The document describes an experiment to optimize the parameters of abrasive water jet machining (AWJM) for Inconel 625 alloy. The experiment aims to maximize the material removal rate (MRR) and minimize the surface roughness (SR) simultaneously. In the first stage, MRR and SR are individually optimized using Taguchi methods. In the second stage, Taguchi method is combined with TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) to optimize MRR and SR simultaneously. Water pressure, standoff distance, abrasive flow rate, and jet traverse speed are varied as parameters. Signal-to-noise ratios, means, and regression equations are generated from the experimental data to determine

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International Journal of Mechanical Engineering and Technology (IJMET)
Volume 6, Issue 9, Sep 2015, pp. 01-09, Article ID: IJMET_06_09_001
Available online at
http://www.iaeme.com/IJMET/issues.asp?JTypeIJMET&VType=6&IType=9
ISSN Print: 0976-6340 and ISSN Online: 0976-6359
© IAEME Publication
________________________________________________________________________
SELECTION OF OPTIMUM PARAMETERS
FOR MRR & SR SIMULTANEOUSLY IN
AWJM OF INCONEL-625 ALLOY USING
TOPSIS METHOD
G Venkata Kishore
M. Tech-CAD/CAM Student, Dept. of Mechanical Engineering,
Siddarth Institute of Engineering and Technology,
Narayanavanam, Puttur, India,
F Anand Raju
Professor, Dept. of Mechanical Engineering,
Siddarth Institute of Engineering and Technology,
Narayanavanam, Puttur, India
ABSTRACT
Inconel 625 has a wide range of applications because of its high resistance
to corrosion cracking, pitting, crevice formation and to a variety of mineral
and organic acids along with high thermal fatigue resistance. However
conventional machining of Inconel 625 is not so economical and ease, on the
other hand non conventional processes like laser cutting, abrasive water jet
machining(AWJM), electric discharge machining(EDM), etc have a very good
potential in overcoming these machining difficulties. Among these AWJM is
commonly employed for very hard and brittle materials due to its economical
and technical significance.
In this paper, research oriented experimentation of the AWJM of Inconel
625 is planned and carried out successfully to optimize the input process
parameters for fine Surface finish (SR) and high Material removal rate
(MRR). Optimization was carried out in two phases, in the first phase SR and
MRR are individually optimized as per Taguchi method, in the second phase
Taguchi’s philosophy is combined with TOPSIS method for the simultaneous
optimization of SR and MRR. S/N ratios, Means, Regression equations, etc
with their corresponding graphs are generated and provided in the following
sections.
Key words: AWJM, Inconel 625, MRR, SR, Taguchi integrated with TOPSIS
method.

2. G Venkata Kishore and F Anand Raju
http://www.iaeme.com/IJMET/index.asp 2 editor@iaeme.com
Cite this Article: G Venkata Kishore and F Anand Raju, Selection of
Optimum Parameters For MRR & SR Simultaneously In AWJM of Inconel-
625 Alloy Using Topsis Method. International Journal of Mechanical
Engineering and Technology, 6(9), 2015, pp. 01-09.
http://www.iaeme.com/currentissue.asp?JType=IJMET&VType=6&IType=9
1. INTRODUCTION
Inconel 625 is one among the family of austenite nickel-chromium-based super alloys
which are having high resistance to corrosion and stabilized mechanical properties
even at extreme temperatures. Specifically In conel 625 is highly resistant to inter
crystalline corrosion, pitting, crevice corrosion, chloride induced stress corrosion
cracking and oxidative stress at high temperatures of up to 10500
C.Hence it is
employed for the manufacture of components that are in continuous exposure to sea
water and high mechanical stresses such as oil and gas extraction machinery,
aerospace industry, marine appliances, chemical processing, nuclear reactors,
pollution control equipment etc. As the alloy is having high strength and work
hardening nature it cannot be machined with conventional machining processes.
Therefore non conventional machining techniques like Laser cutting and abrasive
water jet machining etc are employed. From economic perspective laser cutting is
very good for cutting Inconel 625 plates of upto 4mm thick, for Inconel-625 plates
beyond 4mm thick AWJM is commonly used. Further AWJM has the capability of
machining the least slot widths over the complex components.
Extensive studies over optimization of AWJM of grey cast iron, aluminium,
composites, steel, hard polymers and tiles reveals that water pressure and abrasive jet
treverse speed are the significant factors influencing MRR on the othe hand water
pressure and abrasive flow rate mostly determine the quality of surface finish.
Remaining parameters like abrasive grit size and stand off distance are sub significant
in determining MRR and SR. It was observed that comparitively high abrasive flow
rates are required for ferrous materials followed by non ferrous materials and hard
polymers. Till now optimization of AWJM parameters for Inconel 625 did not gained
much attention despite the prevalence of AWJM of Inconel-625. So the present work
concentrates on the optimization of AWJM parameters for good MRR and SR based
on Taguchi method combined with TOPSIS multicriterian optimization technique. In
this work, Water pressure, stand off distance, abrasive jet traverse speed and abrasive
flow rate were optimized for two quality characteristics namely material removal rate
(MRR) and surface roughness (SR). Optimized values are predicted based on the
results obtained from ANOVA, F-tests, Signal To Noise Ratios and Closeness
Coefficient (CCi) of TOPSIS method. Further regression equations are generated
through Regression Analysis.
2. EXPERIMENTAL WORK
2.1. Work Material
In the present work Inconel 625 alloy plate of 300mm*150mm*15mm was taken to
perform the experiments as per Taguchi’s DOE.
2.2. Machine Details
The experiments are performed at ALIND Pvt. Ltd which is the leading AWJM
workshop in Chennai (T.N). KMT’s Streamline SL-V 50 Classic ultra high pressure

