Optimization of WEDM Process Parameters on SS 317 using Grey Relational Analysis

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056
Volume: 04 Issue: 08 | Aug -2017 www.irjet.net p-ISSN: 2395-0072
© 2017, IRJET | Impact Factor value: 5.181 | ISO 9001:2008 Certified Journal | Page 1212
OPTIMIZATION OF WEDM PROCESS PARAMETERS ON SS 317 USING
GREY RELATIONAL ANALYSIS
Y. Chandra Sekhar Reddy1, T. Pratheep Reddy2, Dr. M. Chandra Sekhar Reddy3
1Student, Dept. of Mechanical Engineering, S.V. College of Engineering, Tirupati, Andhra Pradesh, India
2Asst. Prof., Dept. of Mechanical Engineering, S.V. College of Engineering, Tirupati, Andhra Pradesh, India
3Prof. & HOD, Dept. of Mechanical Engineering, S.V. College of Engineering, Tirupati, Andhra Pradesh, India
---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - In thepresentstudy, Multi-objectiveoptimization
of Wire Electrical Discharge Machining parameters of SS 317
was performed using grey relational analysis which converts
the multi responses into a single grade. Taguchi based L9
orthogonal array is used for plan of experiments. The
objectives chosen are the MaximumMaterialremovalrateand
Minimum surface roughness using process parameters viz.,
pulse on time, pulse off time and Peak Current. The optimal
machining parametersresults better qualityandremovalrate.
Key Words: SS 317, Wire electric discharge machining,
Multiobjective optimization, Grey relational analysis.
1. INTRODUCTION
Need of advanced materials is increasing day to day for
structural and engineering applications. Intense research of
new Alloys and Composites [1-2] are going on for the
requirement.
SS 317 stainless steel is developed primarily for higher
strength and corrosion resistance by increasing nickel and
molybdenum to the 316 stainless steel. At present, these
steels are used as in different applications like desalination
plants, valves & fittings, vessels, piping, fasteners, heat
exchangers and construction materials as a replacement of
austenitic stainless steels
Wire Electric Discharge Machining (WEDM) is a non-
conventional machining process utilises thermal energy to
machine electrically conductive parts with intricate shapes
and hard material with high precision [3]. WEDM is adopted
in many industries throughout the world which focused on
fast machining with high surface finish and precision.
To achieve a sound and economical product, selection of
most appropriate machining parameters are required for
machining, for this industries applies various procedures
and techniques to estimate the influence of the machining
parameters on machining rate andsurface quality,whichare
primary objectives. Machinist experiences are simple and
inexpensive, butdoesn’tguaranteesqualityandfunctionality
of product [4].
In the current scenario, optimization plays a vital role in
organizations, industries and research to meet the demands
of product with best quality with less price [5]. Taguchi
method is used to optimize the single objective, but not
suitable for the requirement of industries which has multi
objectives for a single product [6]. Study of multiple
objectives are still an interestingresearcharea.Optimization
of multiple objectives are more difficult and tedious
compared to single objective optimization. To optimize
multi-objective system into single objective, taguchi
integrated grey relational analysis was performed and the
optimal parameters were determined by using grey
relational grade [7-8].
2. LITERATURE SURVEY
Raju et al. [9] studied the effect of Pulse on time, peak
current, Servo voltage and wire tension on the surface
roughness response on 316L SS usingFull factorial designof
experiments. It is observed that Pulse on time is the most
significant factor that affects surfaceroughness(Ra),andthe
peak current, servo voltage and wire tension plays next to
the pulse on time on characteristics respectively. Rajesh
Khanna et al. [10] also studied and observed that Pulse off
time has the most influent parameter on determining
response characteristics of SS316.
Rajmohan et al. [11] investigated optimization of process
parameters for surface roughnessandMRRonSS304Lusing
taguchi integrated Grey relational analysis, and observed
that Pulse off time is the most significant factor that affects
the Grey relational grade and Grey relational analysis
technique converts the multipleperformancecharacteristics
into single performance characteristic and therefore
simplifies the optimization procedure. The accuracy can be
improved by including more number of parameters and
levels. Shunmuga Priyan et al. [12] observed that Pulse on,
Pulse off, Servo Voltage and Wire feed has the effect on the
Ra in the order on machining of SS 304 using GRA. Pulse off
time has opposite effect to pulse on time.MRRdecreasewith
increase of pulse off time, while surface roughness reduces.
Bharathi et al. [13] optimizedtheWEDM processparameters
using additive law and observed that Pulse on time is
significant factor that affects Ra, MRR and Kerf. Pulse on

