 127 3 3 2 1 3 2 1 2 1 3 1 3 2

COLUMNS

Table 2 L27 Orthogonal array

3. Experimental Procedure

The experimental setup consists of die

casting cell comprising of 250T die casting

machine, an electric holding furnace, shot

monitoring system. The die casting machine is

semi-automated, having an auto ladler for

pouring the molten metal from the melting cum

holding furnace. The various settings required

for this experiment can be set manually and it is

monitored through the shot monitoring system.

The shot monitoring system helps us in

monitoring the parameters set so that we can

check its status during each shot. Aman

Agarwal et al. ( 2008). Aman AgarwalBefore

starting the experiment, the dies were preheated

to a temperature of 190°C using a gas burner.

The die temperature was measured by using

infrared gun. The molten metal was cleaned by

removing the slag to avoid impurities and

degassing was done to remove the hydrogen

content. The material composition was checked

by using spectro analysis before starting of the

experiment. Initially 50 shots were carried so that

machine gets stabilized to conduct the

experiment. A total of 27 different combinations

shown in Table 3 were tried and 3 parts were

cast for each combination, which totals to 81

castings. Each casting was given separate

identification mark to avoid mixing of the

castings. Finally after completing the

experiment, the castings were trimmed, fettled

and cleaned thoroughly. Der Ho et.al (2004)

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Trial.No Metal

temperature (A)

Slow shot (B)

Fast shot (C)

Die holding time (D)

Intensification pressure (E)

1 640 0.12 1.92 8 270

2 640 0.12 2.17 9 280

3 640 0.12 2.42 10 290

4 640 0.17 1.92 9 280

5 640 0.17 2.17 10 290

6 640 0.17 2.42 8 270

7 640 0.22 1.92 10 290

8 640 0.22 2.17 8 270

9 640 0.22 2.42 9 280

10 660 0.12 1.92 9 290

11 660 0.12 2.17 10 270

12 660 0.12 2.42 8 280

13 660 0.17 1.92 10 270

14 660 0.17 2.17 8 280

15 660 0.17 2.42 9 290

16 660 0.22 1.92 8 280

17 660 0.22 2.17 9 290

18 660 0.22 2.42 10 270

19 680 0.12 1.92 10 280

20 680 0.12 2.17 8 290

21 680 0.12 2.42 9 270

22 680 0.17 1.92 8 290

23 680 0.17 2.17 9 270

24 680 0.17 2.42 10 280

25 680 0.22 1.92 9 270

26 680 0.22 2.17 10 280

27 680 0.22 2.42 8 290

Table 3 Test Run design

The density of the castings were

measured based upon the Archimedes principle.

Before measuring the density of the casting, care

was taken that casting is completely free from

the burr or any dust in order to avoid errors in the

measurement. Belavendram(10). Belavendram

The castings were first weighed in air and then

weighed when completely immersed in water.

All the weighings were done by using electronic

weighing machine having accuracy up to 100

mg. Ko Ta Chiang et al.(2009). The density

measurement of each casting was measured

thrice in order to avoid any errors. The density of

the casting were calculated based upon the

formula

ρ= {w1/ (w1-w2)} x ρwater

Where ρ = density of the casting (gm/cm3)

w1= weight of the casting in air (grams)

w2= weight of the casting in water (grams)

ρwater = density of water (1 gm/cm3)

4. Experimental results

The density of the casting was measured

by Archimedes principle and the results are

shown below. Minitab 14 software has been used

to carry out Taguchi optimization .The response

value is taken as average density of the three

sample castings that were cast under each trail

condition. Then S/N ratio were computed based

upon the formula S/N ratio for larger the better

characteristics = -10 log (1/r Σ (1/yi2)) where r

-number of observations, yi -Response value for

each trail. The experimental results are shown in

table 4.

