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Accepted Manuscript
Title: Comparison of tool life between ceramic and cubicboron nitride (CBN) cutting tools when machining hardenedsteels
Author: Y. Sahin
PII: S0924-0136(08)00620-1DOI: doi:10.1016/j.jmatprotec.2008.08.016Reference: PROTEC 12315
To appear in: Journal of Materials Processing Technology
Received date: 20-7-2007Revised date: 6-8-2008Accepted date: 12-8-2008
Please cite this article as: Sahin, Y., Comparison of tool life between ceramic and cubicboron nitride (CBN) cutting tools when machining hardened steels, Journal of MaterialsProcessing Technology (2007), doi:10.1016/j.jmatprotec.2008.08.016
This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proofbefore it is published in its final form. Please note that during the production processerrors may be discovered which could affect the content, and all legal disclaimers thatapply to the journal pertain.
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Comparison of tool life between ceramic and cubic boron
nitride (CBN) cutting tools when machining hardened steels
Y. Sahin, [email protected], Department of Mechanical Education, Faculty of Technical
Education, Gazi University, 06500-Besevler/Ankara/Turkey
Abstract
This paper describes a comparison of tool life between ceramics and CBN cutting tools when
machining hardened bearing steels using the Taguchi method. An orthogonal design, signal-to-
noise ratio (S/N) and analysis of variance (ANOVA) were employed to determine the effective
cutting parameters on the tool life. First order linear and exponential models were carried out to
find out the correlation between cutting time and independent variables. Second order regression
model was also extended from the first order model when considering the effect of cutting speed
(V), feed rate (f), hardness of cutting tool (TH) and two-way of interactions amongst V, f, TH
variables. The results indicated that the V was found to be a dominant factor on the tool life,
followed by the TH, lastly the f. The CBN cutting tool showed the best performance than that of
ceramic based cutting tool. In addition, optimal testing parameter for cutting times was determined.
The confirmation of experiment was conducted to verify the optimal testing parameter.
Furthermore, the second order regression model and exponential model supported the first order
model regarding the prediction capability. Improvements of the S/N ratio from initial testing
parameters to optimal cutting parameters or prediction capability depended on the S/N ratio and
ANOVA results. Moreover, the ANOVA indicated that the cutting speed was significant but other
parameters were an insignificant effect on the tool life at 90% confidence level. The percentage
contribution of the cutting speed was about 41.63 on the tool life when machining the hardened
steel.
Keywords: Turning; Cutting tool; Tool life; Optimal parameter; Orthogonal design; Hardness
* Manuscript
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NOMENCLATURE
AISI american iron and steel institute
ANOVA analysis of variance
CBN cubic boron nitride cutting tool
CNC computer controlled lathe machine
CVD chemical vapor deposition process
d depth of cut (mm)
dB decibel
DF degree of freedom
F statistical characteristic
f feed rate (mm/rev)
HRC hardness value by Rockwell
HV hardness value by Vickers
KY1615 mixed alumina ceramic cutting tool
KY4400 coated alumina ceramic cutting tool
MS mean of squares
P contribution (%)
PVD physical vapor deposition process
Ra average surface roughness value (µm)
R2adj% adjusted coefficient of multiple correlation
SS sum of squares
S/N ratio signal-to-noise ratio (dB)
T tool life (min)
TH tool hardness (HV)
V cutting speed (m/min)
WC tungsten carbide cutting tool
ηi average S/N ratio (dB)
ηi,cal calculation test result for tool life and S/N ratio (dB)
ηi,ver verification test result for tool life and S/N ratio (dB)
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1.0 Introduction
Hardened steels are machined by grinding process in general, but grinding operations are time
consuming and are limited to the range of geometries to be produced (Poulachon et al., 2004). The
hardened steel surfaces have an abrasive effect on the tool material, and the high temperature on
the cutting edge causes diffusion between tool and chip. Therefore, improved technological
processes, optimum tool selection, determination of optimum cutting parameters or tool geometry
should be considered. The developments of new cutting tools have led to the use of higher cutting
speeds compare with conventional machining. High speed cutting reduces machining costs by
increasing production rate. However, high speed cutting leads to the rapid wear of cutting tools,
which is caused by the high temperatures generated at the cutting zone and as a result tool life
decreases (Diniz and Oliveira, 2008). The ability of polycrystalline cubic boron nitride (CBN)
cutting tools to maintain a workable cutting edge at elevated temperature is, to same extent, shared
with several conventional ceramic tools. These tools are characterised by high hot hardness, wear
resistance and good chemical stability and low fracture toughness (Benga and Abrao, 2003). CBN
and ceramic tools are used in the manufacturing industry for hard turning because of its inertness
with ferrous materials and its high hardness. Though CBN particles and binder phases such as TiN
are harder than carbides in steels, it is still possible that the tool will encounter “soft” abrasive
wear. The machining of hardened bearing steel represents groving proportion of applications
involving hard cutting tools such as CBN and ceramics (Lima et al., 2005).
