I. S. P. SUSHMA et al.: PREDICTING THE OPTIMAL PARAMETERS BY MULTI-OBJECTIVE DECISION MAKING ...
453–458
PREDICTING THE OPTIMAL PARAMETERS BY
MULTI-OBJECTIVE DECISION MAKING WHILE MACHINING AN
Al6061 ALLOY USING CBN INSERTS WITH DIFFERENT
CUTTING-EDGE GEOMETRIES
NAPOVED OPTIMALNIH PARAMETROV MEHANSKE OBDELAVE
Al6061 S CBN VLO@KI IN RAZLI^NO GEOMETRIJO REZALNIH
ROBOV S POMO^JO MODM METODE
I Sri Phani Sushma
1
, G. L. Samuel
1*
, Gyula Varga
2
1
Department of Mechanical Engineering, IIT Madras – 600036
2
Institute of Manufacturing Science, University of Miskolc, 3515 Miskolc, Hungary
Prejem rokopisa – received: 2023-03-21; sprejem za objavo – accepted for publication: 2023-08-16
doi:10.17222/mit.2023.830
The present work aims to investigate the effect of cutting-tool edge geometry on cutting force and surface finish while machin-
ing an Al6061 alloy under different conditions. A series of experiments was performed with a custom-fabricated cutting insert
of a chamfered edge to observe the effect of feed rate and depth of cut on the cutting forces and surface finish. The results
showed that varying the cutting-edge geometry has a significant effect on controlling the cutting forces. Also, as the feed and
depth of cut were reduced (at high cutting speeds), the surface roughness was observed to reduce with the geometry effect. Fur-
thermore, in the present work validation of the experimental results were also performed based on a multi-criteria decision-mak-
ing method called Grey Relational Analysis (GRA). The weighted GRA predicted the optimal combination of machining param-
eters for two different cutting-tool inserts. Finally, the obtained optimal results were compared with the predicted and
experimental values in terms of weighted GRG. The result shows that there was no significant improvement while using the
standard cutting tool, whereas a net improvement of 16.9 % was observed while using the chamfered cutting tool for machining
the Al 6061 alloy.
Keywords: cutting forces, surface roughness, machining Al 6061, GRA
V ~lanku avtorji opisujejo raziskavo vpliva geometrije posnetja robov rezalnega orodja z vlo`ki iz kubi~nega bornitrida (CBN)
na sile rezanja in fini{ povr{ine med mehansko obdelavo aluminijeve zlitine vrste Al6061 pri razli~nih pogojih mehanske
obdelave. Avtorji so izvedli vrsto preizkusov mehanske obdelave z doma izdelanimi rezalnimi vlo`ki z razli~no posnetimi
robovi, da bi lahko opazovali vpliv hitrosti podajanja in globino reza na rezalne hitrosti in gladkost povr{ine. Rezultati so
pokazali, da ima variranje geometrije rezalnih robov pomemben vpliv na kontroliranje rezalnih hitrosti. Zmanj{ala sta se hitrost
odvzema in globina reza pri ve~jih rezalnih hitrostih. Prav tako pa je geometrija vplivala na zmanj{anje hrapavosti povr{ine. V
~lanku opisujejo tudi ovrednotenje eksperimentalnih rezultatov z metodo odlo~itve na osnovi ve~ objektnih kriterijev (MODC;
angl.: Multi Objective Decision Criteria) na osnovi relacijske analize v »sivini« (GRA; angl.: Grey Relational Analysis). S
pomo~jo analize GRA so avtorji napovedali optimalno kombinacijo parametrov mehanske obdelave za dve razli~ni vrsti
rezalnih vlo`kov. Dobljene napovedane vrednosti optimalnih parametrov so avtorji primerjali z eksperimentalnimi glede na
analize GRG (angl.: Grey Rational Grade). Rezultati so pokazali, da ni pri{lo do bistvenih izbolj{av, ~e so uporabili standardne
rezalne vlo`ke. Medtem, ko je uporaba posnetih rezalnih orodji pokazala, da je pri mehanski obdelavi aluminijeve zlitine vrste
Al 6061 pri{lo do 16,9 %-nega neto izbolj{anja.
