ª. ÇETIN, T. KIVAK: OPTIMIZATION OF THE MACHINING PARAMETERS FOR THE TURNING ...
133–140
OPTIMIZATION OF THE MACHINING PARAMETERS FOR THE
TURNING OF 15-5 PH STAINLESS STEELS USING THE TAGUCHI
METHOD
UPORABA TAGUCHI METODE ZA OPTIMIZACIJO PARAMETROV
OBDELAVE PRI STRU@ENJU NERJAVNEGA JEKLA 15-5 PH
ªerif Çetin1, Turgay Kývak2
1Düzce University, Institute of Science and Technology, Department of Manufacturing Engineering, 81620, Düzce, Turkey
2Düzce University, Faculty of Technology, Department of Manufacturing Engineering, 81620, Düzce, Turkey
turgaykivak@duzce.edu.tr
Prejem rokopisa – received: 2016-01-05; sprejem za objavo – accepted for publication: 2016-01-27
doi:10.17222/mit.2016.007
The present study investigated the effects of the control factors of cutting speed, feed rate and depth of cut on the response
variables of cutting force (Fc) and surface roughness (Ra) in the dry turning of 15-5 PH martensitic stainless steel. Using PVD
TiAlN-AlCrO- and CVD TiCN-Al2O3-TiN-coated carbide-cutting-tool inserts, a number of turning experiments were conducted
via the L18 (21×33) Taguchi mixed orthogonal array. The machining parameters were optimized using signal-to-noise ratio (S/N)
and analysis of variance (ANOVA). Additionally, empirical models were created for predicting the Fc and Ra using
multiple-regression analysis. The results indicated that depth of cut was the most significant cutting parameter affecting Fc,
while the feed rate contributed the most to Ra. The developed quadratic regression model, showing a high determination
coefficient of 0.981 for Fc and 0.988 for Ra, accurately explained the relationship between the response variable and the control
factors.
Keywords: Taguchi method, turning, cutting force, surface roughness, 15-5 PH steel
[tudija prou~uje vplive kontrolnih faktorjev kot so: hitrost rezanja, hitrost podajanja in globina rezanja na spremenljivki odziva:
silo rezanja (Fc) in hrapavost povr{ine (Ra) pri suhem stru`enju martenzitnega nerjavnega jekla 15-5 PH. Z uporabo PVD
TiAlN-AlCrO- in CVD TiCN-Al2O3-TiN-nanosov na karbidnih vlo`kih za rezanje, so bili izvedeni {tevilni preizkusi stru`enja s
pomo~jo me{ane L18 (21×33) Taguchi ortogonalne matrike. Parametri obdelave so bili optimirani z uporabo razmerja signala
{uma (S/N) in analizo variance (ANOVA). Dodatno so bili postavljeni empiri~ni modeli za napovedovanje Fc in Ra z uporabo
razli~nih regresijskih analiz. Rezultati so pokazali, da je globina reza najpomembnej{i parameter rezanja, ki vpliva na Fc,
medtem ko hitrost podajanja najve~ prispeva k Ra. Razviti kvadratni regresijski model, ki ka`e dolo~en visok koeficient 0,981 za
Fc in 0,988 za Ra, natan~no pojasni razmerje med spremenljivko odziva in kontrolnimi faktorji.
Klju~ne besede: Taguchi metoda, stru`enje, sila rezanja, hrapavost povr{ine, jeklo 15-5 PH
1 INTRODUCTION
15-5 PH is a precipitation-hardened martensitic stain-
less steel that has the characteristics of high strength,
good corrosion resistance, excellent weldability, low dis-
tortion and good mechanical properties at temperatures
up to 600 °F (316 °C).1–3 15-5 PH stainless steel is
widely used in the chemical, nuclear, aerospace, paper,
food processing, petrochemical and general metalwork-
ing industries.4–5 Stainless steel is one of the materials
that exhibits poor machinability. The greatest difficulty
in the machining of these materials is poor surface quali-
ty and a short tool life.6 Especially after precipitation
hardening, the increase in hardness and mechanical
properties makes machinability even more difficult.
However, only a few reports exist concerning the
machining of precipitation-hardened martensitic stainless
steel.
