OPTIMIZATIONS OF MACHINING PARAMETER IN WIRE EDM FOR 316L STAINLESS STEEL BY USING TAGUCHI METHOD,...

http://www.iaeme.com/IJMET/index.asp 307 [email protected]

International Journal of Mechanical Engineering and Technology (IJMET)

Volume 7, Issue 2, March-April 2016, pp. 307–320, Article ID: IJMET_07_02_033

Available online at

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=7&IType=2

Journal Impact Factor (2016): 9.2286 (Calculated by GISI) www.jifactor.com

ISSN Print: 0976-6340 and ISSN Online: 0976-6359

© IAEME Publication

OPTIMIZATIONS OF MACHINING

PARAMETER IN WIRE EDM FOR 316L

STAINLESS STEEL BY USING TAGUCHI

METHOD, ANOVA, AND GREY ANALYSIS

Manish Saini, Rahul Sharma, Abhinav, Gurupreet Singh, Prabhat Mangla

Student, Mechanical Department GNI-Mullana,

Kurukshetra University, Kurukshetra, Haryana, India

Er. Amit Sethi

Asist. Professor, Mechanical Deptt, GNI–Mullana

Kurukshetra University, Kurukshetra, Haryana, India

ABSTRACT

WEDM is one of the non-traditional method used for the machining

complex shape structure and components made up of hard material like

composites and HSS. This is an experimental investigation of wire electro-

discharge machining (WEDM) of 316L SS. The outstanding characteristics of

stainless steel 316L such as their compatibility and noticeable physical,

mechanical and biological performance has led to increased application of

them in various industries especially in biomedical industries over the last 50

years.316L SS is used extensively for weldments where its immunity to carbide

precipitation due to welding assures optimal corrosion resistance. There are

some difficulties in machining of stainless steel by conventional machining.

On the other hand, unconventional machining process especially Wire

electrical discharge machining (WEDM) are more appropriate techniques for

machining difficult to machine materials such as stainless steel. Electrical

conductive materials are cut by wire EDM that uses a wire as electrode in an

electro-thermal mechanism. The machines also specialize in cutting complex

contours or fragile geometries that would be difficult to be produced using

conventional cutting methods. The focus of this paper is on machining of

stainless steel with WEDM because of the above mentioned features of WEDM

and its suitability for machining stainless steel. In this study the effect of nine

parameters including five controlled such as servo voltage(SV), peak

current(Ip), pulse-on time(Ton), pulse-off time (Toff), wire feed (WF) and four

remains fixed such as water pressure (WP), wire tension(WT), servo

feed(SF),voltage potential (VP) on process performance parameters such as

cutting speed and surface roughness are investigated. A Taguchi L16 design of

Manish Saini, Rahul sharma, Abhinav, Gurupreet Singh, Prabhat Mangla and Er.

Amit Sethi


experiment (DOE) is applied to determine the effect of significant parameters

on WEDM performance. The optimal parameters were obtained as (A1 B4 C4

D1 E2) for cutting rate and (A4 B1 C1 D4 E2) for surface roughness.

Optimum predicted values for cutting rate and surface roughness are

2.2009mm/min and = 0.5598 µm respectively. By using ANOVA three

parameters namely pulse-on time, pulse-off time and servo voltage were found

the most significant affecting the cutting rate and surface roughness under

99% confidence level. By grey analysis, the optimum machining parameters

setting can be obtained for considering maximum cutting speed and minimum

surface roughness simultaneously. Thus the optimal set of process parameters

is (A2 B2 C4 D4 E2).

Key words: 316l Ss, Biomaterials, Cutting Speed, Surface Roughness,

Taguchi, Anova, Grey Analysis

Cite this Article Manish Saini, Rahul sharma, Abhinav, Gurupreet Singh,

Prabhat Mangla and Er. Amit Sethi. Optimizations of Machining Parameter In

Wire EDM For 316l Stainless Steel by Using Taguchi Method, Anova, and

Grey Analysis. International Journal of Mechanical Engineering and

Technology, 7(2), 2016, pp. 307–320.

