experimental study and multi- objective optimization … · electrical discharge response. it is...

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International Journal of Mechanical Engineering and Technology (IJMET)

Volume 9, Issue 9, September 2018, pp. 204–213, Article ID: IJMET_09_09_025

Available online at http://www.iaeme.com/ijmet/issues.asp?JType=IJMET&VType=9&IType=9

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

© IAEME Publication Scopus Indexed

EXPERIMENTAL STUDY AND MULTI-

OBJECTIVE OPTIMIZATION OF

INDEPENDENT VARIABLES FOR AISI 304

STAINLESS STEEL

Arunbharathi Ramaswamy*

*Corresponding author, Assistant Professor, Department of Mechanical Engineering,

Sri Krishna College of Engineering and Technology, Coimbatore, Tamilnadu-641008, India

Ashoka Varthanan P

Professor, Department of Mechanical Engineering,


Abinash Raju R, Abdul Haathi M and Aravind Kumar G.B

UG Student, Department of Mechanical Engineering,


ABSTRACT

In this work, an attempt has been made to optimize the process variables such as

current, gap distance, pulse on time, pulse off time and voltage to obtain the desired

performance characteristics like minimum tool wear rate (TWR), over cut (OC) and

maximum material removal rate (MRR) in hole drilling EDM process. In order to

achieve this, experimental work has been carried out on AISI 304 stainless steel using

copper as tool electrode by implementing full factorial central composite design

(CCD) based Response Surface Methodology (RSM). The measured response values

viz., TWR, MRR and OC were also analyzed using MINITAB software to determine the

significant parameters and adequacy of the proposed model with the aid of analysis of

variance method. Mathematical model has been developed for the prediction of MRR,

TWR and OC as a function of interaction and higher order terms of current, gap

distance, pulse on time/off time and voltage. The optimal combination of independent

parameters was obtained using desirability function approach through Response

optimizer plot.

Keywords: Hole Drilling EDM, RSM, AISI 304 SS.

Experimental Study and Multi-Objective Optimization of Independent Variables for Aisi 304

Stainless Steel


Cite this Article: Arunbharathi Ramaswamy, Ashoka Varthanan P, Abinash Raju R,

Abdul Haathi M and Aravind Kumar G.B, Experimental Study and Multi-Objective

Optimization of Independent Variables for Aisi 304 Stainless Steel, International

Journal of Mechanical Engineering and Technology, 9(9), 2018, pp. 204–213.

http://www.iaeme.com/IJMET/issues.asp?JType=IJMET&VType=9&IType=9

1. INTRODUCTION

Nowadays small hole drilling Electrical Discharge Machining (EDM) has developed as

important manufacturing techniques for drilling fine holes precisely at a faster rate. In the

thermal erosion process, the interaction between tool and work material has been eliminated.

Generally, EDM drilling has been used for huge production work. In any electrical conductive

materials, holes are drilled irrespective of its hardness. The process of EDM drilling has

recognized in the manufacturing of metal pieces with complicated geometries [1-4]. It has

some definite applications like fuel injectors, hardened punch for inserting ejectors, drilling

holes in turbine blades, drilling starter holes in wire EDM, cutting a hole for circulating

coolant etc. Among different EDM parameters particularly voltage (V), peak current (IP),

pulse on time (TON), gap distance (GAP) and pulse off time (TOFF) have important influences

on the holes produced. Most of the researchers have studied that the effect of these parameters

on TWR, MRR, surface finish, and OC and also have assumed the voltage, peak current and

the gap distance between work piece as static and tool, i.e. deterministic [5-7]. Simon et al.

[8] studied the effects of electrode revolution speed on MRR, current, voltage, duty factor.

These researches were deliberated about performing rapid hole drilling EDM and the

optimum parameter combinations. The electrodes were made of copper rod and holes were

drilled on the work piece.