In the aviation and automotive sectors, the turning
operation is one of the commonly used metal-cutting
methods for industrial component manufacturing. In
industries where accuracy and measurement integrity are
important, ways to improve the surface quality and lower
the costs of lathed components have become perpetual
subjects of investigation.7 Furthermore, surface rough-
ness plays a significant role in the development and
specification of the surface qualities of the produced
components. When the surface-roughness value falls
within the desired limits, it directly affects the material,
energy and labor costs. In addition, a good surface quali-
ty provides significant improvements in the tribological
properties, fatigue strength, corrosion resistance and
aesthetic appearance of the finished product.8–10 One of
the most important factors in production is energy con-
sumption. The power consumed during machining is the
factor that determines the energy consumption. Apart
from the material factor (in terms of kW), the necessary
power for the turning operation depends on the cutting
depth, feed rate and cutting speed. Depending on the
specific cutting resistance of the material with the other
factors, the main cutting force (Fc) needed during ma-
chining is one of the most important parameters deter-
Materiali in tehnologije / Materials and technology 51 (2017) 1, 133–140 133
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967-2017) – 50 LET/50 YEARS
UDK 67.017:669.018.23:621.9 ISSN 1580-2949
Original scientific article/Izvirni znanstveni ~lanek MTAEC9, 51(1)133(2017)
mining the power consumption for machining and con-
sequently the energy cost.11 There is a strong relation
between the cutting force and the surface roughness,
accuracy of the workpiece and tool wear.12 There are a
number of parameters, including cutting speed, feed,
cutting depth and chip angle, that affect the cutting force
and surface roughness.13–14 Suitable machining para-
meters must be determined in order to obtain low costs
and high-quality products. For this purpose, several opti-
mization methods and techniques are being used, one of
which is the Taguchi experimental design method, which
has been successfully applied in the solution of optimi-
zation problems.15-16
D. P. Selvaraj et al.17 utilized the Taguchi method to
define the optimal cutting parameters in the turning of
two different grades of duplex stainless steel. The
turning operations were carried out with TiC- and
TiCN-coated carbide-cutting-tool inserts. The effects of
cutting speed and feed rate on the cutting force, surface
roughness and tool wear were analyzed and the results
showed that the feed rate was the most significant
parameter influencing the surface roughness and cutting
force. The cutting speed was identified as the most signi-
ficant parameter influencing the tool wear. C. Campo-
seco-Negrete18 conducted an experimental study to
optimize machining parameters during the turning of
AISI 6061 T6. The machining parameters were
optimized using orthogonal array, S/N and ANOVA. The
results showed that feed rate was the most dominant
factor affecting the energy consumption and surface
roughness. A. J. Makadia and J. I. Nanavati10 used res-
ponse surface methodology (RSM) to determine the
optimal cutting parameters for surface roughness in the
turning of AISI 410 steel. The developed prediction
model showed that the feed rate was the most significant
factor on the surface roughness, followed by the tool
nose radius. The surface roughness values were found to
increase with increasing feed rate and to decrease with
increasing tool nose radius. C. Ezilarasan et al.19
experimentally investigated and analyzed the machining
parameters while turning Nimonic C-263 alloy, using
Taguchi’s experimental design. The cutting parameters
considered were cutting speed, feed rate and depth of
cut. The response variables of cutting force, tool wear
and surface roughness were measured. Optimized cutting
parameters were observed at 210 m/min cutting speed,
0.05 mm/r feed rate and 0.50 mm depth of cut. A.
Hasçalýk and U. Çaydaº20 studied the turning operation
of Ti-6Al-4V alloy and broadly examined the effects of
cutting parameters on surface roughness and tool life by
using the Taguchi method. An orthogonal array, S/N
ratio, and ANOVA were employed to investigate the
performance characteristics in the turning of commercial
Ti-6Al-4V. The feed rate was found to be the most
important factor on the surface roughness while cutting
speed was the main factor that had a significant effect on
tool life.
Upon examining studies in the literature, it can be
seen that the Taguchi method has been successfully used
in the optimization of machining parameters. On the
other hand, the machinability of stainless steels is be-
coming a subject of perpetual investigation because of
their wide usage and hard machinability characteristics.
However, in recent years, very few studies have been
conducted on the machinability of precipitation-hard-
ened stainless, which, due to their excellent mechanical
characteristics, are used especially in the aviation indu-
stry. Consequently, in this study, the aim was to optimize
the machining parameters affecting the cutting force and
surface roughness during the dry turning of 15-5 PH
stainless steel using coated cementite-carbide tools. To
this purpose, an experimental design employing a Ta-
guchi L18 orthogonal array was created and analytical
models were developed using regression analysis in
order to estimate Fc and Ra. In addition, ANOVA was
applied to determine the effect levels of the machining
parameters.
2 EXPERIMENTAL PART
2.1 Machine tool and workpiece material
The experimental investigation was carried out using
a Johnford TC 35 lathe with a maximum spindle speed
of 3500 min–1 and a 10-kW drive motor. The workpiece
material used for experimentation was 15-5 precipi-
tation-hardened (PH) martensitic stainless steel with a
hardness of 42 HRC, in the form of round bars, 40 mm
in diameter and 300 mm in length. The chemical com-
position and mechanical properties of the test samples
are given in Tables 1 and 2, respectively. The precipi-
tation-hardening treatment consisted of solutionizing at
1040 °C and air quenching, followed by a final tem-
pering at 550 °C for 4 h (H1025 condition). The typical
microstructures in the etched condition of the 15-5 PH
stainless steel are shown in Figure 1. It can be clearly
seen that the microstructure of the 15-5 PH precipi-
ª. ÇETIN, T. KIVAK: OPTIMIZATION OF THE MACHINING PARAMETERS FOR THE TURNING ...