http://www.iaeme.com/currentissue.asp?JType=IJMET&VType=7&IType=2

1. INTRODUCTION

Wire electrical discharge machining (WEDM) is an important technology, which

demands high-speed cutting and high-precision machining to realize productivity and

improved accuracy. Wire electrical discharge machining (WEDM) is an indispensable

machining technique for producing complicated cut outs through difficult to machine

metals without using high cost grinding or expensive formed tools [1]. WEDM is an

extremely potential electro thermal process for hard metal alloy to get the high

precision. When the servo voltage is increased, there will be spark produced and

temperature will be high of 10,000 of degrees. Due to this high temperature metal will

be removed from the work piece. Material is eroded from the work piece (Anode) and

wire tool electrode (Cathode) separated by deionised water such as dielectric fluid and

continuously flushes away the machining debris. The movement of the wire is

controlled by CNC technology. WEDM has greatly altered the tooling and

manufacturing industry, resulting in dramatic improvements in accuracy, quality

productivity and profit. Over the years, WEDM process has remained as a competitive

economical machining option fulfilling the demanding matching requirements

imposed by the short product development cycle and the growing cost pressure.

Stainless steels have been widely used in various industries because of their good

corrosion resistance and mechanical properties. Among of them, austenitic stainless

steel 304 is the commonest type of stainless steels. However, due to the low hardness,

poor wear resistance of stainless steel, sensitive to pitting corrosion and stress

corrosion cracking in chloride solution, [2−4] the strength of the stainless steel can be

reduced, which limits its application in industrial production. Therefore, how to

improve the corrosion resistance of stainless steel in chloride ion solution and the

wear resistance of stainless steel has been a problem. In this case, it came into being

and developed rapidly that the ceramic films were used to coat on the stainless steel

surface [5−7]. Stainless steel materials are widely used for multiple applications

because of their good mechanical properties and very good corrosion resistance in a

number of environments [8]. stainless steel 316L is having a good resistance to creep

http://www.iaeme.com/currentissue.asp?JType=IJMET&VType=7&IType=2

Optimizations of Machining Parameter In Wire EDM For 316l Stainless Steel by Using

Taguchi Method, Anova, and Grey Analysis


and fatigue, excellent corrosion resistance and biocompatibility, excellent weldability

and easier to machine therefore 316L stainless steel is having more applications like

oil & petroleum , refining equipment, food processing equipment, pharmaceutical

processing equipment, architectural, biomedical .stainless steel constitute prominent

class of valuable iron alloys. They are used in variety of applications when enhanced

properties like corrosion and oxidation resistance. Coupled to good mechanical

characteristics are required. The stainless steel grades currently manufactured by

sintering correspond generally to the grade manufactured with other technology.316L

austenitic stainless steel is now a day’s widely used engineering material due to its

excellent oxidation resistance and good formability. On refining grains of 316L

stainless steel several technique have been used in which coarse grains are refined via

plastic deformation or subsequent re-crystallization mechanical milling, cold rolling,

severe plastic deformation. 316L were mechanically milled and sintered at 1173k.

316L tends to work harden if machined too quickly for this reason low speed and

constant feed rates are recommended. Additionally, WEDM is able to cut metals as

thin as 0.004”.the wire of WEDM unit emits sparks on all sides, which means the cut

must be thicker than the wire itself. Wire electrode is generally made of copper, brass

or tungsten of diameter 0.05mm to 0.3mm, which is capable to achieve very small

corner radii. Thus WEDM has evolved from a simple means of making tools and dies

to the best alternatives of producing micro-scale parts with the highest degree of

dimensional accuracy and surface finish quality [9-11].

Figure 1 Schematic diagram of working of Wire-EDM

2. MATERIAL SELECTION

Selection of material depends upon the desire weld ability qualities which must rely

on basic properties of the material, such as strength, corrosion or erosion resistance,

ductility, and toughness. The properties of the various metallurgical characteristics

associated with the thermal cycles encountered in the welding operation must also be

included in the design process. The specimens of 20mm x 10mm x10mm are

prepared. The Stainless steel 316 L alloy was been used in this study. Chemical

composition of work piece is very much essential for selecting the type of process and


Amit Sethi


their controllable variables. Table 1 indicates the chemical composition of Stainless

steel 316L.