Kiyak et al. [9] examined the stimulation of parameters in Electrical Discharge Machine

on the surface roughness for the process of machining tool steel. The taken EDM parameters

were pulse time, pulse pause time and pulse current. The authors reported that surface

roughness of work piece and electrodes were directly proportional to pulse time and pulsed

current. Higher estimated values of these parameters increases surface roughness of the work

piece. Lower pulse time, relatively higher pulse pause time and lower current that produces a

good surface finish on the work piece.

Pradhan et al. [10] evaluated the surface roughness for steel work piece with AISI D2 tool

and he has taken electrode made of copper. The required input parameters are pulse duration,

pulse off time, applied voltage and discharge current. From the process parameters which

have significant effects on the surface roughness are found to be pulse duration, discharge

current and pulse off time.

Wang et al. [11] inspected about micro-hole machining on the Polycrystalline diamond by

means of the micro-EDM process. A sequence of experiments was conducted to study the

polarities for the influences of micro-EDM parameters and effective machining on the

machining performance. The outcomes of Experiments found that negative polarity

machining is meant for micro-EDM process on PCDs due to the adhesion of eroded materials

brought over the guarded electrode. The tool electrode has a suitable volume of adhesion on

which increases MRR and reduces the relative TWR. To study the EDM process parameters

such as tool-wear rate, over cut material removal rate calculated by Taguchi methodology.

Azad [12] had preferred design factors as current, frequency, width and voltage. The

experimental results concluded that the optimization of most important parameters such as

current and voltage for single quality characteristic are not suitable for the multiple quality

responses. Experimentally investigated the performance obtained from the micro EDM deep

Arunbharathi Ramaswamy, Ashoka Varthanan P, Abinash Raju R, Abdul Haathi M and Aravind

Kumar G.B


drilling with electrically and standard insulated tools. Brass electrodes are used for EDM

drilling of 2mm diameter on Inconel 718.The response surface methodology is used to

develop mathematical model which gives reliable forecasted experimental results within

acceptable range. Prasad and Krishna [13] made on attempt to study the machining of

complex profiles. Choosing the right combination of input parameters will decide the

performance of any machining process. The most significant output parameters are SR and

MRR which influences the performances of machining process. The response surface

methodology is used for modeling SR and MRR and optimization developed mathematical

models are used.

Jahan et al. [14] examined the major parameters that influence the performances of

Electrical Discharge Response. It is very difficult to achieve the optimal performance as there

are many process parameters that determined the output characteristics. The work piece used

is AISI 304 and rotating copper electrode used to drill a hole on it. The optimal combination

of process parameters TON, TOFF, IP, voltage and gap distance are found for better MRR, OC

and TWR in this paper. In the majority of the past examinations, just single objective has been

investigated. In this work an endeavor has been made to consider and enhance multiple

quality characteristics.

2. MATERIALS AND METHODS

The EDM DRILLING machine, SD 350 ZNC fabricated by Oscar EDM Ltd has been utilized

to do the trials. The target of this exploration is to get minimum value of OC, TWR and

maximum value of MRR. the machining zone has shown in the figure 1. The extent of the

work piece considered as 250mm length, 10.5mm thickness and 100mm width. In this

investigation, ø of 3mm hole has been drilled in all the experiments utilizing copper cathode.

Distilled water was utilized as dielectric liquid. The EDM experiment parameters and

conditions are described in the table 1

Figure 1 Photograph of Machining

The elements present in AISI 304 Stainless Steel work piece material is as per the

following: Fe: 66.345%, Ni: 8 to 10%, Cr: 17 to 19%, Mn: Max 2%, Si: Max 1%.