134 Materiali in tehnologije / Materials and technology 51 (2017) 1, 133–140
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967-2017) – 50 LET/50 YEARS
Figure 1: Microstructure of the precipitation-hardened 15-5 stainless
steel
Slika 1: Mikrostruktura izlo~evalno utrjenega nerjavnega jekla 15-5
tation-hardened stainless steel shows lath martensite and
fine carbides.
Table 1: Chemical composition of 15-5 PH martensitic stainless steel
(w/%)
Tabela 1: Kemijska sestava martenzitnega nerjavnega jekla 15-5 PH
(w/%)
Ni Cu Cr C Si Mn P S Nb Ta
5.08 3.53 15.02 0.07 0.338 0.791 0.022 0.004 0.324 0.15
Table 2: Mechanical properties of 15-5 PH martensitic stainless steel
Tabela 2: Mehanske lastnosti martenzitnega nerjavnega jekla 15-5 PH
Tensile
strength (MPa)
Yield strength
(MPa)
Percentage
elongation
(%)
Hardness
(HRC)
1202 1172 12 42
2.2 Cutting tools and holder
In the turning tests, commercially available PVD and
CVD multilayer-coated carbide inserts (Sandvik Coro-
mant – ISO CNMG 120408) were employed. The pro-
perties of the cutting tools and coating materials are
given in Table 3. A right-hand style mechanical tool
holder (ISO PSBNR 2525M12) was used for mounting
the inserts. For each experimental parameter, a fresh
cutting edge was utilized. The turning experiments were
performed using two different cutting tools (PVD and
CVD), three cutting speeds (150, 200, and 250) m/min
and three feed rates (0.1, 0.2 and 0.3) mm/r.
Table 3: Properties of cutting tools and coating materials
Tabela 3: Lastnosti rezilnih orodij in nanosov
Coated
method
Coated
materials
Material
quality of
ISO (Grade)
Coating
thickness
(μm)
Hardness
(HV)
PVD TiAlN/AlCrO GC1125 4 1640
CVD TiCN/Al2O3/TiN GC2015 5.5 1500
2.3 Cutting force measurement
During turning, the cutting force (Fc) was measured
by using a three-component piezoelectric dynamometer
(KISTLER – 9257B). The signals of Fc from the dyna-
mometer were transmitted to a Kistler 5070-A multi-
channel amplifier. Dynoware software was used for the
data-acquisition system of the machine. The experimen-
tal setup and cutting force measurements are shown in
Figure 2.
2.4 Surface-roughness measurement
The average surface roughness (Ra) of the workpiece
was measured on a MAHR Perthometer M1 portable
surface-roughness device. Cut-off and evaluation lengths
for the surface roughness measurements were selected as
0.8 mm and 5.6 mm, respectively. Before the measure-
ments of Ra, the measuring device was calibrated utiliz-
ing a standard calibration specimen. The measurements
were taken at three locations (120° apart) around the
circumference of the workpieces in order to minimize
the deviation, and the average values were reported. The
surface roughness measurements are given in Figure 3.
2.5 Taguchi’s design of experiments
Taguchi’s Design of Experiments provides a simple,
efficient and systematic approach for determining the
optimum machining parameters in the manufacturing
process.21,22 The Taguchi method significantly decreases
the number of tests needed and increases the machining
performance by using orthogonal arrays.23,24 For the
realization of the experimental design, first of all, the
control factors and their levels must be determined. In
this study, the cutting tools (PVD and CVD), cutting
speed (V), feed rate (f) and depth of cut (ap) were deter-
mined as the control factors. The cutting tool was desig-
nated in two levels, whereas the other control factors
were in three levels. The control factors and their levels
are indicated in Table 4.
ª. ÇETIN, T. KIVAK: OPTIMIZATION OF THE MACHINING PARAMETERS FOR THE TURNING ...
