H. AKKUª: OPTIMISING THE EFFECT OF CUTTING PARAMETERS ON THE AVERAGE SURFACE ROUGHNESS ...
781–785
OPTIMISING THE EFFECT OF CUTTING PARAMETERS ON THE
AVERAGE SURFACE ROUGHNESS IN A TURNING PROCESS
WITH THE TAGUCHI METHOD
OPTIMIZIRANJE VPLIVNIH PARAMETROV REZANJA NA
POVPRE^NO POVR[INSKO HRAPAVOST S TAGUCHIJEVO
METODO
Harun Akkuº
Amasya University, Technical Sciences Vocational School, ªeyhcui Mah. Kemal Nehrozoðlu Cad no. 92/B, Amasya 05100, Turkey
harunakkus@windowslive.com
Prejem rokopisa – received: 2018-05-30; sprejem za objavo – accepted for publication: 2018-06-28
doi:10.17222/mit.2018.110
In this study, the AISI 1040 steel was exposed to a turning process. The experiment list was created with the Taguchi L16
experiment design based on the cutting speed, feed rate and depth of cut. The turning experiments were conducted on a CNC
turning machine with a diamond cutter under dry cutting conditions. As a result of the experiments made according to the
experimental design, the average surface-roughness (Ra) values were obtained. Signal-to-noise (S/N) ratios were determined
with the Taguchi analysis within the Minitab package program. According to the results, the feed rate had the most significant
effect on Ra among the three factors. In an ANOVA analysis, the feed rate, depth of cut and cutting speed were effective at the
95 % confidence level at the Ra value. During the repeated experiments carried out for the parameters chosen with the Taguchi
prediction, it was clear that the Taguchi method exhibited a nearly 94 % accuracy.
Keywords: optimization, turning, Taguchi method, surface roughness
Avtorji opisujejo {tudijo procesa stru`enja jekla AISI 1040. Za oblikovanje preizkusa so ustvarili Taguchi-jevo L16 matriko, ki je
temeljila na naslednjih procesnih parametrih: rezalna hitrost, hitrost odvzema materiala in globina reza. Eksperimente so izvajali
na ra~unalni{ko numeri~no kontrolirani (CNC; angl.: Computer Numerical Control) stru`nici z diamantnim no`em v suhih
pogojih rezanja (brez dodatnega mazanja no`a). V skladu z dizajnom eksperimenta so za vsak posamezen preizkus kot rezultat
dobili odgovarjajo~o povpre~no povr{insko hrapavost (Ra). Spekter signalov oz. razmerja signal/{um (S/N; angl: Signal-to-noise
ratios) so dolo~ili s Taguchijevo analizo v programskem paketu Minitab. Rezultati analize so pokazali, da izmed treh
analiziranih procesnih parametrov hitrost odvzema najbolj pomembno vpliva na Ra. ANOVA analiza je pokazala, da je efektivni
nivo zaupanja 95 % za Ra vrednost pri vseh treh izbranih procesnih parametrih; to je hitrosti odvzema, globini reza in hitrosti
rezanja. Ponavljajo~i se eksperimenti za izbrane parametre so pokazali, da je napoved s Taguchijevo metodo pribli`no 94 %
natan~na.
Klju~ne besede: optimizacija, stru`enje, Taguchi, povr{inska hrapavost
1 INTRODUCTION
Due to its machining, abilities to process different
materials, obtain parts with different sizes and
geometries, supply the desired size accuracy and obtain
clean surfaces, the turning process has been used for
years and it has maintained its importance thanks to the
advancing technology.
1
The turning process is an
important branch of machining. It can be described as a
process that creates a cylindrical or rotatory shape of a
material by removing the turnings from a workpiece that
moves circularly.
2
The workpiece constitutes the main
movement by rotating. The cutting depth and the feed
movement are the parameters of a machining method
carried out with a cutting tool. In turning, the impact of
independent variables on dependent variables is one of
the factors that affects the product quality.
3,4
To improve
the product quality, it is important that the parameters
that cause the changes in the dependent variables are
researched and determined.