Klju~ne besede: sile rezanja, povr{inska hrapavost, mehanska obdelava zlitine vrste Al 6061, metoda relacijske analize v
"sivini"
1 INTRODUCTION
Present industrial advancements concentrate on im-
proving the quality of manufactured parts with minimal
energy consumption, thereby minimizing the cost of
manufacturing. The Al 6061 alloy is a widely used mate-
rial in the manufacturing of automobile and aircraft parts
due to its high machinability index, high strength-to-
weight ratio, and excellent corrosion resistance. Carbon
steel was used as the first cutting tool material and nowa-
days after a lot of discussion and studies, many research-
ers use carbide as a cutting material in industry
1
(M.
Rasidi Ibrahim and A. Afiff Latif 2017). The quality of a
manufactured part depends on surface integrity of the
material but using carbide tools, excessive tool wear and
poor surface finish on Al alloys was observed. Ma-
chining Al alloys has consistently created a challenge for
a better quality finish. To overcome this difficulty, re-
searchers have tried high-speed machining operations us-
ing a tool to provide suitable tool life and better surface
finish for Al alloys. Zebala et al.
2
discussed the machin-
ing of sintered carbides using CBN tools with three dif-
ferent nose radii. The cutting forces were used to deter-
mine the equations for the growth of the components,
machining time, and tool wear. The obtained equations
were used to optimize the WC-Co turning process using
Materiali in tehnologije / Materials and technology 57 (2023) 5, 453–458 453
UDK 669.71:621.992.4 ISSN 1580-2949
Original scientific article/Izvirni znanstveni ~lanek MTAEC9, 57(5)453(2023)
*Corresponding author's e-mail:
samuelgl@iim.ac.in (G. L. Samuel)

CBN tools with the maximum metal-removal rate. Li and
Seah
3
and Dabade et al.
4
conducted a turning operation
on AA2124/SiCp composite with PCD/CBN tool. Fur-
ther, Zhijin and Hongjun
5
discussed the optimum geo-
metric parameters of the CBN tool for cutting Al-Si al-
loys. Bhushan et al.
6
studied the influence of cutting
speed, feed rate, and depth of cut on surface roughness
while machining the Al 7075 metal matrix composites
using the PCD tool. Özel, T. K. Hsu et al..
7
investigated
the influence of cutting-edge geometry, work-piece hard-
ness, cutting speed, and feed rate on the resultant force
and surface roughness during the hard turning of AISI
H13 steel. It was found that cutting-edge geometry,
work-piece hardness, cutting speed, and feed rate on the
surface roughness are statistically significant.
Grey relational analysis (GRA) is used to determine
the optimum conditions of various input parameters to
obtain the best quality characteristics. GRA is widely
used for measuring the degree of relationship between
sequences by grey relational grade. In GRA to calculate
the grey relational grade weighted sum of the grey rela-
tional coefficients is required. Weights are calculated us-
ing different methods like entropy, principal component
analysis, etc. Padhy and Singh investigated the optimal
cutting parameters during the dry hard machining of
Inconel 625 with the use of the Taguchi-based Grey rela-
tional analysis method.
8
In the metal-cutting industries, achieving better sur-
face integrity while machining Al 6061 alloy is highly
challenging owing to tool build up edge (BUE) resulting
from the work-material adhesion. Tools with BUE will
generate higher cutting forces and may gradually lead to
catastrophic failure of the tool, effecting the total produc-
tion cycle. This is of greater concern as the current man-
ufacturing industries are moving towards the concept of
sustainable manufacturing. Based on the previous re-
search, it can be observed that many researchers had
worked on machining various materials using different
tools, whereas only a very few researchers have used en-
tropy GRA for determining the optimal machining pa-
rameters. This forms the major objective of the present
work to perform turning experiments and also to use the
entropy GRA method to predict the optimal machining
parameters while turning the Al6061 alloy using a CBN
cutting tool of different cutting-edge geometry. The
weighted sum of the GRA is called the Grey Rational
Grade. But many methods and tools are used to improve
the surface finish while machining the aluminium and
still research is continuing. In the present work the effect
of chamfered PCD tool on the surface finish is investi-
gated at low feed rates. Figure 1 shows the relation be-
tween the input and output responses while turning the
Al 6061 alloy in the current study.
2 EXPERIMENTAL PART
In the current study, a turning operation on Al 6061
was performed using standard CBN tool and chamfered
CBN tool. The experiments were carried out on ACE
Designed W3117 CNC turning machine. The input pa-
rameters were varied during the operation and subse-
quently, the output parameters were determined. An en-
tropy GRA approach was then employed to establish a
correlation between the input variables and their perfor-
mance characteristics. The considered input parameters
and output responses are shown in Figure 1.