Various studies have been conducted to investigate the performance of CBN and ceramic tools
when machining hard steels or hardened steels. Chou and Song (2004) studied tool nose radius
effects on finish turning hardened AISI 52100 steels. The results showed that large tool nose radii
only gave finer surface finish, but comparable tool wear compared to small nose radius tools.
Grzesik and Wanat (2006) investigated the surface finish generated in hard turning of quenched
alloy steel (60 HRC) using conventional and wiper ceramic inserts. They determined that surfaces
produced by wiper tools contained blunt peaks with distinctly smaller slopes resulting in better
bearing properties. Diniz and Oliveira (2008) investigated the turning of AISI 4340 steel (56 HRC)
interrupted surfaces using three types of CBN cutting tools (low CBN content and high CBN
content) and two cutting edge micro-geometries including chamfered and rounded edge. The
results indicated that the longest tool life was obtained when the low CBN tool was used,
regardless of surface type. Lim et al.(2001) investigated the effects of work materials on the wear
improvement of coated tools by comparing uncoated and TiC coated carbide tools. The
experimental results exhibited that the TiC coating was more effective when machining carbon
1045 grade, decreasing tool wear rates by half an order magnitude. Avila and Abrao (2001) studied
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the turning hardened AISI 4340 steel using mixed alumina cutting tools. They observed that the
application of a cutting fluid based on an emilsion without mineral oil resulted in longer tool life
compared to dry cutting.
Kumar et al.(2003) studied the machinability of hardened steel (EN24) using alumina based
ceramic cutting tools. It was observed that notch wear and crater wear was higher for mixed
alumina [Ti (C,N)] ceramic tool than zirconia toughened alumina (ZTA) ceramic tool when
machining the steel (40-45 HRC), but the performance of alumina ceramic tool was better than that
of ZTA. Later work by Kumar et al. (2006) found that flank wear increased with increasing cutting
speed in both types of ceramic cutting tools. The flank wear, crater wear in Ti (C,N) mixed alumina
ceramic tool was lower than that of SiCw reinforced alumina cutting tool on machining martensitic
stainless steel-grade 410 (60 HRC) and EN24 steel (45 HRC). Khrais and Lin (2006) studied wear
mechanisms and tool performances of TiAlN PVD coated inserts during machining of AISI 4140
steels at high speeds for both dry and wet machining. Dry cutting was better than wet cutting at
around 200-400 m/min speed.