Materiali in tehnologije / Materials and technology 51 (2017) 1, 133–140 135
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967-2017) – 50 LET/50 YEARS
Figure 3: Surface-roughness measurement
Slika 3: Merjenje hrapavosti povr{ine
Figure 2: Experimental configuration for the measuring of cutting
force
Slika 2: Eksperimentalni sestav za merjene sile rezanja
Table 4: Control factors and their levels
Tabela 4: Kontrolni faktorji in njihovi nivoji
Control factors Symbol Level 1 Level 2 Level 3
Cutting tool A PVD CVD -
Cutting speed (m/min) B 150 200 250
Feed rate (mm/r) C 0.1 0.2 0.3
Depth of cut (mm) D 0.5 1 1.5
The full-factorial experimental design required a
large number of 54 (21×33) tests, along with increased
costs. Thus, Taguchi’s experimental design was able to
provide better results with fewer tests. For the specified
control factors and their levels, L18 was selected as the
most suitable orthogonal array (Table 4). Consequently,
the number of tests was decreased significantly and only
18 tests were made. The Taguchi method uses a loss
function to determine the quality characteristics and the
values of this loss function are also converted to a S/N
ratio. In analyzing S/N ratios, three different quality cha-
racteristics are used: the "lower-the-better", the "higher-
the-better" and the "nominal-the-best". Minimization of
the Fc and Ra values was the main purpose of this study.
For this reason, the "lower-the-better" quality characte-
ristic was used for Fc and Ra.
"Lower is the better" characteristics (minimization):
S/N
n
y i
i
n
=− ⎡
⎣⎢
⎤
⎦⎥=
∑10 1 2
1
lg (1)
In the Equation (1), yi is the observed data at the i-th
experiment and n is the number of observations.
3 ANALYSIS AND DISCUSSION
3.1 Analysis of the S/N ratio and ANOVA results
The S/N ratio is defined as the ratio of the average for
the standard deviation. This ratio is used to measure the
deviation of quality characteristics from the desired
value. In this study, Fc and Ra were specified as the
quality characteristics. Lower Fc and Ra values are
important from the point of view of energy consumption
and product quality. Therefore, the "lower-the-better"
equation was used for the calculation of the S/N ratio.
The test results of the Fc and Ra and the corresponding
S/N ratios are given in Table 5. The averages of the Fc
and Ra values obtained from the experimental study were
calculated as 447.67 N and 1.97 μm, respectively.
The S/N response table was used for the analysis of
the effects of the control factors (Ct, V, f and ap) on the
Fc and Ra. The S/N response table for Fc and Ra is given
in Table 6. The highest S/N ratios in the table show the
optimum levels. A graphical representation of variations
in the S/N ratios depending on the Fc and Ra control
factors is given in Figures 4 and 5. The optimum levels
for Fc and Ra were determined as A2B3C1D1 and
A2B2C1D1, respectively. Thus, the optimum cutting-
force value was obtained at 250 m/min cutting speed, 0.1
mm/rev feed rate and 0.5 mm cutting depth using the
ª. ÇETIN, T. KIVAK: OPTIMIZATION OF THE MACHINING PARAMETERS FOR THE TURNING ...
136 Materiali in tehnologije / Materials and technology 51 (2017) 1, 133–140
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967-2017) – 50 LET/50 YEARS
Table 5: Experimental results and S/N ratios values
Tabela 5: Rezultati eksperimentov in vrednosti razmerja S/N
Exp. No.
Control factors
Cutting force,
Fc (N)
S/N ratio
for Fc
(dB)
Surface
roughness,
Ra (μm)
S/N ratio
for Ra
(dB)
A
Cutting tool
(Ct)
B
Cutting speed
(V)
C
Feed rate
(f)
D
Depth of cut
(ap)
1 PVD 150 0.1 0.5 195 -45.80 0.71 2.97
2 PVD 150 0.2 1.0 452 -53.10 1.99 -5.98
3 PVD 150 0.3 1.5 927 -59.34 3.97 -11.98
4 PVD 200 0.1 0.5 183 -45.25 0.42 7.54
5 PVD 200 0.2 1.0 460 -53.26 1.51 -3.58
6 PVD 200 0.3 1.5 912 -59.20 3.48 -10.83
7 PVD 250 0.1 1.0 308 -49.77 1.89 -5.53
8 PVD 250 0.2 1.5 627 -55.95 1.94 -5.76
9 PVD 250 0.3 0.5 357 -51.05 3.22 -10.16
10 CVD 150 0.1 1.5 411 -52.28 1.06 -0.51
11 CVD 150 0.2 0.5 273 -48.72 1.67 -4.44
12 CVD 150 0.3 1.0 656 -56.34 3.86 -11.73
13 CVD 200 0.1 1.0 277 -48.85 0.61 4.29
14 CVD 200 0.2 1.5 687 -56.74 1.53 -3.69
15 CVD 200 0.3 0.5 356 -51.03 3.08 -9.77
16 CVD 250 0.1 1.5 397 -51.98 0.57 4.88
17 CVD 250 0.2 0.5 268 -48.56 0.88 1.11
18 CVD 250 0.3 1.0 312 -49.88 3.12 -9.88
TFc (cutting force total mean value) = 447.67 N
TFc-S/N (cutting force S/N ratio total mean value) = -52.06 dB
TRa (surface roughness total mean value) = 1.97 μm
TRa-S/N (surface roughness S/N ratio total mean value) = -4.06 dB
CVD-coated tool, whereas the optimum Ra was obtained
at 200 m/min cutting speed, 0.1 mm/rev feed rate and 0.5
mm cutting depth, also by using the CVD-coated tool.