time, peak current, servo voltage and wire tension plays
significant role in the characteristics respectively.
From the literature study, it is observed that none of the
work has been carried out using SS 317. In the present
investigation, an attempt is made tostudyanddeterminethe
optimal machining parameters of WEDM on SS 317 for
minimum surface roughness and maximum material
removal rate. Pulse on, Pulse off and Peak Current are
chosen as the process parameters with a L9 array plan of
experiments, to collect the experimental data and to analyse
the effect of these parameters on surface roughness and
material removal rate. Taguchi integrated Grey relational
analysis is chosen for Multi-objective optimization.
3. EXPERIMENTAL DETAILS:
SS 317 is used as target material in the presentinvestigation.
Experiments were performed using Electronica Maxicut
Wire EDM as shown in “Fig.1.”
Fig -1 Wire Electric Discharge Machine
Fig -2 Material in Fixture
A 0.25 mm dia brass wire was used as an electrode to erode
the metal under distilled water. A small gap of 0.025 mm to
0.05 mm is maintained in between the wire and work-piece.
The size of the work piece considered for experimentationis
10 * 10 * 10 mm3. The process parameters were being set in
the WEDM machine and the experiments were conducted as
per the plan in Table 2. The time required for material
removal of workpiece is determined by usingstopwatchand
the surface roughness is determined by using talysurf
instrument and the results were tabulated in Table 2.
In this experiment three process parameters are chosen
which have more influence on material removal rate and
surface roughness and each parameter is set at three levels.
The parameters and its levels are showninTable1.L9(33-1 =
9 runs) orthogonal array of experiments was chosen for
experimentation, instead of L 27 array (33 =27 runs) to
reduce the experimentation cost.
Table 1. Process parameters and levels
Process parameters
Levels
Level-1 Level-2 Level-3
Pulse-on (μs) 8 9 10
Pulse-off (μs) 1 2 3
Pulse Current (A) 3 4 5
Table 2. Design of experiments and Responses
S.No
Pulse On
A
(μs)
Pulse off
B
(μs)
Pulse Current
C
(A)
MRR
(mm3
/sec)
Ra
(μ)
1 8 1 3 0.906 4.288
2 8 2 4 0.758 3.133
3 8 3 5 0.612 2.160
4 9 1 4 0.873 3.870
5 9 2 5 0.635 3.735
6 9 3 3 0.758 4.413
7 10 1 5 0.931 5.045
8 10 2 3 0.980 3.600
9 10 3 4 1.022 5.430
Fig -3: Specimens after machining.