Jang et al.(2000) studied tool the wear and maching performance of hardened AISI M2 steel (60
HRC) and determined the flank wear as the dominant wear mode on the ceramic tool insert but
crater wear was very small. Depth of cut was the most important factor to affect cutting force
variation, and the cutting force increased due to the tool wear. Lima et al.(2005) investigated the
machinability of hardened AISI 4340 and AISI D2 cold work tool steel at different levels of that
hardness and using a range of cutting tools. The results indicated that surface roughness of 4340
steel was improved as cutting speed was elevated and deteriorated with feed rate. However, the
surface roughness of AISI D2 steel with mixed alumina inserts allowed a surface finish as good as
that produced by cylindrical grinding. The drastic tool wear occurred for the combination of cutting
speed of 220 m/min with feed rate of 0.15 mm/rev. Xu et al.(2001) showed detail in the effect of
yttrium on mechanical properties and machining performance of Al2O3/TiC(C,N) ceramic tool. The
results exhibited that the adequately addition of yttrium improved the mechanical property of the
ceramic tool material. Barry and Byrne (2001) investigated the mechanism of Al2O3/TiC tool wear
in the finish turning of AISI 4340 hardened steel (52 HRC). The wear resistance of low content of
CBN/TiC composites was found to be superior than high content CBN tools in finish machining of
AISI 4340 steel.
Chou et al.(2002) investigated the performance and wear behaviour of different CBN tools in finish
turning of hardened AISI 52100 steel (62 HRC). The results indicated that low CBN content tools
consistently performed better than high CBN content. The flank wear rates were proportional to
cutting speed and high CBN tools exhibited accelerated thermal wear associated with high cutting
temperatures. Benga and Abrao (2003) studied the machinability of hardened 100Cr6 bearing steel
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(62-64 HRC) when dry turning using mixed alumina, whisker reinforced alumina and PCBN
inserts. The best tool life results were obtained with the CBN compact, followed by the mixed
alumina at low feed rates and by the whisker reinforced alumina when feed rate was increased.
Arsecularatne et al.(2005) studied the wear mechanisms of cutting tools such as WC, CBN and
polycrystalline diamond (PCD) using the tool life and temperature results. It was concluded that the
most likely dominant tool wear mechanism for the WC was diffusion and that for the CBN was
chemical wear.
Poulachon et al.(2001) presented various modes wear and damage of CBN cutting tools under
different loading conditions on 100Cr6 bearing steel (45-65 HRC), in order to establish a reliable
wear modelling. The wear mechanisms depended not only on the chemical composition of the
CBN, and the nature of the binder phase but also on the hardness value and above all on the
microstructure (percentage of martensite, type, composition of the hard phases, etc.) of machining
work material. Later work by Poulachon et al.(2004) investigated the tool wear mechanism of CBN
cutting tools in finish turning of various hardened steels such as X155CrMoV12 (AISI D2) cold
wok steel, X38CrMoV5 (AISI H11) hot work steel, 35NiCrMo16 hot work steel and 100Cr6
bearing steel (AISI 52100), treated at 54 HRC. They found that tool flank grooves were correlated
with the hard carbide content of the steel workpiece. For steels with only martensitic grains, the
increase in the cutting speed had a greater impact on the tool-wear rate. The last work by
Poulachon et al.(2005) investigated the evaluation of white layers produced during progressive tool
flank wear in dry hard turning with CBN cutting tools at the same workpieces. The surface profile
was influenced by the workpiece microstructure, especially if it was coarse-grained. Luo et
al.(1999) studied the wear of ceramic and CBN tool when turning AISI 4340 steel at various
hardnesses. It was concluded that the flank wear was reduced as work material hardness increased
up to a critical value of 50 HRC, and a further increase in the workpiece hardness accelarated the
tool wear rate. The tool wear was mainly due to abrasion of the tool/binder by hard carbide
particles in the steel workpieces. Kishawy and Elbestawi (2001) investigated the tool wear
characteristics during high speed turning of AISI D2 cold work tool steel. The unfavorable residual
stresses were minimized at high cutting speed and high depth of cut.
Theile et al.(2000) showed that cutting edge geometry had a significant impact on surface integrity
and residual stresses in finish hard turning. Large hone radius tools produced more compressive
stresses, but left white layers on the surface. Ozel and Nadgir (2003) reported that change in edge
geometry, increased cutting speed and depth of cut resulted in increased tool stresses and tool
temperatures at the cutting zone. Hua et al. (2005) indicated that hone edge plus chamfer cutting
edge and aggressive feed rate helped to increase both compressive stresses and penetration depth.