5
In turning, the parameters
like the cutting speed, feed rate, cutting depth, revolu-
tion, process length, cutting tool type, material, cutting
liquid, etc., represent the independent variables.
6,7
The
impact of these independent variables on the dependent
variables like the surface roughness, abrasiveness, vib-
ration, force, acoustic emission, etc., constitute the focal
point of several researches.
8–11
Detecting the surface roughness of produced parts
and determining the parameters affecting the surface
roughness are important for the machining.
12
Estimating
the surface roughness and assessing the compatibility of
the processing parameters like the feed rate, cutting
speed and cutting depth improve the product quality and
help to obtain the desired surface roughness.
13
In the
researches conducted, the surface roughness decreases
with an increase in the cutting speed; however, it in-
creases with an increase in the feed rate and depth of
cut.
14
In machining, generally, a better surface quality is
obtained at a high cutting speed.
15
However, as a high
cutting speed accelerates the tool wear, the same surface
quality cannot be maintained for a long period of
time.
16,17
Materiali in tehnologije / Materials and technology 52 (2018) 6, 781–785 781
UDK 620.1:681.5.015.23:621.9 ISSN 1580-2949
Original scientific article/Izvirni znanstveni ~lanek MTAEC9, 52(6)781(2018)

High-speed steel, cobalt-based alloys, carburized
carbide, ceramic, polycrystal cubic boron nitride and
polycrystal diamond glass are used as the cutting tool
materials.
2
For different machining applications, diffe-
rent tool materials are required. Before starting a
process, it is important to determine the proper material
and the cutting tool that will fit the material.
18
After the
material and the cutting tool are determined, an experi-
mental design is carried out within the framework of the
parameters that the cutting tool allows.
19
During the
measurement, a calibration of the measuring device is
important for the measurement accuracy. The interpre-
tation of the values obtained from the measurements
made with calibrated instruments is also an important
issue.
20
Statistical methods, an artificial neural network,
fuzzy logic, etc., are used when interpreting the experi-
ment results. The Taguchi method is a statistical method
used commonly in our research.
21
It has a low-cost
development cycle and it is a problem-solving tool that
can enhance the product performance in the system and
process design. On the other hand, the Taguchi method is
a problem-solving process involving experimental and
analytical concepts that determine the most effective
parameters so that the results can provide significant im-
provements for the overall performance. For this reason,
the Taguchi method uses a special design of orthogonal
arrays for a study involving a process with few experi-
ments.
22
In this study, the effect of the cutting parameters on
the Ra values was researched experimentally using the
Taguchi L
16
orthogonal arrays in a turning process. The
optimal cutting parameters were determined with the
Taguchi analysis. Then random experiments were carried
out with randomly selected parameters for the prediction.
Certain percent errors between the estimation experiment
and the Taguchi estimation were calculated. The results
obtained are good.
2 EXPERIMENTAL PART
The cutting parameters were selected according to
the cutting-tool manufacturer’s catalog. Four levels of
cutting speed (V), feed rate (f) and depth of cut (a) were
determined in the experiments. The processing condi-
tions for the present experimental study are shown in
Table 1. Figure 1 shows the cutting insert and tool
holder used in the experiments. The test scheme is shown
in Figure 2.
Considering the full factorial design based on the
total cutting parameters specified, 64 experiments would
have to be carried out. However, it would have been
appropriate for us to have a small number of experiments
to save the time and costs. Therefore, the L
16
orthogonal
array was designed with the Taguchi method. The rough-
ness device was calibrated before the measurements to
allow for the measurement accuracy. The experiments
were done in five replicates, and then the average of
these replicates was taken. The tip used had four corners.
A corner was used for each experiment. In Table 2, the
experimental list generated and the average Ra values
obtained with the measurement are given.