2.1 Work material
The material selected for turning operation in this in-
vestigation is the Al 6061 Alloy, and the chemical com-
position of the Al 6061 alloy is given in Table 1.
2.2 Cutting tool
A commercially available standard CBN and
chamfered CBN tool were used for conducting the ma-
chining operation on the Al 6061 alloy. The tool’s shape
is a rhombus with an included angle of 80°, nose radius
of 0.8 mm, edge length of 9 mm, inserts thickness of
4 mm, and shank cross-section of 13 mm × 3 mm. An
SCLCL 2020 K09T3 tool holder is used for holding the
tool.
Table 1: Chemical composition of the Al 6061 alloy
Element
Composition
(%)
Element
Composition
(%)
Al 96.85 Cr 0.25
Mg 0.9 Zn 0.20
Si 0.7 Ti 0.10
Fe 0.60 Mg 0.05
Cu 0.30 Others 0.05
2.3 Experimental Design
To perform the machining operation, a limited num-
ber of experiments were designed using orthogonal ex-
perimental array design. The input parameters such as
cutting speed, feed rate, and depth of cut are considered
for conducting the machining operation on the Al 6061
alloy. Therefore, in the current study, an L9 orthogonal
I. S. P. SUSHMA et al.: PREDICTING THE OPTIMAL PARAMETERS BY MULTI-OBJECTIVE DECISION MAKING ...
454 Materiali in tehnologije / Materials and technology 57 (2023) 5, 453–458
Figure 1: Schematic diagram shows the relation between the input
and output responses

array that has 8 degrees of freedom will provide better
results when selecting the machining parameters. The
machining parameters are assigned in a row of the or-
thogonal array, and the combinations of machining pa-
rameters are nine, which is given in Table 2. The output
variables (responses) considered for the current investi-
gation are the cutting force, thrust force, shear force,
ploughing force, and surface roughness.
Table 2: Input parameters and their levels for machining the Al 6061
alloy
Levels of fac-
tors
Input Parameters
Cutting speed
(v) m/min
Feed rate
(f) mm/rev
Depth of cut
(d)m m
1 314 0.1 0.1
2 565 0.14 0.2
3 785 0.18 0.3
2.4 Experimental procedure
In the present work, the cutting speeds were main-
tained at different ranges of cutting speed from
314 m/min to 785 m/min, by maintaining the feed rates
of 0.1 mm/rev, 0.14 mm/rev and 0.18 mm/rev and depth
of cuts of 0.1 mm, 0.2 mm and 0.3 mm, respectively.
There are two types of side edge chamfer CBN tools,
such as 0 μm and 80 μm used for conducting the machin-
ing operation on the Al 6061 alloy. A three-component,
piezo-electric dynamometer is mounted on the tool post
to measure the various cutting forces: ploughing force,
cutting force, shear force, and thrust force. The output of
the dynamometer signal is amplified by using charge
ampli?ers, which are acquired and sampled by a data-ac-
quisition card and Kistler DynoWare software. By mea-
suring the forces from the dynamometer, the ploughing
force is determined by the equations from the referred
analysis done on the slip-line method. Further, the sur-
face roughness on the machined components was
measured using a Mahr perthometer. It is quantified by
the deviations in the direction of the normal vector of a
real surface from its ideal form. The cut-off length and
sampling length for the measurements are 0.8 mm and
4.0 mm, respectively. Figure 2 shows photographs of the
piezo-electric dynamometer used for the force measure-
ment and the Mahr perthometer used for the sur-
face-roughness measurement, respectively.
2.5 Grey Relational Analysis (GRA)
To conduct the machining process on the Al 6061 al-
loy, nine different experiments were planed using or-
thogonal design analysis. Therefore, in GRA, the above
nine different experiments can be considered as the nine
subsystems. Moreover, the influence of the developed
nine subsystems on the response variables, i.e., cutting
force, thrust force, shear force, ploughing force, and sur-
face roughness, is to be analyzed using the GRA tech-
nique. A grey relational analysis has been utilized to op-
timize the output responses in this study. The multiple
I. S. P. SUSHMA et al.: PREDICTING THE OPTIMAL PARAMETERS BY MULTI-OBJECTIVE DECISION MAKING ...
Materiali in tehnologije / Materials and technology 57 (2023) 5, 453–458 455
Figure 2: a) Machining unit with Force dynamometer, b) surface profile with Mahr Perthometer
Figure 3: Flow chart showing the step-by-step procedure of GRA

characteristic optimizations using GRA was made based
on the previous literature,
8
wherein the main steps are
presented in Figure 3.