Yen et al. (2004) analysed the effect of various edge geometries of uncoated carbide tools by using
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FEM cutting simulation. The results exhibited that the tool wear was directly related to cutting
temperature, tool stresses, and chip sliding velocity.
There are very few publications appeared in the literature for predicting tool life using neural
network or other modelling techniques. For example, Choudry and Bartarya (2003) compared the
design of experiments technique and neural networks techniques for predicting tool wear. They
established the relationships between temperature and flank wear. They concluded that neural
networks performed better than design of experiments technique (Schefter et al.,2003; Sick et
al.,2002). Davim (2001) studied the influence of cutting conditions on the surface finish based on
the Taguchi method. The resuts indicated that cutting velocity had a greater effect on the
roughness, followed by the feed rate. Sahin and Motorcu (2008) developed the surface roughness
model using response surface methodology when machining hardened AISI 1050 steel. They
reported that CBN cutting tools produced a better surface roughness than those of KY1615 cutting
tools in all experimental conditions. Later work by Davim and Figueira (2006) investigated the
machinability evaluation in hard turning of cold work steel (D2) with ceramic tools using statistical
techniques. It was concluded that the tool wear was highly influenced by the cutting velocity, and
in a smaller degree, by cutting time. The specific cutting pressure was also strongly influenced by
the feed rate.
Al-Ahmari et al.(2007) investigated that empirical models for predicting of machinability models
(tool life, cutting force and surface roughness) were developed based on the cutting experiments on
austenitic AIS 302 steels. The developed computational neural networks (CNN), response surface
methodology (RSM) and multiple linear regression analysis (RA) are compared and evaluated. It
was found that CNN models were better than RA and RSM models. Also, RSM models were better
than RA models for predicting the tool life and cutting force models. The artificial neural network
(ANN) model of surface roughness analysis by Davim et al.(2007) reveled that cutting speed and
feed rate had significant effects in reducing the surface roughness of free machining steel.
Yang et al. (1998) used the Taguchi method to find the optimal cutting parameters for turning
operations. Analysis of variance was employed to investigate the cutting characteristics of AISI
1045 steels using cemented carbide cutting tools. He found that cutting speed and feed rate were
the significant cutting parameters for affecting the tool life. Feng and Wang (2002) used regression
analysis to develop a complete empirical model of surface roughness, using feed rate, workpiece
hardness, tool point angle, depth of cut, spindle speed, and cutting time. Hypothesis testing
established the adequacy of the model while its performance was deemed satisfactory. Their
analysis concluded that the feed rate was also identified as the most important factor along with the
cutting time. Ozel and Karpat (2005) indicated that an exponential model for both surface
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roughness and flank wear was developed considering finish hard turning of AISI H13 steels using
CBN tools. In that model, the surface roughness or flank wear was a function of work material
hardness, edge radius of the CBN tool, cutting speed, feed rate and cutting length. The modified
exponential work achieved by the same authors reported that the work material hardness and feed
rate had the greatest effect on the flank wear and surface roughness, respectively. It was followed
by cutting length and feed rate for the flank wear. Kopac et al.(2002) used the Taguchi design to
determine the optimal machining parameters for a desired surface roughness in fine turning of cold
pre-formed steels. They analysed the influence of workpiece material properties, cutting parameters
and TiN (PVD) hard coating on the surface roughness. According to their analysis, cutting speed
was the most significant influence on the surface quality and a higher cutting speed resulted in a
smoother surface. Cheng and Hong (2003) studied a comparison of tool life of tungsten carbide
coated by multi-layer TiCN and TiAlCN for end mills using the Taguchi method. The TiCN coated
tool had the best wear resistance for machining of quenched AISI 1045 carbon steel. In their
investigations, they found that the material of the tool was the main parameter among the four
controllable factors (different coated deposition, feed rate, spindle speed and tool material).