Table 1: Determination of the conditions for the turning process
Number Machining conditions Descriptions
1 Workpiece AISI 1040
2 Workpiece hardness 46 HRc
3 Workpiece dimensions Ø80 × 135 mm
4 Processing size 50 mm
5 Lathe machine
ACE Micromatic
Designers LT-20C
6 Cutting speed (m/min) 200-225-250-275
7 Feed rate (mm/rev) 0.15, 0.25, 0.35, 0.45
8 Depth of cut (mm) 1.5, 2.5, 3.5, 4.5
9 Cutting medium Dry
10 Inserts
Korloy DNMG 150608
diamond insert
11 Tool holder Teknik DDJNR 2525M15
12 Measurement value
Average surface
roughness (Ra)
13
Roughness measuring
device
Mitutoyo Surftest SJ-210
14 Hardness tester BMS Digirock RSR
15 Used programs Minitab, Excel
16 Evaluation of results Taguchi
H. AKKUª: OPTIMISING THE EFFECT OF CUTTING PARAMETERS ON THE AVERAGE SURFACE ROUGHNESS ...
782 Materiali in tehnologije / Materials and technology 52 (2018) 6, 781–785
Figure 2: Experimental scheme Figure 1: Cutting insert and tool holder

Table 2: L
16
design and results
Experimental
number
V
(m/min)
f
(mm/rev)
a
(mm)
Ra
(μm)
1 200 0.15 1.5 1.130
2 200 0.25 2.5 2.715
3 200 0.35 3.5 4.938
4 200 0.4 4.5 7.695
5 225 0.15 2.5 1.316
6 225 0.25 1.5 2.659
7 225 0.35 4.5 5.183
8 225 0.4 3.5 7.444
9 250 0.15 3.5 0.940
10 250 0.25 4.5 2.682
11 250 0.35 1.5 4.749
12 250 0.4 2.5 7.252
13 275 0.15 4.5 1.494
14 275 0.25 3.5 2.744
15 275 0.35 2.5 4.719
16 275 0.4 1.5 7.048
3 RESULTS
In the Taguchi analysis, the smaller-the-better
signal-to-noise ratio was chosen to determine the best
surface-roughness value. The S/N ratios and level values
obtained with the analysis are given in Table 3.
Table 3: S/N values determined with the Taguchi method
Experimental
number
Cutting
speed
Feed
rate
Depth of
cut
S/N
1 200 0.15 1.5 –1.06
2 200 0.25 2.5 –8.66
3 200 0.35 3.5 –13.87
4 200 0.4 4.5 –17.73
5 225 0.15 2.5 –2.41
6 225 0.25 1.5 –8.50
7 225 0.35 4.5 –14.29
8 225 0.4 3.5 –17.43
9 250 0.15 3.5 0.54
10 250 0.25 4.5 –8.56
11 250 0.35 1.5 –13.53
12 250 0.4 2.5 –17.21
13 275 0.15 4.5 –3.46
14 275 0.25 3.5 –8.76
15 275 0.35 2.5 –13.48
16 275 0.4 1.5 –16.96
The most basic criterion specified with the Taguchi
method is the signal/noise (S/N) value. The Taguchi me-
thod takes into account the high S/N value to determine
the optimum cutting parameters. According to this S/N
ratio, in the L
16
orthogonal sequence, the most optimal
cutting parameters from Table 3 are determined as 0.54
S/N for Ra, V = 250 m/min and f = 0.15 mm/rev; when
a = 3.5 mm, the lowest Ra value is obtained. Table 5
shows the level values of the factors. Figure 3 shows a
graph of the level values. For the experiments to be
performed under the same conditions, the interpretation
can be made according to the level values of the cut-off
parameters shown in Table 4 and Figure 3 in order to
determine the optimum cutting conditions. The third
level of the cutting-speed factor, the first level of the
feed-rate factor, and the third level of the depth-of-cut
factor are high as shown in Figure 3 and Table 4. There-
fore, the optimum cutting conditions determined under
the same conditions for the experiments to be carried out
are V = 250 m/min, f = 0.15 mm/rev and a = 3.5 mm. In
Table 4, the cutting parameters used in the experiments
show that the surface roughness is most affected by the
feed rate.