Table 7: Results of ANOVA for cutting force and surface roughness
Tabela 7: Rezultati ANOVA za silo rezanja in hrapavost povr{ine
Variance
source
Degree of
freedom
(DoF)
Sum of
squares
(SS)
Mean
square
(MS)
F ratio
Contribu-
tion rate
(%)
Fc
A 1 34148 34148 5.33 3.96
B 2 43599 21799 3.40 5.05
C 2 256557 128278 20.01 29.74
D 2 464230 232115 36.21 53.82
Error 10 64098 6410 - 7.43
Total 17 862632 - - 100
Ra
A 1 0.4214 0.4214 3.78 1.74
B 2 0.5862 0.2931 2.63 2.41
C 2 21.2808 10.6404 95.38 87.64
D 2 0.8777 0.4388 3.93 3.61
Error 10 1.1156 0.1116 - 4.59
Total 17 24.2816 - - 100
ANOVA was used for the purpose of determining the
effects of the control factors (Ct, V, f and ap) of the
experimental design on Fc and Ra. The ANOVA analysis
was performed with a 5 % significance level and 95 %
confidence level. The ANOVA results for the Fc and Ra
are shown in Table 7. In the determination of the signi-
ficance levels of the control parameters, F ratio values
were used, and from this, the contribution ratios of the
machining parameters were calculated. The most effec-
tive factor on the Fc was revealed to be the depth of cut
(factor D) with a 53.82 % contribution ratio, whereas on
the Ra, the most effective factor was the feed rate (factor
C) with a contribution ratio of 87.64 %. Figure 6 shows
the percentage contributions of the control factors on Fc
and Ra. The error percentages were extremely low at
7.43 % and 4.59 %, respectively, for Fc and Ra.
3.2 Evaluation of the experimental results
The variations in the cutting forces obtained during
the experimental study for precipitation-hardened 15-5
PH steel are given in the graphs of Figure 7. The cutting
force exhibited a slight decrease with the increasing
cutting speed. It is thought that the increasing cutting
speed caused a decrease in the tool-chip contact area,
resulting in a decrease in the cutting forces.25 From the
point of obtaining lower cutting force values, the CVD
TiCN-Al2O3-TiN-coated tool showed some advantage
over the PVD TiAlN-AlCrO-coated tool (Figure 7a).
Cutting depth and feed rate had a significant effect on the
cutting force (Figure 7b). Increase in the feed rate and
ª. ÇETIN, T. KIVAK: OPTIMIZATION OF THE MACHINING PARAMETERS FOR THE TURNING ...
Materiali in tehnologije / Materials and technology 51 (2017) 1, 133–140 137
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967-2017) – 50 LET/50 YEARS
Figure 5: S/N graph for surface roughness
Slika 5: S/N diagram za hrapavost povr{ine
Figure 4: S/N graph for cutting force
Slika 4: S/N diagram za silo rezanja
Table 6: S/N response table for Fc and Ra control factors
Tabela 6: Tabela S/N odgovora za Fc in Ra kontrolna faktorja
Levels
Cutting force (Fc) Surface roughness (Ra)
A B C D A B C D
Level 1 -52.52 -52.60 -48.99 -48.40 -4.811 -5.276 2.275 -2.125
Level 2 -51.60 -52.39 -52.72 -51.87 -3.304 -2.675 -3.723 -5.400
Level 3 - -51.20 -54.47 -55.91 - -4.222 -10.724 -4.647
Delta 0.93 1.40 5.49 7.51 1.506 2.601 12.999 3.275
Figure 6: Percentage contribution of the control factors for cutting
force and surface roughness
Slika 6: Prispevek kontrolnih faktorjev v odstotkih na silo rezanja in
hrapavost povr{ine
depth of cut values increased the cutting force consider-
ably. After precipitation hardening, the hardness of the
material reached a value of 42 HRC and its resistance
was increased against the penetration of the cutting tool.
In addition to the increase in hardness, the martensite
formation in the material structure following the heat
treatment played an important role in the increasing
cutting forces. Consequently, with the increase in Fc, the
cutting depth was more effective than the feed rate.