The above review shows that the most of the machining study has been focused on hard and
hardened steels using ceramic or CBN cutting tools. However, relatively few works related to
machining steels and/or hardened steels based on the tool life or surface roughness prediction
models have been reported by the Taguchi design or other methods (Cheng and Hong, 2003;
Davim, 2001; Davim and Figueira, 2006; Kơpac et al., 2002; Sahin, 2006; Sahin et al., 2004; Sahin
and Motorcu, 2008; Yang et al.1998; Ozel and Nadgir, 2002; Ozel and Karpat.2005). The Taguchi
method is a systematic application of design and analysis of experiments for the purpose of
designing and improving product quality. A high quality product can be produced quickly and at
low cost. It is relatively simple method that can be used for optimizing different production stages
with few experimental runs (Sahin, 2001; Montgomery, 2001). The aim of the present study was,
therefore, to develop the tool life model using main cutting parameters such as cutting speed, feed
rate and cutting tool’s hardness, based on the Taguchi method when machining hardened AISI
52100 bearing steels. First order linear and exponential and second order predicting equations for
tool lives were developed within reasonable error. Furthermore, analysis of variance was employed
to investigate the cutting characteristics of steel bars.
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2.0 Experimental Work
2.1. Materials
The machine used for turning tests was a Johnford TC35 Industrial type of CNC lathe machine.
The lathe equipped with variable spindle speed from 50 rpm to 3500 rpm, and a 10 KW motor
drive was used for the tests. Three types of cutting tools were used for the present work. These are
mixed alumina ceramic tools, coated ceramic cutting tools and CBN cutting tools. One of the tools
was a mixed alumina ceramic with an Al2O3 (70%);TiC (30%) matrix, which is designated by
KY1615. The other insert was coated using a Physical Vapor Deposition (PVD) method. Coating
substance takes place on the mixed ceramic substrate and PVD-TiN coated mixed ceramic with a
matrix of Al2O3 (70%);TiC (30%)+TiN, which is called as KY4400 grade. The insert types were
TNGA 160408-KY1615 and TNGA 160408-KY4400. The geometry angles of insert seating for
ceramics: nominal rake angle -6 o, back rake angle -6 o, clearance angle 6 o, approach angle 75 o,
major tool cutting angle 60 o triangle-shaped inserts and 0.8 mm nose radius. The insert was rigidly
attached to a tool holder of ISO designation of PTBNR 2525-16. These ceramic based tools are
commercially available inserts according to ISO code, supplied by Kennametal Inc. for the turning
tests. The last one was a CBN with an Al2O3+TiC matrix, which is designated by CBN/TiC. The
CBN/TiC tools contained CBN (50%); TiC (40%); WC (6%); AlN, AlB2 (4%). However, the
CBN/TiC insert type was CNGA 120408S-L0 to machine the bearing steel. The tool edge
configuration between the rake and the clearance face consists of the chamfer of 0.1 mm width and
a constant negative rake angle -20 o, side rake angle -6 o, clearance angle 6 o, approach angle 75 o,
edge major tool cutting angle 80 o diamond-shaped inserts with 0.8 mm nose radius. The tool
holder of PCLNR 2525-12A was used. These cutting tools are also commercially available inserts,
supplied by SECO Inc. Hardness’s of these cutting tools are about 2145, 2250 and 3660 HV,
respectively. Details of characteristics of cutting tools types, geometry, designation and some
properties are given in Table 1.
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The material used throughout this work was an AISI 52100 steel. The material was the AISI 52100
bearing steel containing C (0.99%), Mn (0.39%), Si (0.16%), Cr (1.40%), Ni (1.4%) and balance
Fe. For heat treatment of AISI 52100 steels, the workpieces were austenised at 850 o C for 2 h and
quenched with oil. Several tempering temperatures were selected to prepare specimens of various
hardness values. It was tempered for 1.5 h at 185 oC, resulting in a hardness of 659 HV. Samples to
be cut were in the cylindrical form of steel bars with diameter of 48 mm, length of 240 mm. These
bars were machined under dry condition. The work material bars were trued, centered and cleaned
by removing a 1 mm depth of cut from the outside surface, prior to the actual machining tests.