3. Selection of Optimum Parameters for MRR & SR Simultaneously In AWJM of Inconel-625
Alloy Using Topsis Method
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intensifier pump fitted with a CNC cutting bed and variable abrasive feeding system
was used for this research work.
Table 1 Machine specifications
S.No Parameter Specification
1 Dimentions of pump 1700 mm X 1500 mm X 1400 mm
2 Table size 1500 mm X 3000 mm
3 X-Axis 1500 mm
4 Y-Axis 3000 mm
5 Z-Axis 210 mm
6 Ultra high pump 50 HP
7 Max water pressure 55,000 psi (3,800 bar)
8 Orifice diameter 0.35 mm
9 Number of nozzles 1
10 Abrasive flow rate (Garnet Sand) 100 – 700 gms/min
2.3. Process Parameters
2.3.1. Constant parameters
The parameters as given in Table 2 are kept constant throught the experimentation.
Table 2 Constant Parameters
Abrasive type Garnet Sand
Abrasive size 80 Mesh
Orifice dia 0.35 mm
Nozzle dia 1.05 mm
Nozzle length 76.2 mm
Work piece thickness 15 mm
Jet impact angle 900
Avg dia of abrasives 0.18 mm
2.3.2. Variable parameters and their range
Input Parameters as given in Table 3 are varied within their maximum feasible range
to generate Taguchi’s L9 Orthogonal Array.
Table 3 Variable Process Parameters
Symbol Control Factor Level 1 Level 2 Level 3
PRE Water pressure (MPa) 230 300 370
SOD Stand off distance (mm) 2 5 8
AFR Abrasive flow rate (gm/min) 300 400 500
JTS Jet traverse speed (mm/min) 84 147 210
2.4. Measuring MRR and SR
2.4.1. MRR measurement
The work piece is first weighted with the balancing machine and then kept on the rack
of the working table. After cutting the work piece, its weight is measured again for
finding out the difference in weights, which is used to calculate the material removal
rate.

4. G Venkata Kishore and F Anand Raju
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2.4.2. Surface finish measurement
Talysurf SJ-201P: Talysurf SJ-201P (Portable surface roughness tester) instrument
was used to measure the surface roughness values at different cross-section of the
cuts.
2.5. Experimental procedure
Practical experiments are conducted as per Taguchi’s OA. Since we are opting for 4
parameters each variable at 3 different levels, as per Taguchi’s DOE a L9 Orthogonal
array is best suited for 4x3 design. Therefore vertical cuts are made over the work
piece by varying the parameter settings in accordance with the OA given in Table 4,
for this purpose water pressure is varied at a separate manual console provided in the
KMT Streamline SL-V pump hose, remaining three parameters are varied at the CNC
console provided near the machining bed.
3. OPTIMIZATION
Simultaneous mode of optimization followed here consists two stages of optimization.
In the first stage individual characteristics i.e.MRR followed by SR are optimized one
by one saperately as per Taguchi philosophy and two optimized parameter settings are
predicted one each for MRR and SR respectively. In the second stage both the quality
characteristics i.e.MRR and SR are taken simultaneously to calculate the
CLOSENESS COEFFICIENT(CCi) as per TOPSIS method. This CCi is again
optimized as per Taguchi analysis to generate a input parameter combination such that
the generated combination represents a simultaneous optimized setting for both the
quality characteristics MRR and SR.
3.1. Optimization of individual quality characteristics
3.1.1. Material removal rate (MRR)
For the purpose of optimising MRR, Taguchi analysis is carried out using MINITAB
16.1 software.
Table 4 L9 OA with observed MRR and generated S/N ratios and Means
Trial
No.
Control Parameter (Levels)
Result /
observed value
Taguchi Analysis
PRE
(Mpa)
SOD
(mm)
AFR
(gm/min)
JTS
(mm/min)
MRR(g/min)
S/N
Ratios
Means
1 230 2 300 84 37.37 31.4505 37.37
2 230 5 400 147 40.83 32.2196 40.83
3 230 8 500 210 42.22 32.5104 42.22
4 300 2 400 210 39.52 31.9363 39.52
5 300 5 500 84 42.76 32.6208 42.76
6 300 8 300 147 38.16 31.6322 38.16
7 370 2 500 147 40.45 32.1384 40.45
8 370 5 300 210 41.84 32.4318 41.84
9 370 8 400 90 45.34 33.1296 45.34