The variations in surface roughness obtained in the
experimental study for precipitation-hardened 15-5 PH
steel are given in the graphs of Figure 8. Especially in
the tests made with the CVD-coated tool, the Ra values
decreased with increasing cutting speed. Although the
application of different coatings had no effect on surface
roughness, lower and Ra values were obtained with the
CVD-coated tool. At a feed rate of 0.2 and 0.3 mm/r,
increasing cutting depth caused the Ra values to increase
(Figure 8b). Therefore, it is possible to say that among
the parameters tested, the feed rate was the most effec-
tive parameter on the Ra. Since the Ra is a function of the
feed rate, the increased values increased the Ra signi-
ficantly. Moreover, the results of variance analysis also
verified that feed rate, with a contribution rate of 87.64
%, was the most effective parameter on Ra. The cutting
speed increasing in a way parallel to the cutting forces
caused a decrease in the Ra values. Furthermore, the in-
creasing cutting depth, especially at higher feed rates,
caused the Ra values to increase slightly. As was the case
with the cutting forces, with the surface roughness, the
CVD TiCN-Al2O3-TiN-coated tool also displayed some
advantage over the PVD TiAlN-AlCrO-coated tool. This
can be explained by the lower frictional coefficient of the
TiN coating on the uppermost layer of the CVD coating.
3.3 Empirical models and prediction performance
For the purpose of defining the relationship between
one dependent variable and one or more independent
variables, regression analysis was used.26 In this study,
dependent variables were defined as Fc and Ra, whereas
the independent variables were specified as the cutting
tool, cutting speed, feed rate and cutting depth. Estima-
tion equations for Fc and Ra were established for linear
and quadratic regression models separately. The estima-
tion equations obtained for the linear-regression model
of Fc and Ra are given in Equations (2) and (3), whereas
the estimation equations obtained for the quadratic
regression model are given in Equations (4) and (5).
Fc Ct
V f ap
l = − −
− + +
113667 871111
1075 14575 388167
.
. . .
(2)
ª. ÇETIN, T. KIVAK: OPTIMIZATION OF THE MACHINING PARAMETERS FOR THE TURNING ...
138 Materiali in tehnologije / Materials and technology 51 (2017) 1, 133–140
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967-2017) – 50 LET/50 YEARS
Figure 7: Effect of cutting parameters on cutting force: a) cutting
speed and cutting tool, b) feed rate and depth of cut
Slika 7: Vpliv parametrov rezanja na silo rezanja: a) hitrost rezanja in
rezilno orodje, b) hitrost podajanja in globina rezanja
Figure 8: Effect of cutting parameters on surface roughness:
a) cutting speed and cutting tool, b) feed rate and depth of cut
Slika 8: Vpliv parametrov rezanja na hrapavost povr{ine: a) hitrost
rezanja in rezilno orodje, b) hitrost podajanja in globina rezanja
R-Sq = 89,93 % R-Sq(adj) = 86,83 %
Ra Ct
V f
l =− − −
− + +
0 0297778 0306
0 00272667 1289 0 42866
. .
. . . 7ap
(3)
R-Sq = 87,04 % R-Sq(adj) = 83,05 %
FC Ct V f
ap
q =− + + + −1098 02 142 084 9 25331 485461
348127
. . . .
. − − + −
−
0857436 330 082 47 4709
0 0130256 14
. . .
.
CtV Ctf Ctap
VV . .
. . .
1955 0699091
305532 117487 258 969
Vf Vap
ff fap ap
− −
+ + ap
(4)
R-Sq = 98.11 % R-Sq(adj) = 91.97 %
Ra Ct V
f
q = + + +
+ −
2 20536 0 939478 0 0300324
8 99345 31784
. . .
. . 4 0 00727744 25756
0352393 0 000113
ap CtV Ctf
Ctap
− − +
+ −
. .
. . 441 0 0278727
0 00193455 611292 141846
VV Vf
Vap ff
− −
− − +
.
. . . fap
apap
+
+0 74574.
(5)
R-Sq = 98.79 % R-Sq(adj) = 94.87 %
The correlation coefficient (R2) of the equations
obtained with the linear-regression model for Fc and Ra
were calculated as 89.93 % and 87.04 %, respectively.
These values were calculated for the quadratic regression
model as 98.11 % and 98.79 %. When compared with
the linear-regression model, it can be seen that more
accurate estimations can be made using the quadratic
regression model. Figure 9 gives the comparison of the
Taguchi method with regression models for the
estimation of Fc and Ra. From the figure it is clear that
for both Fc and Ra, the closest values to the line were
obtained with the quadratic regression model, followed
by the values obtained by the Taguchi method and the
linear regression model. Thus, it is possible to apply the
quadratic regression model successfully for the estima-
tion of Fc and Ra.