Table 1 Experimental Conditions and Parameters

Parameters Designation Description

Work piece material - AISI 304 stainless steel

Electrode material - Copper

Electrode diameter - 3 mm

Pulse current A 3 - 7 A

Pulse on time B 15 - 25 μs

Pulse off time C 9 - 12 μs

Voltage D 2 - 4 volt

Gap Distance (E) E 2 - 4 mm


Stainless Steel


Prior to the experimentation, top surface of the work piece was leveled utilizing surface

granulating machine. During initial and last phase of the machining procedure, weight of the

work piece has been estimated using electronic weight adjust. Refer to (1-3) to compute the

tool wear rate, material removal rate and over cut for the hole with the assistance of tool

maker microscope and it is showed up in figure 2.

Figure 2 Tool Makers Microscope

(

(1)

(2)

(3)

The combination of parameters with constrained numbers can be framed by utilizing

Design of Experiment. In this examination, full factorial central composite design based RSM

has been utilized to design the experiments. Table 2 exhibits the process parameters

considered for designing the experiments. Five levels were taken for pulse current (factor A),

pulse on time (factor B), pulse off time (factor C), voltage (factor D) and gap distance (E).

The factors were chosen by the guide of primer tests and furthermore the handbook prescribed

by the machine producers.

Table 2 Machining Parameters and Levels.

Factors Symbol Unit Levels

1 2 3 4 5

A IP A 3 4 5 6 7

B TON Μs 15 17.5 20 22.5 25

C TOFF Μs 9 10.5 12 13.5 15

D V Volt 2.5 3 3.5 3 4

E GAP Mm 2.5 3 3.5 3 4

54 runs/tests were recommended by CCD based RSM method and it is shown in table 3.

Tests were conducted according to the design combination parameters and the responses tool

wear rate, material removal rate and over cut were calculated. Final hole drilled components

are shown in figure 3(a) & 3(b).