Table 1 Chemical composition of Stainless steel 316L. (Wt.%)

C

%

Mn

%

P

%

S

%

Si

%

Cu

%

Ni

%

Cr

%

V

%

Mo

%

Fe

%

0.0306 1.191 0.0305 0.0026 0.2062 0.1041 10.34 16.73 0.0433 2.015 Balance

3. METHOD OF EXPERIMENT

Taguchi method

Taguchi method is a powerful tool for the design of high quality systems. It provides

simple, efficient and systematic approach to optimize designs for performance, quality

and cost. Optimization of process parameters is the key step in Taguchi method to

achieving high quality without increasing cost. This is because optimization of

process parameters can improve quality characteristics and optimal process

parameters obtained from Taguchi method are insensitive to the variation of

environmental conditions and other noise factors. Classical process parameters design

is complex and not an easy task. To solve this task the Taguchi method uses a special

design of orthogonal arrays to study the entire process parameter space with a small

number of experiments only.

Anova

The analysis of variance (ANOVA) of raw data and S/N data were performed to

determine the significant and insignificant variables and to show their effects on the

response characteristic. Then, the response curves (main effect) were plotted for raw

data and S/N data in order to examine the parametric effects on the response

characteristics. Finally, the optimal values of significant process parameters in terms

of mean response characteristics are defined based on analyzing the ANOVA table

and response curves.

Grey relational analysis (GRA)

The Grey Theory was introduced by Dr. Deng J.L. (1982) which includes Grey

relational analysis, Grey modeling prediction and decision making of a systems

including incomplete information, multi-input and discrete and poor data information

where as partial information is to be known and partial information is unknown. A

grey relational grade is obtained to evaluate the multiple performance characteristics.

In GRA optimization of complicated multiple performance characteristics can be

converted into optimization of a single grey relational grade.

Experiment setup

The mechanism of metal removal in wire electrical discharge machining mainly

involves the removal of material due to melting and vaporization caused by the

electric spark discharge generated by a pulsating direct current power supply between

the electrodes. In this mechanism, negative electrode is a continuously moving wire

and the positive electrode is the work piece. The sparks will generate between two

closely spaced electrodes. Different numbers of experiments were performed to study




the effects of the various machining parameters of wire electric discharge machining.

The studies have been undertaken to investigate the effect of peak current (Ip),wire

feed (Wf), servo voltage (SV), pulse on time (Ton) and pulse off time (Toff).The

material of the wire is zinc coated brass wire having a diameter of 0.25mm .For the

calculation of the cutting speed (CS) and surface roughness(SR) we cut the small

pieces of the material 316L stainless steel of dimension 20mm x 10mm x10mm to

measure and surface roughness with surface roughness tester SRT-8210 and wire

EDM machine calculates the cutting speed, and the value is displayed on the output

screen of the CNC interface.

Table 2 Process parameters and their levels

Factor Process Parameters Level 1 Level 2 Level 3 Level 4

A Servo voltage (SV) 20 30 40 50

B Peak current (Ip) 90 110 130 150

C Pulse-on time (Ton) 106 110 114 118

D Pulse-off time (Toff) 30 35 40 45

E Wire feed (Wf) 3 5 - -

The WEDM experiments were performed in order to study the effect of process

parameters on the output response characteristics such as cutting speed and surface

roughness with the help of TAGUCHI method.

4. RESULT AND DISCUSSIONS

Results by Taguchi

The WEDM experiments and using the parametric approach of the Taguchi’s method

were conducted in this study. In this section, the influence of the various process

parameters on the cutting speed and surface roughness for different experimental

conditions is discussed.

Cutting Speed (CS)

The wire electric discharge machining calculates the cutting speed, and the value is

displayed on the output screen of the CNC interface. For wire electric discharge

machining, cutting speed is a desirable characteristic and it should be as high as

possible to give least machine cycle time leading to increased productivity. In the

present study cutting rate is a measure of job cutting which is digitally displayed on

the screen of the machine and is given quantitatively in mm/min.

Table 3 Response table for cutting rate (Mean data)

Level SV Ip Ton Toff WF

1 1.7913 1.4015 0.8268 1.8111 1.5340

2 1.6009 1.6194 1.4017 1.6887 1.6171

3 1.5109 1.5266 1.8727 1.5313

4 1.3991 1.7546 2.2009 1.2711

Delta 0.3922 0.3531 1.3741 0.5400 0.0831

Rank 3 4 1 2 5


Amit Sethi


For example, the average effect on cutting speed (Mean data) for parameters Wf

and Ip at level 1 can be calculated as follows:

Wf = (1.1368+1.6586+2.297+2.223+1.2137+2.1686+0.9046+0.6694)/8= 1.53387

Ip= (1.1368+1.2495+1.2137+2.0061)/4= 1.40075

Table 4 Response table for cutting rate (S/N Data)

Level SV Ip Ton Toff Wf

1 4.762 2.694 -1.892 4.876 2.965

2 3.035 3.313 2.634 4.036 3.436

3 3.061 2.824 5.224 2.839

4 1.945 3.971 6.837 1.052

Delta 2.816 1.276 8.729 3.824 0.471

Rank 3 4 1 2 5

For example, the average effect on cutting speed (S/N data) for parameters Ip and

Ton at level 1 can be calculated as follows:

Ip= (1.11368+1.93473+1.68223+6.04705)/4= 2.6944

Ton = (1.11368-3.9582-1.23862-3.48629)/4= -1.8923

50403020

2.4

2.0

1.6

1.2

0.8

15013011090 118114110106

45403530

2.4

2.0

1.6

1.2

0.8

53

SV

Me

an

of

Me

an

s

IP TON

TOFF WF

Main Effects Plot for MeansData Means

Figure 2 Effects of response of process parameters on cutting rate (Raw data)




50403020

7.5

5.0

2.5

0.0

15013011090 118114110106

45403530

7.5

5.0

2.5

0.0

53

SV

Me

an

of

SN

ra

tio

s

IP TON

TOFF WF

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Larger is better

Figure 3 Effects of response of process parameters on cutting rate (S/N data)

From figure 2 and 3, it is clear that the cutting rate increases with the increase of

pulse on time, peak current and wire feed, and decreases with increase in pulse off

time and servo voltage. This is because the discharge energy increases with the pulse

on time and peak current which result in faster cutting rate. As the pulse off time

decreases, the number of discharges within a given period becomes more which leads

to a higher cutting rate. With increase in servo voltage the average discharge gap gets

widened resulting into a lower cutting rate. The effect of wire feed on cutting rate is

not very significant.

Selection of optimum process parameters have been made from the response table.

Here response table is used to calculate the effect of each level of process parameter

on performance measure. The response tables 3 and 4 show the average of each

response characteristic (Raw data, S/N data) for each level of each factor. As cutting

rate is the “higher the better” type quality characteristic, it can be seen from Figure 2

that the first level of wire servo voltage (A1), fourth level of peak current (B4), fourth

level of pulse on time (C4), first level of pulse off time (D1) and second level of wire

feed (E2) provide maximum value of cutting rate. The S/N data analysis (Figure 3)

also suggests the same levels of the variables (A1 B4 C4 D1 E2) as the best levels for

maximum CS in WEDM process.

Larger the better:

)(MSDlog10N

SHB

HB

(1)

where

R

1j)2

j(1/y

R

1MSDHB


Amit Sethi


Surface roughness (SR)

One of a good predictor of Wire EDM performance is surface roughness because

nucleation sites can be formed for cracks or corrosion by irregularity in the surface. It

is quantified by the vertical deviations of a real surface from its ideal form. If these deviations are

large, the surface is rough; if small, the surface is smooth. In this work the surface

roughness was measured by Mitutoyo Surftest SRT-8210. In order to see the effect of

process parameters on surface roughness, the average values of surface roughness for

each parameter at levels 1, 2, 3 and 4 for raw data and S/N data are tabulated in table

5 and 6 respectively.

Table 5 Response table for mean surface roughness (Mean data)


1 3.304 2.187 1.770 2.841 2.701

2 2.761 2.429 2.353 2.842 2.543

3 2.248 2.785 2.921 2.514

4 2.177 3.090 3.446 2.293

Delta 1.127 0.903 1.675 0.548 0.158

Rank 2 3 1 4 5

Table 6 Response table for surface roughness (S/N data)


1 -10.168 -6.692 -4.806 -8.810 -8.204

2 -7.892 -7.266 -7.164 -8.706 -7.554

3 -6.848 -8.199 -8.979 -7.645

4 -6.608 -9.359 -10.567 -6.355

Delta 3.560 2.668 5.761 2.455` 0.650

Rank 2 3 1 4 5

Method for calculating the response for surface roughness is same as that of the

response for cutting rate. And the same average values for raw data and S/N data are

plotted in figure 4 and 5 respectively. It is clear that the surface roughness increases

with the increase of pulse on time, peak current and servo voltage, and decreases with

increase in pulse off time. There is no significant change in the surface roughness

with the increase of wire feed. The discharge energy increases with the pulse on time

and peak current and larger discharge energy produces a larger crater, causing a larger

surface roughness value on the work piece.