4 CONCLUSION
This study was focused on the optimization (via the
Taguchi method) of machining parameters, which in-
cluded cutting tool, cutting speed, feed rate and cutting
depth affecting cutting force and surface roughness in
the turning of 15-5 PH martensitic stainless steel. Analy-
tical models were developed using regression analysis
for the estimation of Fc and Ra. The results are summa-
rized as follows:
The optimum levels of the control factors for mini-
mizing Fc and Ra were defined by using signal-to-noise
ratios. The optimum control factors for cutting force
(A2B3C1D1) were determined as a CVD-coated cutting
tool, a cutting speed of 250 m/min, a feed rate of
0.1 mm/r and a depth of cut of 0.5 mm, while the opti-
mum control factors for surface roughness (A2B2C1D1)
were determined as a CVD-coated cutting tool, a cutting
speed of 200 m/min, a feed rate of 0.1 mm/r and a depth
of cut of 0.5 mm.
The ANOVA analysis revealed that the depth of cut
was the most dominant parameter on Fc with the contri-
bution ratio of 53.82 %, and that the feed rate was the
most dominant parameter on Ra with the contribution
ratio of 87.64 %.
The R2 values of the equations obtained by the
linear-regression model for the cutting force and surface
roughness were calculated as 89.93 % and 87.04 %,
respectively. These values were found to be 98.11 % and
98.79 % for the quadratic regression model. The higher
correlation coefficients showed that the models had a
good estimation ability. Consequently, from the point of
view of estimation performances, the best result was
obtained with the quadratic regression model, followed
by the Taguchi method and the linear regression model,
accordingly.
From the point of view of obtaining lower cutting
force and lower surface roughness, the CVD TiCN-
Al2O3-TiN-coated tool exhibited some advantage over
the PVD TiAlN-AlCrO-coated tool.
In conclusion, in the turning of precipitation-hard-
ened 15-5 PH stainless steel, the Taguchi method can be
used successfully to decrease energy consumption and to
increase product quality.
ª. ÇETIN, T. KIVAK: OPTIMIZATION OF THE MACHINING PARAMETERS FOR THE TURNING ...
Materiali in tehnologije / Materials and technology 51 (2017) 1, 133–140 139
MATERIALI IN TEHNOLOGIJE/MATERIALS AND TECHNOLOGY (1967-2017) – 50 LET/50 YEARS
Figure 9: Comparisons of actual predicted values for: a) cutting force
and b) surface roughness
Slika 9: Primerjave dobljenih-napovedanih vrednosti za: a) silo
rezanja in b) hrapavost povr{ine
Acknowledgment
The authors wish to place their sincere thanks to
Düzce University Scientific Research Project Division
for financial support for the Project No: BAP
2014.07.04.217
5 REFERENCES
1 M. Aghaie-Khafri, F. Adhami, Hot deformation of 15-5 PH stainless
steel, Mater. Sci. Eng. A, 527 (2010), 1052–1057, doi:10.1016/
j.msea.2009.09.032
2 A. Mondelin, F. Valiorgue, J. Rech, M. Coret, E. Feulvarch, Hybrid
model for the prediction of residual stresses induced by 15-5PH steel
turning, Int. J. Mech. Sci., 58 (2012), 69–85, doi:10.1016/j.ijmecsci.
2012.03.003
3 A. Kumar, Y. Balaji, N. Eswara, P. Rasad, G. Gouda, K. Tamilmani,
Indigenous development and airworthiness certification of 15–5 PH
precipitation hardenable stainless steel for aircraft applications,
Indian Academy of Sciences, Sadhana, 38 (2013), 3–23
4 F. Danoix, J. Lacaze, A. Gibert, D. Mangelinck, K. Hoummadaf, E.
Andrieu, Effect of external stress on the Fe–Cr phase separation in
15-5 PH and Fe–15Cr–5Ni alloys, Ultramicroscopy, 132 (2013)
193–198, doi:10.1016/j.ultramic.2012.12.004
5 H. R. Habibi Bajguirani, The effect of ageing upon the microstruc-
ture and mechanical properties of type 15-5 PH stainless steel, Mater.
Sci. Eng. A, 338 (2002), 142–159, doi:10.1016/S0921-5093(02)
00062-X
6 H. Shao, L. Liu, H. L. Qu, Machinability study on 3%Co–12%Cr
stainless steel in milling, Wear, 263 (2007), 736–744, doi:10.1016/
j.wear.2007.01.074
7 M. Sarýkaya, A. Güllü, Multi-response optimization of minimum
quantity lubrication parameters using Taguchi-based grey relational
analysis in turning of difficult-to-cut alloy Haynes 25, J. Cleaner
Prod., 91 (2015), 347–357, doi:10.1016/j.jclepro.2014.12.020
8 T. Kývak, Optimization of surface roughness and flank wear using
the Taguchi method in milling of Hadfield steel with PVD and CVD
coated inserts, Measurement, 50 (2014), 19–28, doi:10.1016/
j.measurement.2013.12.017
9 M. A. Xavior, M. Adithan, Determining the influence of cutting
fluids on tool wear and surface roughness during turning of AISI 304
austenitic stainless steel, J. Mat. Process. Technol, 209 (2009),
900–909, doi:10.1016/j.jmatprotec.2008.02.068
10 A. J. Makadia, J. I. Nanavati, Optimisation of machining parameters
for turning operations based on response surface methodology,
Measurement, 46 (2013), 1521–1529, doi:10.1016/j.measurement.