After each test, the worn cutting tool was measured with the optical tool microscope to determine
the degree of flank wear. The width of the flank wear criteria was taken as 0.3 mm. In general,
experiment was stopped to measure the width of the wear land at each 5, 10, 15 and 30 minutes.
2.2. Experimental design
The Taguchi design was selected to find out the relationships between independent variables and
cutting time. The independent variables were cutting speed, feed rate, depth of cut and tool’s
hardness. The experiments were carried out to analyze the influence of cutting parameters on tool
life for machining hardened AISI 52100 steels. Cutting parameters were selected keeping in mind
that the hard turning operation was generally used as a finishing operation as an alternative to
grinding.
The depth of cut was fixed as 0.2 mm in all test conditions. Three feed rates (f) 0.06, 0.084, 0.117
mm/rev were selected. Three cutting speeds (V) were chosen: 100, 140, 196 m/min. Details of
experimental design, control factors and their levels, and results for tool lives are shown in Table 2.
This table showed that the experimental plan had three levels. A standard Taguchi experimental
plan with notation L9 (34) was chosen. The rows in the L9 orthogonal array used in the experiment
corresponded to each trial and the columns contained the factors to be studied. The first column
consisted of cutting speed, the second contained the feed rate and the consecutive column consisted
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of the cutting tool’s hardness. The experiments were conducted twice for each row of the
orthogonal array to circumvent the possible errors in the experimental study. In the Taguchi
method, the experimental results are transformed into a signal-to-noise (S/N) ratio. This method
recommends the use of S/N ratio to measure the quality characteristics deviating from the desired
values. To obtain optimal testing parameters, the-higher-the-better quality characteristic for
machining the steels was taken due to measurement of the tool life. The S/N ratio for each level of
testing parameters was computed based on the S/N analysis. This design was sufficient to
investigate the three main effects. With S/N ratio analysis, the optimal combination of the testing
parameters could be determined.
Apart from this method, another model with transformation technique was developed and
compared. This model is an exponential model with logarithmic transformed variables such as V, f
and TH. The functional relationship between tool life and independent variables under
investigation could be postulated as;
pmn THfVCTmean ...= (1)
where V is cutting speed, f is feed rate and TH is hardness of tool. A logarithmic transformation
can be applied to convert the non-linear form of equation into the linear form of Eq.(1);
THpfmVnCTmean ln.ln.ln.lnln +++= (2)
A logarithmic transformation can be applied to convert the non-linear form of equation into the
linear form of Eq.(1). Details of the logarithmic transformation equations and estimations can be
found in previous work (Sahin, 2001; Sahin, 2006).
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3. 0 Results and Discussion
3.1. Analysis of control factor
Analysis of the influence of each control factor (V, f, TH) on the tool life was performed with a so-
called signal-to-noise (S/N) response table, using a Minitab 15 computer package. The
experimental design, results for tool lives and S/N ratios are shown in Table 2. The control factors
and their un-coded tool lives were included in this table. Table 3 shows the S/N response table of
tool lives for machining the hardened steels. It indicated the S/N ratio at each level of control factor
and how it was changed when settings of each control factor were changed from level 1 to level 2.
The influence of interactions between control factors was neglected here. The control factor with
the strongest influence was determined by differences value. The higher the difference, the more
influential was the control factor. The control factors were sorted in relation to the difference
values. It could be seen in the Table 3 that the strongest influence was exerted by cutting speed,
followed by hardness of cutting tool, lastly feed rate, respectively. Since the first level of the
cutting speed was about 41.4 dB while the third level of the cutting speed was about 27.18 dB the
difference being the most highest of 13.58 dB. It is followed by the hardness of cutting tool. The
difference between the first level of the tool hardness and third level of the tool hardness was found
to be about 12.06 dB, which is significant level again. The feed rate showed the least effect on the
tool life since the difference between the first level and third level were about 10.51 dB. A similar
observation in the mean table was also found for the tool life of machining these types of hardened
steels.