Table 4: S/N result table
Level Cutting speed Feed rate Depth of cut
1 –10.331 –1.6 –10.014
2 –10.657 –8.619 –10.439
3 –9.691 –13.793 –9.881
4 –10.665 –17.333 –11.011
Delta 0.974 15.733 1.13
Rank 3 1 2
When the optimum cutting parameters were deter-
mined with the Taguchi method, it was determined
whether there was a relation between the cutting
parameters and the variance analysis. The relations of the
S/N with the cutting speed, feed rate and depth of cut
were assessed. ANOVA-analysis results are shown in
Table 5. According to the ANOVA results, the signi-
ficance level must be p<0.01 or p<0.05. According to
these results, the most significant value order is the feed
rate, depth of cut and cutting speed. The cutting speed,
feed rate and cutting depth were effective at the 95-%
confidence level. At the same time, these results confirm
the order of importance shown in Table 4.
The Taguchi method aims at lowering the number of
experiments and acquiring reliable results in a short
period. The estimation experiments carried out in this
study did not take the time and cost into consideration;
they were carried out to prove that the Taguchi esti-
mation was close to the real values. In the last stage of
the Taguchi analysis, estimations were made for the
H. AKKUª: OPTIMISING THE EFFECT OF CUTTING PARAMETERS ON THE AVERAGE SURFACE ROUGHNESS ...
Materiali in tehnologije / Materials and technology 52 (2018) 6, 781–785 783
Figure 3: Factor level’s Ra graphic according to S/N ratios

levels given in Table 6. This table lists the results of the
Taguchi estimation, experimental results and the absolute
errors between them given in percentages. In light of
these results, the Taguchi estimation is 94-% correct.
4 CONCLUSIONS
In this study, an analysis of the Ra values was made
for the optimization of a turning process using the
Taguchi experiment design. The results of the study are
given below.
For the four different levels of the cutting speed, feed
rate and depth of cut that were the cutting factors, an L
16
orthogonal array was obtained within the MINITAB
program using the Taguchi method. Thanks to this,
instead of 64 full factorial experiments, 16 experiments
were carried out. As the result of the experiments carried
out according to the L
16
orthogonal array, the S/N ratio of
Ra was found. Using the smaller-the-better S/N ratio
equation, the maximum value of the S/N ratio was
determined. The maximum S/N ratio allowed the most
optimal cutting parameters. For the lowest surface
roughness in the turning operation, the optimal cutting
conditions that corresponded to the maximum Ra value
of 0.54 S/N were a cutting speed of 250 m/min, a feed
rate of 0.15 mm/rev and a depth of cut of 3.5 mm.
By applying the analysis of variance to the S/N ratios,
the relation levels of the cutting parameters for Ra were
determined. According to the ANOVA analysis, the feed
rate, depth of cut and cutting speed were effective at the
95 % confidence level at the Ra value.
By comparing the Taguchi estimation with the expe-
riment result, it was determined that the Taguchi estima-
tion was approximately 94 % correct.
For the subsequent studies, different materials, cutt-
ing tools, process methods, tool wears, cutting forces,
vibration and acoustic-emission measurements are
advised.
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784 Materiali in tehnologije / Materials and technology 52 (2018) 6, 781–785
Table 5: Interaction between S/N and cutting speed, feed rate and depth of cut
Source DF Seq SS Adj SS Adj MS F P
Cutting speed 3 0.1531 0.1531 0.051 3.15 0.108
Feed rate 3 86.0494 86.0494 28.6831 1771.48 0.000
Depth of cut 3 0.2879 0.2879 0.096 5.93 0.032
Residual error 6 0.0972 0.0972 0.0162
Total 15 86.5876
Table 6: Absolute error (%) between the predicted results and experimental results of Ra
Cutting speed
(m/min)
Feed rate
(mm/rev)
Depth of cut
(mm)
Experimental Ra
(μm)
Taguchi estimated
Ra (μm)
Absolute
difference
Absolute error
(%)
200 0.15 4.5 1.28 1.32 0.04 3.34
250 0.25 2.5 2.57 2.73 0.16 6.06
225 0.35 1.5 4.63 4.86 0.22 4.84

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