50403020

3.6

3.2

2.8

2.4

2.0

15013011090 118114110106

45403530

3.6

3.2

2.8

2.4

2.0

53

SVM

ean

of M

eans

IP TON

TOFF WF


Figure 4 Effects of response of process parameters on surface roughness (Raw data)

50403020

-4

-6

-8

-10

15013011090 118114110106

45403530

-4

-6

-8

-10

53

SV

Mea

n of

SN

rati

os

IP TON

TOFF WF

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Smaller is better

Figure 5 Effects of response of process parameters on surface roughness (S/N data)

As the pulse off time decreases, the number of discharges increases which causes

poor surface accuracy. The response tables 5 and 6 show the average of each response

characteristic (raw data, S/N data) for each level of each factor. As cutting rate is the

“lower the better” type quality characteristic, it can be seen from Figure 4 that the

fourth level of servo voltage (A4),first level of peak current (B1), first level of pulse

on time (C1), fourth level of pulse off time (D4) and second level of wire feed (E2)

provide maximum value of surface roughness. The S/N data analysis Figure 5 also

suggests the same levels of the variables (A4, B1, C1, D4 and E2) as the best levels

for minimum SR in wire electric discharge machining process. Lower the better:

)(MSDlog10N

SLB

LB

(2)

Where,

R

1j

2

jLB )(yR

1MSD


Amit Sethi


Result by Anova

In order to predict the optimal values of the machining characteristics, only significant

parameters are considered, and those effect is great on the machining characteristics.

These significant parameters were found using Analysis of Variance (ANOVA) on

S/N data of machining characteristics. Analysis of variance (ANOVA) is a common

statistical technique to determine the percent contribution of each factor for results of

experiments. It calculates parameters known as sum of square (SS), pure SS, variance,

degree of freedom (DOF) and F-ratio. Since the procedure of ANOVA is a very

complicated and employs a considerable of statistical formulae. Results of the

ANOVA are given in the tables 7 and 8 for cutting rate and surface roughness

respectively.

Table 7 Analysis of variance for cutting rate (S/N data)

Analysis of Variance for CS, using Adjusted SS for Tests

Source DF Seq SS Adj SS Adj MS F P

SV 3 0.33001 0.33001 0.011000 4.98 0.172

Ip 3 0.2664 0.2664 0.08888 4.03 0.205

Ton 3 4.28091 4.28091 1.42697 64.63 0.015

Toff 3 0.65170 0.65170 0.21723 9.84 0.094

Wf 1 0.02766 0.02766 0.02766 1.25 0.379

Error 2 0.04416 0.04416 0.02208

Total 15 5.60107

S = 0.148585 R-Sq = 99.21% R-Sq (adj.) = 94.09%

Table 8 Analysis of variance for surface roughness (S/N data)

Source DF Seq SS Adj SS Adj MS F P

SV 3 3.29201 3.29201 1.09734 69.50 0.014

Ip 3 1.88929 1.88929 0.62976 39.89 0.025

Ton 3 6.26404 6.26404 2.08801 132.24 0.008

Toff 3 0.86200 0.86200 0.28733 18.20 0.053

Wf 1 0.09994 0.09994 0.09994 6.33 0.128

Error 2 0.03158 0.03158 0.01579

Total 15 12.43885

S = 0.125656 R-Sq = 99.75% R-Sq (adj) = 98.10%

Optimal value for CS

Form the tables 7, it is clear that three process parameter namely pulse on time (C)

and pulse off time (D) are the most significant process parameters affecting the

cutting rate. Wire feed and peak current shows the least contribution. The optimal

value is predicted using the Eq. (3); the optimum value is calculated as follow,




= T i (3)

Here, significant parameters are three in number. So above equation becomes

= T (C )

= 1.575538+ (2.2009-1.575538)

= 2.2009 mm/min

Prediction of optimal value for SR

Form the tables 8, it is clear that three process parameter namely peak current (B),

pulse on time (C), pulse off time (D) are the most significant process parameters

affecting the surface roughness. Wire feed shows the least contribution. The optimum

value is calculated as the similar way as in case of CR. So the optimum value is,

= T i (3)

= T (A4 T) (B1 ) (C1 ) (D4 )

=2.6224 (2.177 2.6224) (2.187 2.6224) (1.770 2.6224) (2.293 2.6224)

= 0.5598 µm

Results by Grey relational analysis (GRA)

In GRA, optimization of complicated multiple performance characteristics can be

converted into optimization of a single gray relational grade.