3.2. Main effect on the tool life
Fig. 1a, b shows the main effect plots for tool lives of the cutting tools for S/N ratios and mean
values, respectively. The greater is the S/N ratio, the smaller is the variance of the tool life around
the desired value. Optimal testing conditions of these control factors could be very easily
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determined from the response graph. The best tool life value was at the higher S/N value in the
response graph. For main control factors, Fig.1a indicates the optimum condition for the tested
samples (V1 f1 TH3). Thus, it could be concluded that the best tool life of cutting tools can be
achieved and their optimal setting of control factors for tested samples are shown in Table 4. From
the results of control factors, higher tool life was obtained under cutting conditions of V=100
m/min, f=0.06 mm/rev when machining AISI 52100 workpiece by CBN/TiC cutting tool. The
experimental work was carried out on the same bearing steel using the determined optimal control
factors. The tool life was found to be about 165.510 min. Then this value was transferred to the S/N
ratio (dB), average value of S/N ratio was calculated and it was about 44.376 dB. These values
were lower than that of theoretical values of tool lives in Table 5. Moreover, the mean tool life of
cutting tool is shown in Fig.1b. It was evident that the cutting speed had the greatest effect on the
optimal testing conditions. It might be that higher cutting speed led to higher flank wear width
because of easy for removal of particles from the place. It is followed by the cutting tool’s
hardness. It was observed that the tool life obviously increased highly as cutting tool changed from
level 1 to level 3 due to the having considerable hardness’s between these two cutting tools. The
feed rate was also effective on the tool life of the cutting tool (see Table 1). Effects, however, were
lower compared to those of cutting speeds. The effects of cutting parameters and their interaction
effects on the tool life are shown in Fig.2 as a three dimensional surface contour graph. As shown
in Fig.2a, longer tool life was observed when reducing the cutting speed. Increasing the feed rate
was not so effective on the tool life when machining at lower cutting speed and feed rate. However,
the tool life decreased with increasing the feed rate when machining medium cutting speed. It was
obvious that performance of all tools were good at lower cutting speeds. The tool life for the
KY1615 and KY4400 insert decreased considerably with increasing the cutting speed, but the
CBN/TiC cutting tool showed the best performance because of retaining their hardness at elevated
temperature and fracture toughness (Fig.2b). In addition, performances of all cutting tools were
good when machining at lower feed rate (Fig.2c). The cutting time reduced significantly when
tested with the KY1615 cutting tool. Moreover, the tool life decreased with increasing the feed rate,
especially for the KY1615 and KY4400 cutting tools because of not having high enough chemical
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stability of these tools. In the current study of machining hardened bearing steel, an orthogonal
design, S/N ratio and ANOVA were employed to determine the effective cutting parameters such
as V, f and tool’s hardness on the tool life. It was concluded that the cutting speed was found to be
the most important parameters on the tool life among control parameters. Similar results were
obtained with previous works carried out by Lima et al.(2005), Jang et al.(2000); Sahin (2006),
Benga and Abrao (2003), Chou et al.(2002), Yang and Tarng (1998), Davim and Figueira (2006).