3. MULTI OBJECTIVE OPTIMIZATION
3.1 Grey relational analysis:
Grey relational analysis (GRA) methodology is used to
optimize the process parameters having multi-objectives
through grey relational grade. The GRA methodology is as
follows:
1. Conduct theexperiments as per plan.
2. Normalize the responses.
3. Calculate the grey relational coefficients.
4. Calculate the grey relational grade by averaging the grey
relational coefficient.
3.1.1 Normalization:
Convert the original sequencesto a set ofcomparable
sequences by normalizing the data. Depending upon the
response characteristic, three main categories for
normalizing the data is as follows:
3.1.2 Grey relational coefficient and grey relational
grade
Grey relation coefficient (αij) is calculatedfor each
of the performance characteristics, which expresses the
relationship between ideal and actual normalized
experimental results, as shown in “Eq.(4).”
Grey relational gradecan be calculatedby taking the
average of is the weighted grey relational coefficient and
defined as follows
Table -3: Grey Relational Grades
Expt.
No
MRR
(mm3/sec)
Ra
(μm)
Normalized
values
Grey Relational
Coefficients
Grey
Relational
GradesMRR Ra MRR Ra
1 0.906 4.288 0.7157 0.3494 0.637 0.435 0.536
2 0.758 3.133 0.3545 0.7023 0.436 0.627 0.532
3 0.612 2.160 0.0000 1.0000 0.333 1.000 0.667
4 0.873 3.870 0.6348 0.4771 0.578 0.489 0.533
5 0.635 3.735 0.0569 0.5183 0.346 0.509 0.428
6 0.758 4.413 0.3545 0.3112 0.436 0.421 0.429
7 0.931 5.045 0.7773 0.1177 0.692 0.362 0.527
8 0.980 3.600 0.8974 0.5596 0.830 0.532 0.681
9 1.022 5.430 1.0000 0.0000 1.000 0.333 0.667
4. ANALYSIS OF VARIANCE
ANOVA was performed to identify the process parameters
that significantly affect the time and quality. This is
accomplished by separating the total variability of the grey
relational grades, which is measured by the sum of the
squared deviations from the total meanofthegrey relational
grade, into contributions by each machining process
parameters and the error. An ANOVA table consists of sums
of squares, corresponding degrees of freedom, the F-ratios,
and the contribution percentages of the machining factors.
These contribution percentages can be used to assess the
importance of each factor for the interested grades.
Table - 4: Analysis of Variance
Source DF Seq SS Adj SS F - Value P- Value %Contribution
Pulse on 2 0.041539 0.020769 1.57 0.388 55.16%
Pulse off 2 0.004978 0.002489 0.19 0.841 6.62%
Pulse Current 2 0.00223 0.001115 0.08 0.922 2.96%
Error 2 0.026378 0.013189 -- -- 35.11%
Total 8 0.075124 -- -- --
The relative effect among the control factors for the Grey
relational grades can be verified by using the ANOVA so that
the optimal combinations of the machining factors can be
accurately determined. From Table 4, it is also evident that
the control factors Pulse on, Pulse off and Pulse Current has
the most % of contribution in the descending order on the
Grey relational grades.