Steps in GRA

GRA consists of three steps:

1. Data Pre-processing (Normalization).

2. Calculating the grey relational coefficients.

3. Calculating the grey relational grade

Step 1:

First step is associated with the normalization of results. When the range of the series

is too large or the optimal value of a quality characteristic is too enormous, it will

causes the influence of some factors to be ignored. The original experimental data

must be normalized to avert such effect. It is the process of transforming the original

sequence to a comparable sequence. Normalization is done in the range of zero and

one, the process is known as grey relational generating. Three types of data

normalization are there in the GRA, lower the better (LB), the higher the better (HB)

and nominal the best (NB).

Lower is better (LB)

(4)

Higher is Better (HB)

(5)

Nominal is best (NB)

(6)

Let the original reference sequence is X0(k). is normalized value of the kth

element in the ith sequence, is desired value of the kth quality characteristic,

max is the largest value of , and min

is the smallest value of ,

Where i = 1,2,………,n; k = 1,2,……,p; n (=32) is the number of experiments and p

(=2) is the number of quality characteristics.


Amit Sethi


Step 2:

Second step is to display the relationship between optimal and actual normalized

value. Grey relational coefficient shows such kind of relationship. For this we have to

calculate deviation sequences of the normalized data. The grey relational coefficient

can be expressed as

0,i(k)

(7)

i = 1,… ,n; k = 1,…,p

where 0,i(k) is the relative difference of kth element between comparative

sequence Xi and the reference sequence X0 (also called GRC), is the absolute

value of difference between X0(k) and Xi(k). [ = X0(k) - Xi (k) ]

is a distinguishing or identification coefficient, and its value lie between zero and one. In general it is set to 0.5.

Step 3:

Gray relational grade is the weighting sum of grey relational coefficient. Highest Grey

Relational Grade gives the best multiple machining characteristics. In this research, it

had been taken the average of the grey relational co-efficient as the grey relational

grade. The grey relational grade is determined by Eq. 8.

GRG = k 0,i(k), i = 1,2,…….,32 (8)

Selection of optimum level

Basically, the larger the grey relational grade, the better is the multiple performance

characteristics. It is clear from table 9 and figure 6 if the process parameter setting on

(A2 B2 C4 D4 E2), then it has the highest grey relational grade. Therefore, A (30V),

B (110 ampere), C (118µs), D (45μs), and E (5mm/min) is the optimal parameter

combination for multi-machining characteristics. The main effects of each process

parameter on grey relational grade are given in table 9.

Table 9 Response table for mean GRG

Level SV Ip Ton Toff Wf

1 0.5698 0.5607 0.5509 0.5969 0.5701

2 0.6333 0.6271 0.5536 0.5854 0.6137

2 0.5880 0.5844 0.6100 0.5804

4 0.5765 0.5955 0.6530 0.6050

Delta 0.0634 0.0664 0.1021 0.0246 0.0436

Rank 3 2 1 5 4




50403020

0.650

0.625

0.600

0.575

0.550

15013011090 118114110106

45403530

0.650

0.625

0.600

0.575

0.550

53

SV

Me

an

of

Me

an

sIP TON

TOFF WF


Figure 6 Shows the graphical representation of values which are tabulated in table 9

5. CONCLUSION

In present work, wire electrical discharge machining (WEDM) for 316L has been

studied. Grey relational analysis (GRA), Anova along with Taguchi method was used

to optimize the Cutting Speed (CS) and surface roughness (SR), simultaneously.

Based on the results and discussions, the following conclusions are made:

Using Taguchi method, CS and SR were optimized individually. The cutting

speed is mostly affected by pulse-on time (Ton) , Pulse off time (Toff) and Servo

Voltage (SV) and surface roughness are mostly affected by the peak current (Ip),

pulse-on time (Ton) , Pulse off time (Toff) and Servo Voltage (SV). Anova has been

applied to find the significant process parameter. Basically, the larger the grey

relational grade, the better is the multiple performance characteristics. The process

parameter setting of (A2 B2 C4 D4 E2) has the highest grey relational grade.