Kumar G.B


Table 3 Experimental Results

Run A B C D E MRR g/min TWR (%) OC mm

1 6 17 13 3 3 0.1179 28.57 0.0494

2 4 18 10 3 3 0.1314 19.04 0.0557

3 4 20 10 2 3 0.1268 28.57 0.024

4 5 20 12 3 3 0.1785 47.61 0.043

5 5 20 12 3 3 0.1693 33.37 0.0449

6 5 20 12 3 3 0.1755 38.09 0.033

7 4 18 13 2 2 0.1108 42.85 0.0557

8 5 20 12 3 3 0.1745 38.09 0.0303

9 4 23 13 3 3 0.1163 28.57 0.043

10 6 23 10 2 2 0.1106 38.09 0.0621

11 6 23 10 2 3 0.1068 38.09 0.0494

12 6 18 13 3 3 0.1198 28.57 0.0557

13 4 23 13 2 2 0.1146 47.61 0.043

14 4 18 10 3 3 0.1316 38.09 0.0557

15 6 23 13 3 3 0.1016 28.57 0.0494

16 6 23 13 3 3 0.1005 33.37 0.043

17 5 20 12 3 3 0.1789 28.57 0.0494

18 6 18 13 2 3 0.1102 23.8 0.0245

19 6 18 10 3 3 0.1256 33.33 0.0748

20 4 23 13 3 3 0.1197 23.8 0.0684

21 6 18 10 3 3 0.1226 19.04 0.0621

22 6 23 10 3 3 0.1106 23.8 0.0557

23 6 18 10 3 3 0.1219 28.57 0.0621

24 5 20 12 3 3 0.1965 38.09 0.1192

25 6 23 13 2 2 0.1025 28.57 0.0494

26 4 23 13 3 3 0.1159 33.33 0.043

27 4 18 13 3 3 0.1084 28.57 0.0303

23 5 20 12 3 3 0.1794 19.04 0.0494

28 4 23 10 3 2 0.1228 9.52 0.0811

29 4 18 13 2 2 0.1093 14.28 0.0367

30 4 23 10 2 3 0.1152 28.57 0.624

31 4 18 13 3 3 0.1228 19.04 0.0557

32 4 23 10 3 3 0.1258 19.04 0.0748

33 4 23 10 3 3 0.1235 28.57 0.0621

34 5 20 12 3 3 0.1859 28.57 0.0748

35 4 18 10 2 3 0.1136 19.04 0.0557

36 6 18 13 3 3 0.1223 19.04 0.0748

37 6 18 10 2 3 0.1195 19.04 0.0494

38 6 23 13 3 2 0.1055 23.8 0.0113

39 6 23 10 3 3 0.1067 38.09 0.0241

40 5 20 9 3 3 0.1953 28.57 0.1002

41 5 20 12 3 3 0.1844 28.57 0.0684

42 5 20 12 3 3 0.1868 19.04 0.0811

43 5 20 12 3 2 0.1911 19.04 0.0875

44 5 25 12 3 3 0.1999 28.57 0.1002

45 5 20 12 3 3 0.1859 19.04 0.0748

46 5 20 12 3 3 0.1844 14.28 0.0684

47 5 20 12 2 3 0.1856 9.52 0.0811

48 5 20 12 3 3 0.1824 19.04 0.0621

49 5 20 15 3 3 0.1774 19.04 0.0557

50 5 20 12 3 3 0.1945 23.8 0.1107

51 7 20 12 3 3 0.0942 28.57 0.0176

52 3 20 12 3 3 0.1022 28.57 0.0815

53 5 15 12 3 3 0.1837 28.57 0.0875

54 5 15 12 3 3 0.1837 28.57 0.0875


Stainless Steel


Figure 3 a Final Hole Drilled Component (Run 1-27)

Figure 3 b Final Hole Drilled Component (Run 28-54)

3. MATHEMATICAL MODELS

Response surface methodology (RSM) model is utilized for collecting of mathematical and

statistical techniques that are useful for improving, developing and optimizing the process.

RSM additionally has basic applications in the development, design, and specifying of new

items and further in the difference in existing item design. In this work, second order

regression model is generated for forecasting TWR, OC and MRR as far as interactive and

higher order machining parameters through RSM methodology using experimental data and it

is appeared in (6-8). Generally, the surface response can be rewritten as

4

The term Y is the corresponding response e.g. TWR, MRR and OC are created by the

various process parameters of hole drilling Electrical Discharge Machine; xi (1, 2,… , n)

speaks to the coded levels of n quantitative process parameters. Where the term β0, βi, βii,

and βij are the second order regression co-efficient. Under this summation the second term

represents that the condition is polynomial due to the linear effects, however the third term

relates to the higher order effects, the fourth term corresponds to the interactive effects of

process parameters.

Applying the least square technique, using the observations collected (Y1, Y2,…..Yn )

during design points (n), the values of these co-efficient can be determined. This equation can

also be rewritten based on the five variables in the coded form (5).

Yu= bo + b1 X1 + b2 X2 + b3X3 + b4 X4 + b5 X5 + b11 X12+b22 X2

2 + b33 X3

2+ b44 X4

2 + b55 X5

2+ b12 X1X2

+ b13 X1 X3 + b14 X1 X4 + b15 X1 X5 + b23 X2 X3 + b24 X2 X4 + b34 X3 X4 + b35 X3 X5 + b45 X4 X5 (5)

MRR = - 1.40843 + 0.317995 × A + 0.0434513 × B + 0.0524569 × C + 0.158511 × D -

0.118856 × E - 0.03033009× A*A - 0.00120308 × B*B - 0.00319425 × C*C - 5.80301E-04 ×

A*B + 0.000421345 × A*C - 0.0010809 × A*D - 0.00291466 × A*E + 0.000484994 × B*C -

0.00388477 × B*D + 0.00419969 × B*E - 0.00305185 × C*D + 0.00626079 × C*E -

0.00885370 × D*E (6)