The corresponding highest-weighted GRG will pro-
vide the minimum values of the cutting force (CF), thrust
force (TF), shear force (SF), ploughing force (PF), and
surface roughness (SR) based on the systematic analysis.
To minimize the five responses initially, the problem has
been converted into a multi-objective optimization prob-
lem. It is stated as Minimization: f (CF, TF, SF, PF and
SR)”, the five forces considered are the function of input
parameters, ranges of the independent input decision
variables such as, cutting speed denoted as v (m/min);
314 565 785, feed rate represented as f (mm/rev);
0.1 0.14 0.18 and depth of cut indicated as d (mm);
0.1 0.2 0.3. Moreover, it can be observed that the
number of output responses is high, and it is very diffi-
cult to minimize the responses. The multi-objective opti-
mization problem has been converted into a single objec-
tive optimization problem using the GRA technique to
overcome this difficulty in the present study. Further, the
following subsections discussed the step-by-step proce-
dure of the GRA optimization.
9
3 RESULTS
The measured output responses, i.e., cutting force,
thrust force, shear force, ploughing force, and surface
roughness at various machining parameters using stan-
dard CBN cutting tool and chamfered CBN cutting tool,
I. S. P. SUSHMA et al.: PREDICTING THE OPTIMAL PARAMETERS BY MULTI-OBJECTIVE DECISION MAKING ...
456 Materiali in tehnologije / Materials and technology 57 (2023) 5, 453–458
Table 3a: Experiments designed using L
9
orthogonal array for Chamfered CBN cutting tool
No v (m/min) f (mm/rev) d (mm) F
c (N) F
t (N) F
s
(N) F
p (N) Ra (μm)
1 314 0.1 0.1 281 255 81 299 0.446
2 314 0.14 0.2 294 286 96 313 0.434
3 314 0.18 0.3 298 329 83 361 0.532
4 565 0.1 0.1 307 221 131 285 0.514
5 565 0.14 0.2 315 286 156 290 0.486
6 565 0.18 0.3 358 294 124 138 0.458
7 785 0.1 0.1 383 294 145 338 0.581
8 785 0.14 0.2 384 307 160 331 0.479
9 785 0.18 0.3 419 288 229 280 0.517
Table 3b: Experiments designed using L
9
orthogonal array for CBN cutting tool
No v (m/min) f (mm/rev) d (mm) F
c (N) F
t (N) F
s
(N) F
p (N) Ra (μm)
1 314 0.1 0.1 482 373 181 428 0.794
2 314 0.14 0.2 581 434 264 461 0.638
3 314 0.18 0.3 440 341 270 330 0.602
4 565 0.1 0.2 409 357 162 381 0.604
5 565 0.14 0.3 472 410 211 414 0.763
6 565 0.18 0.1 436 427 106 504 0.764
7 785 0.1 0.3 416 353 193 352 0.794
8 785 0.18 0.2 464 394 158 450 0.769
9 785 0.14 0.1 487 427 195 453 0.786
v – cutting speed; f – feed rate; d – depth of cut; F
c
– cutting force; F
t
– thrust force; F
s
– shear force; F
p
– ploughing force; Ra – surface rough-
ness
Figure 4: Surface-roughness profile obtained for the workpiece machined with standard CBN insert at 0.1 mm/rev feed rate, 314 m/min cutting
speed 0.1 mm Depth of cut

are tabulated in Table 3a and 3b, respectively. The de-
tails of the surface roughness measurement is shown in
Figure 4.
The output responses such as cutting force, thrust
force, shear force ploughing force and surface roughness
with respect to each experimental trials are shown in
Figures 5a to 5e. It has been observed that the forces in-
crease as the number of experiments increases in
chamfered CBN cutting tool. Moreover, the surface
roughness slowly increases along with the number of ex-
periments in both and Chamfered CBN cutting tools.
Further, the average of the cutting forces and surface
roughness of CBN and chamfered CBN tools are shown
in Figure 6a and 6b. To conduct the machining opera-
tion on Al 6061 alloy using standard CBN and
Chamfered CBN tools, lower cutting force, thrust force,
shear force and ploughing force are performing better.