Therefore, A (30V), B (110 ampere), C (118µs), D (45μs), and E (5 mm/min) is the

optimal parameter combination for multi-machining characteristics.

REFERENCES

[1] Pandey P. C. and Shan H. S, Modern Machining Processes, Tata McGraw-Hill

Publishing Company, 1980, pp. 79-80.

[2] Li, L.; Dong, C. F.; Xiao, K.; Yao, J. Z.; Li, X. G. Effect of pH on Pitting

Corrosion of Stainless Steel Welds in Alkaline Salt Water. Constr. Build. Mater.

2014, 68, 709−715.

[3] Sui, R. J.; Liu, Y.; Wang, W. Q.; Qu, Y. P.; Su, C. G.; Chang, F. Failure Analysis

of Austenitic Stainless Steel Pipes in Amine Liquid Regeneration Unit of a

Desulfurizer. Eng. Failure Anal. 2015, 57, 164−170.

[4] Zhiming, L.; Laimin, S.; Shenjin, Z.; Zhidong, T.; Yazhou, J. Effect of High

Energy Shot Peening Pressure on the Stress Corrosion Cracking of the Weld Joint

of 304 Austenitic Stainless Steel. Mater. Sci. Eng, A 2015, 637, 170−174.


Amit Sethi


[5] Hsu, C.-H.; Huang, K.-H.; Lin, Y.-H. Microstructure and Wear Performance of

Arc-deposited Ti−N-O Coatings on AISI 304 Stainless Steel. Wear 2013, 306

(1−2), 97−102.

[6] urko i , L.; urko i , . .; alopek, .; en o, M. M.; egota, S.

Enhancement of Corrosion Protection of AISI 304 Stainless Steel by

Nanostructured Sol-Gel TiO2 Films. Corros. Sci. 2013, 77, 176−184.

[7] Xing, X. G.; Han, Z. J.; Wang, H. F.; Lu, P. N. Electrochemical Corrosion

Resistance of CeO2-Cr/Ti Coatings on 304 Stainless Steel via Pack Cementation.

J. Rare Earths 2015, 33 (10), 1122−1128.

[8] T. Hryniewicz, R. Rokicki, and K. Rokosz. Corrosion Characteristics of Medical-

Grade AISI Type 316L Stainless Steel Surface after Electro polishing in a

Magnetic Field, January 2016.

[9] Lin Gu, LeiLi, Wansheng Zhao, K. P. Rajurkar (2012), Electrical discharge

machining of Ti6Al4V with a bundled electrode, International Journal of

Machine Tools & Manufacture 53,PP 100–106.

[10] O. Dodun, A. M. Gonaclave, L. Slatineanu, G. Nagit (2009), Using wire electric

discharge machining for improved corner cutting accuracy of thin parts, Int. J.

Adv. Manuf. Technol. 41: 858-864.

[11] Susanta Kumar Gauri, Shankar Chakraborty (2009), Multi-response optimization

of WEDM process using principal component analysis, Int. J. Adv. Manuf.

Technol, 41, pp. 741-748.

[12] U. D. Gulhane, A. B. Dixit, P. V. Bane, G. S. Salvi. Optimization of Process

Parameters for 316l Stainless Steel Using Taguchi Method and Anova.

International Journal of Mechanical Engineering and Technology, 3(2), 2012,

pp. 67–72.

[13] Vishal Francis, Ravi.S.Singh, Nikita Singh, Ali.R.Rizvi and Santosh Kumar.

Application of Taguchi Method and Anova in Optimization of Cutting

Parameters for Material Removal Rate and Surface Roughness in Turning

Operation. International Journal of Mechanical Engineering and Technology,

4(3), 2013, pp. 47–53.

[14] Ramkrishna Parihar and Sanjay Jathar Application of Taguchi Method to

Optimize Tensile Shear Strength between Stainless Steel Aisi304 and Mild Steel.

International Journal of Mechanical Engineering and Technology, 6(10), 2015,

pp. 194–201.

[15] Amit Sethi Determine Optimum process parameters in Wire Electric Discharge

Machining (WEDM) on Titanium (Ti6Al7Nb) Alloy by using Grey Relational

Analysis, 2013.

Date post:	13-Feb-2017
Category:	Engineering
Upload:	iaeme-publication
View:	52 times
Download:	0 times