TWR = 14.8259 - 18.34795 × A + 3.25698 × B + 14.5058 × C - 48.77762 × D - 10.3300 × E

+ 0.3254464 × A*A + 0.0547520 × B*B - 0.496588 × C*C + 0.3286700 × A*B -0.348564 ×


Kumar G.B


A*C-0.543955 × A*D + 5.03621 × A*E + 0.08886204 × B*C - 1.89090 × B*D - 0.730292 ×

B*E + 3.06100 × C*D - 4.14883 × C*E + 20.0097 × D*E (7)

OC = - 0.424665 + 0.190089 × A + 0.0059123 × B - 0.084865 × C + 1.19245 × D - 0706743 ×

E - 0.00485362 × A*A -0.00150086 × B*B + 0.0008052 × C*C - 00112096 × A*B +

000680489 × A*C + 0.0731086 × A*D - 0.0731916 × A*E - 0.001134 × B*C - 0.05042 ×

B*D + 0.051439 × B*E -0.0163674 × C*D + 0.0328546 × C*E - 0.144424 × D*E (8)

4. RESULTS AND DISCUSSIONS

The Minitab Statistical package was utilized to examine the response parameters and

experimental data. To check the capability of the recommended models and the significance

of individual parameters at 95% assurance level that can be Analysis of variance has been

performed. The regression coefficients for MRR, OC, TWR and the corresponding P values

shown as it are in the table (4-6). When the value of P less than 0.005, then the factor is called

as significant factor. When the P is 0, it is known as the most significant factor.

Table 4 Estimated Regression Coefficients for MRR

Term coef SE coef T P

CONSTANT 0.125056 0.006793 18.410 0.000

IP 0.128485 0.194270 0.661 0.049

TON 0.020863 0.116016 0.180 0.038

TOFF -0.101246 0.085826 -1.180 0.245

V 0.001944 0.005062 0.3840 0.703

GAP 0.003956 0.006793 0.582 0.563

IP * IP 0.155098 0.230892 0.672 0.505

TON*TON -0.038959 0.145598 -0.268 0.790

TOFF*TOFF -0.098102 0.089488 -1.096 0.279

IP *GAP 0.034857 0.071011 0.491 0.626

TON*GAP -0.032696 0.067410 -0.485 0.630

V*GAP 0.000644 0.005062 0.127 0899

S = 0.0144680 R – Sq = 86.88 R–Sq(adj) = 83.45

Table 5 Estimated Regression Coefficients for TWR


CONSTANT 22.8161 1.483 15.384 0.000

IP 67.2184 42.417 1.585 0.031

TON -29.2654 16.739 -1.402 0.002

TOFF -26.2654 18.739 -1.402 0.108

V 1.1003 1.105 0.996 0.325

GAP -0.3398 1.483 -0.229 0.820

IP * IP 88.5750 50.413 1.757 0.086

TON*TON -57.1146 31.790 -1.797 0.080

TOFF*TOFF -30.1317 19.539 -1.542 0.131

IP *GAP -5.1553 15.504 -0.333 0.741

TON*GAP 6.1768 14.718 0.420 0.677

V*GAP 0.2328 1.105 0.211 0.834

S = 3.15894 R – Sq = 82.28 R –Sq (adj) = 77.64


Stainless Steel


Table 6 Estimated Regression Coefficients for OC


CONSTANT 0.051645 0.004741 10.893 0.000

A -0.096841 0.135589 -0.714 0.479

TON -0.049193 0.080973 -0.608 0.547

TOFF 0.442566 0.059902 7.3888 0.000

V 0.004640 0.003533 1.313 0.196

GAP -0.00896 0.004741 -0.189 0.851

A*A -0.343482 0.161150 -2.131 0.039

TON*TON -0.102014 0.101619 -1.004 0.321

TOFF*TOFF 0.712397 0.062458 11.406 0.000

A*GAP -0.016840 0.049562 -0.340 0.736

TON*GAP 0.020554 0.047049 0.437 0.664

V*GAP 0.001465 0.003533 0.415 0.681

S = 0.0100979 R – Sq =98.75 R – Sq (adj) = 98.42

The above shown tables demonstrate that pulse on-time, peak current significantly effects

on material removal rate and also have effects on the Tool wear rate and it likewise observed

that second order peak current and pulse off time significantly has effects on Over Cut.