2012.11.026
11 M. C. Çakýr, Principles of modern metal cutting. Vipaº A.ª, Bursa,
Turkey, (2000), 350–390
12 B. de Agustina, C. Bernal, A. M. Camacho, E. M. Rubio, Experimen-
tal Analysis of the Cutting Forces Obtained in Dry Turning Processes
of UNS A97075 Aluminium Alloys, Procedia Engineering, 63
(2013), 694–699, The Manufacturing Engineering Society Interna-
tional Conference, MESIC 2013, doi:10.1016/j.proeng.2013.08.248
13 Y. Karpat, T. Özel, Multi-objective optimization for turning pro-
cesses using neural network modeling and dynamic-neighborhood
particle swarm optimization, Int. J. Adv. Manuf. Technol., 35 (2008),
234–247, doi:10.1007/s00170-006-0719-8
14 A. Çiçek, T. Kývak, G. Samtaº, Y. Çay, Modelling of Thrust Forces in
Drilling of AISI 316 Stainless Steel Using Artificial Neural Network
and Multiple Regression Analysis, Strojni{ki vestnik – Journal of
Mechanical Engineering 58 (7-8) (2012), 492–498, doi:10.5545/
sv-jme.2011.297
15 Ý. Asiltürk, S. Neºeli, Multi response optimisation of CNC turning
parameters via Taguchi method-based response surface analysis,
Measurement 45 (2012), 785–794, doi:10.1016/j.measurement.2011.
12.004
16 M. Günay, E. Yücel, Application of Taguchi method for determining
optimum surface roughness in turning of high-alloy white cast iron,
Measurement, 46 (2013), 913–919, doi:10.1016/j.measurement.2012.
10.013
17 D. P. Selvaraj, P. Chandramohan, M. Mohanraj, Optimization of
surface roughness, cutting force and tool wear of nitrogen alloyed
duplex stainless steel in a dry turning process using Taguchi method,
Measurement, 49 (2014), 205–215, doi:10.1016/j.measurement.2013.
11.037
18 C. Camposeco-Negrete, Optimization of cutting parameters for mini-
mizing energy consumption in turning of AISI 6061 T6 using
Taguchi methodology and ANOVA, J. Cleaner Prod., 53 (2013),
195–203, doi:10.1016/j.jclepro.2013.03.049
19 C. Ezilarasan, V. S. Senthil Kumar, A. Velayudham, An experimental
analysis and measurement of process performances in machining of
nimonic C-263 super alloy, Measurement, 46 (2013), 185–199,
doi:10.1016/j.measurement.2012.06.006
20 A. Hasçalýk, U. Çaydaº, Optimization of turning parameters for sur-
face roughness and tool life based on the Taguchi method, Int. J.
Adv. Manuf. Technol., 38 (2008), 896–903, doi:10.1007/s00170-
007-1147-0
21 U. Yalcin, A. D. Karaoglan, I. Korkut, Optimization of cutting para-
meters in face milling with neural networks and Taguchi based on
cutting force, surface roughness and temperatures, Int. J. Prod. Res.,
51 (2013), 3404–3414, doi:10.1080/00207543.2013.774482
22 I. Asilturk, H. Akkuº, Determining the effect of cutting parameters
on surface roughness in hard turning using the Taguchi method,
Measurement 44 (2011), 1697–1704, doi:10.1016/j.measurement.
2011.07.003
23 ª. Karabulut, Optimization of surface roughness and cutting force
during AA7039/Al2O3 metal matrix composites milling using neural
networks and Taguchi method, Measurement 66, (2015), 139–149,
doi:10.1016/j.measurement.2015.01.027
24 M. Sarikaya, Optimization of the surface roughness by applying the
Taguchi technique for the turning of stainless steel under cooling
conditions, Materials and Technology, 49 (6) (2015), 941–948,
doi:10.17222/mit.2014.282
25 A. Çiçek, F. Kara, T. Kývak, E. Ekici, Evaluation of machinability of
hardened and cryo-treated AISI H13 hot work tool steel with ceramic
inserts, International Journal of Refractory Metals and Hard
Materials, 41 (2013), 461–469, doi:10.1016/j.ijrmhm.2013.06.004
26 M. H. Cetin, B. Özcelik, E. Kuram, E. Demirbaº, Evaluation of vege-
table based cutting fluids with extreme pressure and cutting parame-
ters in turning of AISI 304L by Taguchi method, J. Cleaner Prod., 19
(2011), 2049–2056, doi:10.1016/j.jclepro.2011.07.013
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