Density (g/cm3) Average density

S/N Ratio Sample

1 Sample

2 Sample

3 2.578 2.583 2.584 2.581 8.236 2.581 2.587 2.587 2.585 8.249 2.591 2.592 2.592 2.592 8.273 2.584 2.589 2.590 2.587 8.256 2.588 2.592 2.594 2.592 8.273 2.578 2.582 2.584 2.581 8.236 2.588 2.593 2.595 2.592 8.273 2.577 2.584 2.584 2.582 8.239

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2.582 2.588 2.589 2.586 8.253 2.587 2.592 2.594 2.591 8.269 2.584 2.582 2.586 2.584 8.246 2.581 2.586 2.587 2.585 8.249 2.582 2.587 2.588 2.586 8.253 2.582 2.588 2.589 2.586 8.253 2.585 2.592 2.593 2.590 8.266 2.583 2.589 2.590 2.587 8.256 2.588 2.589 2.594 2.590 8.266 2.574 2.588 2.589 2.584 8.246 2.586 2.591 2.592 2.590 8.266 2.585 2.591 2.592 2.590 8.266 2.579 2.585 2.585 2.583 8.242 2.588 2.593 2.597 2.592 8.273 2.582 2.586 2.588 2.585 8.249 2.585 2.590 2.592 2.589 8.263 2.582 2.588 2.589 2.586 8.253 2.584 2.590 2.591 2.588 8.259 2.586 2.591 2.593 2.590 8.266

Average=2.587 Average=8.256

Table 4 Experimental results

The response table and graphs for mean (casting

density) are shown in table 5 and figure 3.

Table 5 Response table for Means

Figure 3 Response Graphs for Means (casting density)

The response table and graphs for S/N ratios are

shown in table 6 and figure 4 Levels

Metal temperature

(A)

Slow Shot (B)

Fast shot (C)



1 2.586 2.5867 2.588 2.586 2.584

2 2.587 2.5875

2.5868 2.587 2.587

3 2.588 2.5872

2.5866 2.589 2.591

Max-Min 0.002 0.000

8 0.001

4 0.003 0.007

Rank 3 5 4 2 1

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Levels Metal

temperature (A)

Slow Shot (B)

Fast shot (C)



1 8.254 8.255 8.25

9 8.253 8.244

2 8.256 8.258 8.25

6 8.256 8.256

3 8.26 8.257 8.25

5 8.261 8.269 Max-Min 0.006 0.003

0 0.00

4 0.008 0.025

Rank 3 5 4 2 1

Table 6 Response table for S/N ratio

Figure 4 Response graphs for S/N Ratios

From the response graphs the optimized results

are Metal temperature -680°C, Slow shot-0.17

m/s, Fast Shot-1.92 m/s, Die holding time-10

seconds, Intensification pressure-290 kgf/cm2

4.1 ANOVA table for Means and S/N Ratios

In order to determine which parameter

significantly affect the quality characteristic

(casting density), ANOVA is done. S.Guharaja

(2009). Ftab-value was taken at 95% confidence

interval. The ANOVA table for means and S/N

ratios are shown in table 7 and 8.

Table 7 ANOVA table for Means

SOURCE DF SS MS F-CAL F-tab P%

A 2.000 0.000013 0.0000065 13 6.94 4.17

B 2.000 0.000003 0.0000015 3 6.94 0.96

C 2.000 0.000009 0.0000045 9 6.94 2.88

D 2.000 0.000030 0.000015 30 6.94 9.62

E 2.000 0.000250 0.000125 250 6.94 80.13

AXB 4.000 0.000000 0 0 6.39 0.00

AXC 4.000 0.000003 7.5E-07 1.5 6.39 0.96

BXC 4.000 0.000002 0.0000005 1 6.39 0.64

ERROR 4.000 0.000002 0.0000005 0.64

TOTAL 26.000 0.000312 0.000012 100.00

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SOURCE DF SS MS F-CAL F-tab P%

A 2 14E-03 73E-04 10.81 6.94 4.150

B 2 31E-04 155E-04 2.30 6.94 0.881

C 2 104E-03 52E-04 7.70 6.94 2.956

D 2 337E-03 1685E-03 24.96 6.94 9.579

E 2 281E-02 0.001408 208.59 6.94 80.045

AXB 4 4E-05 0.000001 0.15 6.39 0.114

AXC 4 29E-04 7.25E-06 1.07 6.39 0.824

BXC 4 24E-04 0.000006 0.89 6.39 0.682

ERROR 4 27E-04 6.75E-06 0.767

TOTAL 26 3518E-02

0.0001353 100.000

Table 8 ANOVA table for S/N Ratio

4.2 Prediction of Optimum response

In establishing to what extent the data of

the experiment is successful, it is necessary to

predict the mean at the optimum condition and

then compare them against a confirmation

experiment. For mean response the overall

average of casting density (T) is 2.587. The

predicted optimal response (µ) is:

µpredicted = Estimate of the process mean at

optimum condition

= T+(A3-T)+(C1-T)+(D3-T)+(E3-T)