The multiple regression coefficients were calculated to check whether the model has

actually defined the experimental data or not. The percentages of multiple regression

coefficients were observed to be 82.28%, 86. 88% and 98.75% for TWR, MRR and OC

respectively. When the regression coefficients have larger value, it tends to be said the second

order model that are sufficient for the process. Surface plot for the responses (TWR, MRR

and OC) plotted by using most significant parameters and significant parameters and therefore

others parameters were kept constant due to their influence is not much on the responses and

it has shown in the figure 4(a) ,4(b) and 4(c).

z

Figure 4 a Surface Plot for MRR Figure 4 b Surface plot for TWR

Figure 4 c Surface Plot for OC

0.00

0.05

0.10

3.04.5

6.0

0.15

25

20

15

7.5

MRR

T ON

A

TOFF 12

V 2.5

GAP 2.5

Hold Values

MRR

0

15

30

3.03.04.5

6.0

3.0

30

45

15

7.5

20

25

T WR

T ON

A

TOFF 9

V 2

GAP 2

Hold Values

TWR

-0.1

0.0

0.1

3.03.04.5

6.0

3.0

0.2

14

12

10

7.5

OC

T OFF

A

TON 15

V 2

GAP 2

Hold Values

OC


Kumar G.B


From the surface plots we came to know that MRR increases while there is an increase in

pulse current value. The reasons that the discharged energy has increased to facilitate the

action of vaporization and melting and to early large impulsive force in the spark gap,

therefore it increases the material removal rate(MRR). In case of high current, Material

removal rate is high and more concentrated at the electrode gap as well as debris is larger.

Bridging the gap between work piece and electrode because of the excessive concentration of

larger size of debris and subsequently short circuits which reduces MRR. From the figure 4(b)

shown that TWR increases while there is an increasing in pulse on time and pulse current.

Therefore, the large pulse duration and large pulse current will produced larges discharge

energy and it causes a Large Tool Wear Rate in the work piece. Due to the low level of pulse

on time and high level of pulse current, reduces the TWR value.

Many designed investigations which comprises determining optimal conditions that can

be produces the best resultant value for these responses. The operating conditions can be

controlled depends upon the design type (responses surface, factorial or mixtures). It may

include one or more following design parameters are components, factors, amount of

variables or process variables. Response optimizer has been utilized for providing optimal

solutions of optimization plot and the input variable combinations. The optimization plot is

more interactive and by adjusting input parameters settings on the plot to obtain more

desirable solutions. Using Response optimizer plot to identify the optimal combination of

parameters are shown in the figure 5.

Figure 5 Response Optimizer

The figure 5 shows that optimal solution for the EDM drilling process among various

DOE combination. The optimized value for the process can be indicated through square

bracket at the top of the figure 5. The optimized value is engaged between the high and low

range of process parameters value. The best optimal parameter for output reaction on work

piece AISI 304 stainless steel as follows, Toff = 12 μs, Ton = 20 μs, Ip = 5 amp, V = 3 volt and

Gap distance = 2mm.

5. CONCLUSION

Hole drilling EDM machine has been used to carry out the experiments and the process

parameters were optimized to attain the desired performances. From the experimental results,

it is inferred that current and sparking time has major effect on material removal rate and tool

wear rate. Also it has been observed that pause time and higher order of current has major

influence on over cut.


Stainless Steel


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http://www.springerlink.com/content/?Author=P.+Kuppan

http://www.springerlink.com/content/?Author=A.+Rajadurai

http://www.springerlink.com/content/?Author=S.+Narayanan

http://www.springerlink.com/content/0268-3768/

http://www.springerlink.com/content/0268-3768/

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