Therefore, while evaluating data pre-processing in GRA,
all output responses are considered as the "lower is
better" (LB) and the normalized values of the output re-
sponses of the standard CBN and chamfered CBN cut-
ting tools.
I. S. P. SUSHMA et al.: PREDICTING THE OPTIMAL PARAMETERS BY MULTI-OBJECTIVE DECISION MAKING ...
Materiali in tehnologije / Materials and technology 57 (2023) 5, 453–458 457
Figure 5: Variation in output responses: a) Cutting Force, b) Thrust Force, c) Shear Force, d) Ploughing Force, e) Average output responses tools
vs Weighted ratios of CBN and CCBN tool.
Figure 6: a) Result shows the average output responses tools vs forces, b) Weighted grey relational grade of standard and chamfered CBN tool
with the number of experiments

4 DISCUSSION
Confirmation experiments were also performed three
times and repeated at the optimum level of control pa-
rameters for standard CBN cutting tool (v
1
-f
1
-d
1
) and
chamfered CBN cutting tool v
2
-f
3
-d
1
) to obtain the im-
provement of responses. In practice, providing numerical
relative weights of different decision criteria is difficult,
even for a single decision maker. It is, of course, harder
to obtain parameter weights from several decision mak-
ers. Often, decision-makers are much more comfortable
merely assigning ordinary ranks to the various criteria
that are being considered. In such cases, relative weights
of the criteria can be extracted from the ranks of criteria
given by decision makers. The decision to select an ap-
propriate weighting method is challenging to solve a de-
cision problem with multi-criteria. It has also been found
that there is no improvement between the predicted and
experimental values of the standard CBN cutting tool.
Because the initial machining parameter setting (v
1
-f
1
-d
1
)
and the optimal machining parameter setting (v
1
-f
1
-d
1
)
are the same. In the case of the Chamfered CBN tool,
there is an improvement between the predicted and ex-
perimental values. The initial machining-parameter set-
ting (v
1
-f
1
-d
1
) of the weighted grey relational grade is
0.1564, and the optimal parameter setting is (v
2
-f
3
-d
1
)o f
the weighted grey relational grade 0.1635.
Thus, the percentage improvement of the grey rela-
tional grade of the initial and optimal machining parame-
ters for the standard CBN cutting tool is 0% and that for
the chamfered CBN cutting tool is 15.65 %. It is found
that the chamfered CBN cutting tool is performing better
than the standard CBN cutting tool while obtaining the
optimal machining parameters: cutting force, thrust
force, shear force, ploughing force, and surface rough-
ness using entropy GRA.
5 CONCLUSIONS
In this work, a multi-objective grey relational analy-
sis was proposed to obtain the optimal machining param-
eters. The machined specimens’ responses like cutting
force, thrust force, shear force, ploughing force, and sur-
face roughness were selected as quality targets. A total
nine experiments are designed using the orthogonal array
design of experiments. A multi-objective optimization
problem has been converted to a single-objective optimi-
zation problem using GRA technique to optimize the
output responses. The weights of the grey relational
grade of the standard CBN and Chamfered CBN cutting
tools are obtained from the entropy method. The conclu-
sions of the present study are as follows:
• The process parameters were optimized and the opti-
mal parameter were found to be 314 m/min of cutting
speed, 0.10 mm/rev of feed and 0.1 mm of depth of
cut for the standard CBN cutting tool. Whereas for
the CBN chamfered tool the process parameters were
noted to be 565 m/min of cutting speed, 0.18 mm/rev
of feed and 0.1 mm of depth of cut.
• Optimization analysis showed an improvement in the
value of predicted weighted grey relational grade
from 0.1635 to 0.1812 and an increase in the value of
experimental weighted grey relational grade from
0.0999 to 0.1564 for chamfered CBN tool. This con-
firms an improvement of 15.65 % in machining per-
formance while using optimal parameters of
chamfered CBN tools.
• Furthermore, the experimental analysis revealed a
better machining performance (in terms of cutting
force and surface roughness) while using the
chamfered CBN cutting tool than the standard CBN
cutting tool.
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I. S. P. SUSHMA et al.: PREDICTING THE OPTIMAL PARAMETERS BY MULTI-OBJECTIVE DECISION MAKING ...
458 Materiali in tehnologije / Materials and technology 57 (2023) 5, 453–458