Fig -4: S/N ratio – Grey relational grades.
5. CONFIRMATION RUN:
After determining the optimal combination of parameters,
the last phase is to verify the MRR, surface roughness by
conducting the confirmation experiment. The A3B3C2 is an
optimal parameter combination of themachiningprocessby
Grey relational analysis. The confirmation test is carried out
with the optimal parameter combination A3B3C2, and the
results are tabulated in Table.5 and the grey relational grade
is increased by 30%. It is clear that the MRR and SR
increased greatly with the optimal parameters.
Table 5. Confirmation test results
Type Initial Optimal/Predicted Experimental
Level combination A1B3C3 A3B3C2 A3B3C2
MRR (mm3/sec) 0.612 0.8872 0.855
SR (μm) 2.16 2.12 2.3
GRG 0.667 0.93 0.94
Conclusions:
 The effect of process parameters i.e. pulse on-time, pulse
off-time, Pulse current on response variables such as
material removal rate, surface roughness has been
thoroughly studied. The levels of significance of process
parameters for each response variable has been investigated
using ANOVA.
 Pulse on time found to be the most significant factors
influencing all responses investigated for both the
experiment sets.
 The A3B3C2 is an optimal parameter combination of the
machining process by Taguchi based Grey relational
analysis.
 The grey relational grade is increased by 30%. It is clear
that the MRR and SR increased greatly with the optimal
parameters.
REFERENCES
[1] Bala G Narasimha, Vamsi M Krishna and Anthony M
Xavior, “A Review on Processing of Particulate Metal
Matrix Composites and its Properties,” International
Journal of Applied Engineering Research,vol.8,Number
6, pp. 647-666, 2013.
[2] G. Bala Narasimha, M. Vamsi Krishna and Anthony M
Xavior, “A Comparative Study on Analytical and
Experimental Buckling Stability of Metal Matrix
Composite Columns with Fixed & Hinged Ends”
International Journal of Applied Engineering Research,
vol. 9, Number 2, pp. 191-196, 2014.
[3] P. Raju, MMM. Sarcar and B. Satyanarayana,“
Optimization of wire electrical discharge machining
parameters for surafcae roughness on 316 L stainless
steel using Full Factorial experimental design”,Procedia
Materials Science, 5, 1670 – 1676, 2014.
[4] P. Bharathi, Tummalapenta Gouri Lalitha Priyanka, G.
Srinivasa Rao, Boggarapu Nageswara Rao, “Optimum
WEDM Process Parameters of SS304 Using Taguchi
Method”, International Journal of Industrial and
Manufacturing Systems Engineering , 1(3), 69-72,2016.
[5] G. Bala Narasimha, M. Vamsi Krishna and R.Sindhu,
“Prediction of Wear Behaviour of Almg1sicu Hybrid
MMC Using Taguchi with Grey Rational Analysis”
Procedia Engineering, vol. 97, Number 2, pp. 555-562,
2014.
[6] Manish Saini, Rahul Sharma, Abhinav, Gurupreet Singh,
Prabhat Mangla,AmitSethi,“Optimizationsofmachining
parameter in wire edm for 316L stainless steel by using
taguchi method, anova, and grey analysis”,International
Journal of Mechanical Engineering and Technology,
Volume 7, Issue 2 pp. 307–320, 2016,M.Vamsi Krishna,
[7] G.Bala Narasimha, N.Rajesh And Anthony M
.Xavior, “Optimization of Influencial parameters on
Mechanical behavior of AlMg1 SiCu HybridMetal Matrix
Composites using Taguchi integrated Fuzzy Approach”,
Procedia Materials today, vol.2(4), pp.1464-146,
2015.
[8] Munuswamy, U., and T. Pratheep Reddy."Multiobjective
Optimization of WEDM Process Parameters on
Al5052/Sic/Gr Hybrid MMC Using Grey Fuzzy."
International Research Journal of Engineering and
Technology, Vol.03, 8, pp. 1320 -1325, 2016.
[9] P. Raju, MMM. Sarcar and B. Satyanarayana,“
Optimization of wire electrical discharge machining
parameters for surafcae roughness on 316 L stainless
steel using Full Factorial experimental design”,Procedia
Materials Science, 5, 1670 – 1676, 2014.
[10] S K Rajesh Kanna, P Sethuramalingam, “Stainless Steel
316 Wire EDM Process Parameter Optimization by
Using Taguchi Method” International Journal of

Emerging Research in Management &Technology,Vol.6
(2), pp. 69 -73, 2017.
[11] T.Rajmohan, Gopi Krishna,AnkitKumarSinghandA.P.V.
Swamy Naidu, “Multiple Performance Optimization in
WEDM Parameters using Grey Relational Analysis”,
Applied Mechanics and MaterialsVols.813-814,pp357-
361, 2015.
[12] M. Shunmuga Priyan , W. Willbert Swin, V. Anand, P.
Kelvin, V. Sai Siva, “Investigation of Surface Roughness
and MRR on Stainless Steel Machined by Wire EDM”,
International Journal of Engineering Research &
Technology, Vol. 5 (03), March-2016.
[13] P. Bharathi, Tummalapenta Gouri Lalitha Priyanka, G.
Srinivasa Rao, Boggarapu Nageswara Rao, “Optimum
WEDM Process Parameters of SS304 Using Taguchi
Method”, International Journal of Industrial and
Manufacturing Systems Engineering ,Vol. (3), 69-72,
2016.

Optimization of WEDM Process Parameters on SS 317 using Grey Relational Analysis

More Related Content

What's hot (17)

Similar to Optimization of WEDM Process Parameters on SS 317 using Grey Relational Analysis (20)

More from IRJET Journal (20)

Recently uploaded (20)

Optimization of WEDM Process Parameters on SS 317 using Grey Relational Analysis