5. Selection of Optimum Parameters for MRR & SR Simultaneously In AWJM of Inconel-625
Alloy Using Topsis Method
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Table 5 Response table of mean S/Nratio for MRR
370300230
32.6
32.4
32.2
32.0
31.8
852
500400300
32.6
32.4
32.2
32.0
31.8
21014784
Pressure
MeanofSNratios
SOD
AFR JTE
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Larger is better
Figure 1 Main effects plot for S/N ratios of MRR
3.1.2. Surface roughness (SR)
SR values are also analysed and optimized in the same manner as that of MRR and
the generated results are given below in the form of tables and graphs.
Table 6 L9 OA with observed SR and generated S/N ratios and Means
Trial
No.
Control Parameter (Levels) Resut/ Observed value Taguchi Analysis
PRE
(Mpa)
SOD
(mm)
AFR
(gm/min)
JTS
(mm/min)
Surface roughness (μm)
1 2 3 Avg S/N
ratios
Means
1 230 2 300 84 0.97 1.21 1.10 1.09 -0.74853 1.09
2 230 5 400 147 0.79 0.95 1.14 0.98 0.26457 0.97
3 230 8 500 210 0.86 0.97 1.01 0.95 0.44553 0.95
4 300 2 400 210 1.09 1.12 1.24 1.15 -1.21396 1.15
5 300 5 500 84 1.05 1.19 1.06 1.10 -0.82785 1.10
6 300 8 300 147 1.51 1.28 1.35 1.38 -2.79758 1.38
7 370 2 500 147 0.96 0.92 0.92 0.93 0.63034 0.93
8 370 5 300 210 1.04 0.99 1.02 1.02 -0.17200 1.02
9 370 8 400 90 0.81 0.90 0.84 0.85 1.41162 0.85
Level Pressure SOD AFR JTS
1 31.84 32.06 31.84 32.40
2 32.43 32.06 32.42 32.00
3 32.42 32.57 32.42 32.29
Delta 0.59 0.51 0.58 0.40
Rank 1 3 2 4

6. G Venkata Kishore and F Anand Raju
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Table 7 Response table of mean S/Nratio for SR
370300230
0.5
0.0
-0.5
-1.0
-1.5
852
500400300
0.5
0.0
-0.5
-1.0
-1.5
21014784
Pressure
MeanofSNratios
SOD
AFR JTS
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Smaller is better
Figure 2 Main effects plot for S/N ratios of SR
3.2. Simultaneous optimization by TOPSIS method
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was
stated in the year 1995 by Hwang and Yoon. References provided at the end will
explain the detailed step by step of TOPSIS. Here only the end resulted tables are
provided due to a constraint on total no. of pages.
Table 8 Decision Matrix (DM)
S.No 1 2 3 4 5 6 7 8 9
MRR 37.37 40.83 42.22 39.52 42.76 38.16 40.45 41.84 45.34
SR 1.09 0.97 0.95 1.15 1.10 1.38 0.93 1.02 0.85
Table 9 Normalised DM (NDM)
S.No 1 2 3 4 5 6 7 8 9
MRR 0.303 0.332 0.343 0.321 0.347 0.310 0.328 0.340 0.368
SR 0.328 0.313 0.343 0.349 0.334 0.411 0.280 0.292 0.331
Table 10 Weighted NDM (WNDM)
S.No 1 2 3 4 5 6 7 8 9
MRR 0.152 0.165 0.171 0.160 0.173 0.155 0.164 0.170 0.184
SR 0.164 0.156 0.171 0.174 0.167 0.205 0.140 0.146 0.165
Level Pressure SOD AFR JTS
1 -0.012 -0.444 -1.239 -0.054
2 -1.613 -0.245 0.154 -0.634
3 0.623 -0.313 0.082 -0.313
Delta 2.236 0.198 1.393 0.579
Rank 1 4 2 3