= 2.595

Estimation of confidence interval (means) for

confirmation experiment is :

C.I= (F(α,1,ve)Ve(1/ηeff +1/r))1/2

where α is the level of risk, ve is the degrees of

freedom for error, ηeff is the effective number of

replications is the number trials taken for

confirmation test, Ve is the error variance

ηeff = (Total number of experiments/sum of dof

used in estimate of mean)

UsingthevaluesF-

ratio(0.05,1,4)=7.71,Ve=0.0000005, ve=4,r=3

Therefore C.I = {7.71*0.0000005*((1/9)+(1/3))}

= ±0.001308

The 95% confidence interval of the predicted

optimum of the casting density is 2.593<µ<2.596

5. Confirmation test

Finally confirmation test were carried out

at optimized conditions. The average casting

density (2.593g/cm3) of three confirmation test

samples lies well within the confidence limit.

Philip J. Ross(11). The Radiography test was

carried out on the confirmation test samples and

the test results revealed no significant defects

were found. The Radiography images of

defective sample and confirmation test sample

are shown in Figure 5.

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Defective sample

Confirmation test sample

Figure 5 Radiography images

Similarly the confirmation test samples were

machined in order to evaluate the critical area.

After machining the critical area were free from

blow holes. The images of critical area machined

are shown in figure 6

Defective sample Confirmation test sample

Figure 6 Images of critical area machined

6. Regression Analysis

The Multi variable regression equation (MVLR)

developed is

Density=2.45+0.000042*A+0.00444*B-

0.002667*C+0.0012778*D+0.000372*E

Where A is Metal Temperature, B is slow shot,

C is Fast shot, D is Die holding time, E is

Intensification pressure

R2 = 0.962 for the MVLR equation developed

for density. Similarly the Fcal value (105.88) for

the regression model is greater than Ftab(2.68) at

95% of confidence limit.

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Figure 7 Predicted Vs Actual density values

7. Numerical simulation

The numerical simulation was also done

at the optimized conditions in order to predict

any defects were arising in the casting. The

casting simulation was done by using Z-cast v2.6

trial version. The simulation results consist of

Flow analysis, Solidification analysis, and Defect

analysis. The simulation results are shown

below.

7.1 Flow analysis

Figure 8 Flow analysis

7.2 Solidification analysis

Figure 9 Solidification analysis

7.3 Defect analysis

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Figure 10 Defect analysis

The simulation results also reveal that no defects

were found in the casting. In the defect analysis

simulation defect was found only in the over

flow connector area. The casting simulation

results coincide well with the experimental

results.

8. Results and discussion

The experiments have been conducted and the

results are summarized as follows:

1) The optimized parameters of this experiment

are

• Metal temperature-680ºC

• Slow shot-0.17 m/s

• Fast shot-1.92 m/s

• Die holding time -10 seconds

• Intensification pressure- 290 kg/cm2

2) The parameters considered contribute

variation towards casting density. The percentage

of contribution of each factor was calculated by

using ANOVA method. They contribution of

each factor towards casting density and S/N ratio

are shown in the below graph.

Figure 11 Percentage contributions of control factors

3) The radiography images show that castings

that were taken under the optimized conditions

show no defect when compared to the castings

that was taken during non-optimized conditions.

4) Similarly the confirmation test samples were

machined and the critical area of the casting was

found to be free of blow holes.

5) Simulation results also reveals that under

optimized conditions no defect (blow holes) was

found in the casting

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10. Conclusion

The optimization procedure has been

made to study the effect of die casting process

parameters on casting density. Generally when

the casting density is higher, internal defects

such as blow holes and porosity is eliminated.

So, the basic idea is to provide a decision tool for

setting optimum parameters so that the defects

occurring in the casting is reduced.

Taguchi method was applied for

optimizing the die casting process parameters,

and the results obtained using this method was

useful in eliminating the blow holes problem in

Front Wheel Hub I casting. At optimized

parameters the casting quality was better than the

previous casting that was cast under the non

optimized conditions. The radiography test

results also revealed that samples cast under the

optimized parameters has no significant defects.

Similarly the results obtained using simulation

software Z-cast V2.6 (Trial version) at optimized

condition shows no presence of defects in

casting. Considering the contribution of the

parameters, intensification pressure was the

factor showing more influence on the casting

density compared to the other parameters.