7. Selection of Optimum Parameters for MRR & SR Simultaneously In AWJM of Inconel-625
Alloy Using Topsis Method
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Table 11 Saperation Measures
S.No 1 2 3 4 5 6 7 8 9
Si
+
0.040 0.024 0.033 0.027 0.028 0.071 0.019 0.015 0.025
Si
-
0.041 0.051 0.039 0.032 0.044 0.003 0.066 0.623 0.041
Table 12 Closeness Coefficient (CCi)
S.No 1 2 3 4 5 6 7 8 9
Si
+
+ Si
-
0.082 0.075 0.073 0.059 0.073 0.074 0.086 0.077 0.066
CCi= Si
-
/
(Si
+
+ Si
-
)
0.509 0.675 0.539 0.544 0.607 0.042 0.770 0.802 0.621
Table 13 L9 OA of CCi with generated S/N ratios and Means
Exp No PRE SOD AFR JTS CCi S/N Ratio MEAN
1 230 2 300 84 0.509145 -5.8632 0.509145
2 230 5 400 147 0.675223 -3.4111 0.675223
3 230 8 500 210 0.539553 -5.3593 0.539553
4 300 2 400 210 0.544465 -5.2806 0.544465
5 300 5 500 84 0.607172 -4.3338 0.607172
6 300 8 300 147 0.042817 -27.367 0.042817
7 370 2 500 147 0.770740 -2.2618 0.770740
8 370 5 300 210 0.802058 -1.9159 0.802058
9 370 8 400 84 0.621040 -4.1376 0.621040
Table 14 Response table of mean S/N ratio for CCi
370300230
-4
-6
-8
-10
-12
852
500400300
-4
-6
-8
-10
-12
21014784
Pressure
MeanofSNratios
SOD
AFR JTS
Main Effects Plot for SN ratios
Data Means
Signal-to-noise: Larger is better
Figure 3 Main effects plot for S/N ratios of CCi
Level Pressure SOD AFR JTS
1 4.878 4.469 11.716 4.778
2 12.327 3.220 4.276 11.014
3 2.772 12.288 3.985 4.185
Delta 9.556 9.068 7.731 6.828
Rank 1 2 3 4

8. G Venkata Kishore and F Anand Raju
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4. RESULTS AND DISCUSSION
Hence simultaneous optimization of AWJM parameters of Inconel 625 has been
successfully carried out and the optimized process parameters are presented below
Table 15 Predicted optimum parameter setting for SR
Parameter Level Description Level
Water Pressure 300 2
Stand of Distance 2 1
Jet traverse speed 147 2
Abrasive flow rate 300 1
Confirmation experiments were conducted with the above parameter setting and a
Surface roughness value of 0.72µm was realised.
Table 16 Predicted optimum parameter setting for MRR
Parameter Level Description Level
Pressure 370 3
Stand of Distance 5 2
Jet traverse speed 84 1
Abrasive flow rate 400 2
Confirmation experiments were conducted with the above parameter setting and a
MRR of 44.32gms/min was realised.
Table 17 Predicted optimum parameter setting for combined SR and MRR
Parameter Level Description Level
Pressure 370 3
Stand of Distance 5 2
Jet traverse speed 210 3
Abrasive flow rate 500 3
Confirmation experiments were conducted with the above parameter setting, SR
and MRR values of 0.83µm and 42.54gms/min are obtained respectively.
4.1. Regression Equations
MRR = 28.8 + 0.0172 Pressure + 0.466 SOD + 0.0134 AFR - 0.0050 JTS
SR = 1.36 - 0.000643 Pressure + 0.0239 SOD - 0.000417 AFR - 0.000079 JTS
CCi = - 0.029 + 0.00112 Pressure - 0.0345 SOD + 0.000939 AFR + 0.00039 JTS
5. CONCLUSION
Among various methods of simultaneous optimization for conventional and non
conventional machining, the method (TOPSIS) was studied and applied in this
research work because of its flexibility with no requirement of calculating complex
domain formulations or simulation of the process with a computer, as these tasks
would require a lot of hardware and time for determining the optimum, via TOPSIS
one can find the optimum solution for a complex problem with very simple statistical
calculations that can be made with a scientific calculator. This approach also gives
much more reliable solutions as exact experimental values are used to represent the
process. The experimental result for optimum setting shows that there is a
considerable improvement in the performance characteristics. Thus the multiobjective

9. Selection of Optimum Parameters for MRR & SR Simultaneously In AWJM of Inconel-625
Alloy Using Topsis Method
http://www.iaeme.com/IJMET/index.asp 9 editor@iaeme.com
approach using TOPSIS with Taguchi approach is capable of solving any type of
optimization problem.
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