The outcome of this project work was

very useful for finding solution for casting

defects that occurs due to the incorrect process

parameters in die casting. Also these results will

help in improving the productivity of Front

Wheel Hub I castings at Roots Cast Private

Limited in future. Thus combination of

optimization techniques along with casting

simulation serves as a tool for improving the

productivity of the castings to a greater extent, in

die casting industries.

References

[1].DOE applied to optimization of aluminum alloy die castings by G.O. Verran, R.P.K. Mendes, L.V.O. Dalla Valentina, , Journal of Materials Processing Technology, May 2008

[2].Optimization of porosity formation in AlSi9Cu3 pressure die castings using genetic algorithm analysis by V.D Tsoukalas , Materials & Design, December 2008

[3].Use of Taguchi method to develop robust design for the magnesium alloy die casting process by Der Ho Wu, Mao Sheng Chang , Materials Science and Engineering, August 2004

[4].Modelling and analysis of the effects of processing parameters on the performance characteristics in the high pressure die casting process of Al–SI alloys by Ko-Ta Chiang & Nun-Ming Liu & Te-Chang Tsai, International Journal of Advanced Manufacturing Technology (2009)

[5].Optimization of squeeze cast parameters of LM6 aluminium alloy for surface roughness using Taguchi method by P. Vijian, V.P.

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Arunachalam,Journal of Materials Processing Technology, December 2006

[6].Optimization of Green sand casting process parameters by using Taguchi’s method by S.Guharaja, A.Noorul Haq, K.M.Karuppannan, International Journal of Advanced Manufacturing Technology (2009)

[7].Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi’s technique- by Aman Agarwal,Hari Singh, Pradeep Kumar, Manmohan singh , Journal of materials processing technology ( 2008)

[8].Optimization of high pressure die-casting process parameters using artificial neural network by by Jiang Zheng, Qudong Wang,Peng Zhao, Congbo Wu, International Journal of Advanced Manufacturing Technology (2009)

[9].Die Casting Engineering by Bill Anderson

[10].Quality by design by Nicole Belavendram, He did two Bachelor degrees in B. Sc Physics at Ayya Nadar Janaki Ammal College, M.K. University, Madurai, and B. Tech Automobile Engineering at Madras Institute of Technology (M.I.T) Chrompet Campus, Anna University Chennai, Master degree in M.E Energy Engineering at the College of Engineering Guindy, Anna University Chennai. He received his Ph. D Mechanical Engineering from College of Engineering Guindy, Anna University Chennai in recognizing his significant contribution in the area of Cascaded Latent Heat Storage System for Diesel Engine Waste Heat RecoveryPrentice Hall Publications

[11].Taguchi techniques for Quality engineering, by Philip J. Ross, McGraw Hill Publications. BioGraphy:

Dr. P.V Senthiil is currently working as Director(AMT)Prof & Head, Mechanical Engg at St Peter's University, Chennai, India. He lectures modules in CNC machines, Rapid Prototyping and AI & Robotics. He is also guiding 10 researchers in different disciplines. His work experience as a Factory consultant / knowledge transfer partnership associate working for electronic goods consumer industry in south Africa, France, UK. He was involved in developing, implementing & documenting operating procedures in advanced applications CNC industries for inventory reduction activities within various sectors of the company. Prior to start of his research, Dr Senthiil was working as a Professor at Coimbatore Institute of Technology between May 1986 to Jun 2011. He did his masters in Mechanical Engineering at PSG Tech, April 1985, Bachelor’s at PSG Tech, in Mechanical Engineering, April 1983. He is a World Science Academy Council Member,Visiting Professor – Nottinghamshire University, Malaysia and Visiting Professor of Caledonian University, MUSCAT/AIT Bhagdad Dr M.Chinnapandian is currently working as Prof & Head, Aeronautical Engg at St Peter's College of Engg Tech, Chennai, India. He did two Bachelor degrees in B. Sc Physics at Ayya Nadar Janaki Ammal College, M.K. University, Madurai, and B. Tech Automobile Engineering at Madras Institute of Technology (M.I.T) Chrompet Campus, Anna University Chennai, Master degree in M.E Energy Engineering at the College of Engineering Guindy, Anna University Chennai. He received his Ph. D Mechanical Engineering from College of Engineering Guindy, Anna University Chennai in recognizing his significant contribution in the area of Cascaded Latent Heat Storage System for Diesel Engine Waste Heat Recovery.

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