For example, flank wear of the mixed alumina ceramic tools increased with increasing cutting
speed and depth of cut. It was concluded that surface roughness or tool flank wear models were
developed as a function of cutting speed, feed rate, and cutting time (Lima et al.2005). Higher
speed accelerated tool wear and increased the cutting temperature of the ceramic tool when
machining the hardened AISI M2 steel (Jang et al.2000). It was concluded that the tool wear and
surface roughness were highly influenced by the cutting velocity (Davim 2001; Davim and
Figueira 2006). In a similar study, it was reported that the cutting speed was the most dominant
factor affecting the tool life when turning of hardened 100Cr6 bearing steel (Benga and
Abrao,2003). The flank wear rates were proportional to cutting speed and high CBN tools exhibited
accelerated thermal wear when finish turning of hardened AISI 52100 steels (Chou et al.2002,
Barry and Byrne, 2001). The proposed model allowed to prediction of the tool life as a function of
the cutting parameters and workpiece hardness (Poulachon et al.2001). However, Chou et
al.(2002), Ozel and Nadgir (2002); Ozel and Karpat (2005), Poulachon et al.(2001), Poulachon et
al.(2004), Luo et al.(1999), Hua et al.(2005); Theile et al.(2000), Yen et al.(2004), pointed out that,
the most likely dominant parameters were workpiece hardness, steel microstructure, CBN content,
and edge geometry. On the other hand, Cheng and Hong (2003) indicated that material of tool was
the main parameter among other factors in milling quenched carbon steel.
3.3. Confirmation tests
Once the optimal level of design parameters was selected, the final step was to predict and verify
the improvement of the quality characteristic using the optimal level of the design parameters.
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Initial cutting parameters for machining the hardened steels were V2 f2 TH2, but it corresponded to
35.022 dB. Table 5 indicates the testing conditions used to obtain the tool life of cutting tools for
the confirmation tests. The established S/N ratio (ˆηi) using the optimal level of the design
parameters could be calculated. The mean S/N ratio was found to be about 52.905 dB at the
optimal level. The increase of the S/N ratio from the initial cutting parameter to the optimal cutting
parameter was about 17.88 dB. It meant that the tool life was increased by about 1.51 times.
Therefore, based on the S/N ratio analysis, the optimal testing parameters for the tool lives of
different cutting tools were the cutting speed at 1 level, feed rate at level 1, and type of material at 3
level. This table showed the results of the confirmation experiment for the tool life of the cutting
tool. In the table, ηi,ver indicates the verification test results, and ηi,cal shows the theoretical
calculated values in terms of optimal cutting parameters and significant factors. Table 5 also
showed a comparison of predicted tool life with the actual tool life using the optimal testing
parameter, but it is not a good agreement between the predicted and actual tool life. The optimal
testing parameter for cutting time seen in this table showed that the difference |ηi,ver -ηi,cal| was
about 8.53 dB. However, the optimal cutting time could be calculated based on ANOVA results
using significant factors. In this case, the S/N ratio was about 41.4 dB and its corresponding value
was about 117.489 min (see Table 5). The difference in the S/N ratio was about 2.97 dB. It is the
indication of a good agreement between the predicted and actual tool life. Consequently, it clearly
showed that the required cutting performance characteristics in dry turning testing processes had
great improvements through this proposed model when machining the hardened bearing steels.
3.4. Correlations
The correlations between the main factors V, f, TH and the cutting time of the tools were obtained
by multiple regressions. The obtained correlations were as follows;
The first order model equation for tool life prediction is given by;
THfVTmean *0324.0*955*790.0190 +−−= (3)
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where Tmean is the mean tool life. Eq.(3) indicated that the feed rate had the most significant effect
on the tool life of the sample when machining the hardened steel. The model had an adjusted R2
value of 87.7% and standard error was about 0.17.
The prediction model of exponential multiple regressions are given by;
THfVTmean ln*07.2ln*80.1ln*32.2269.5ln +−−−= (4)
An adjusted R2 value of 86.7% indicated that 86.7% of the variability in the tool life lnTmean is
explained by the model with factors lnV, lnf, lnTH and standard error was about 0.38.
A second order model was postulated to increase the sensitivity of the predicting equations by
taking into account of interaction effects.
The second order model equation for the tool life prediction is given by;
THfTHV
fVTHfVTmean
**09.1**00060.0
**2.6*144.0*4313*63.2656
+++−−−=
(5)
Eq.(5) indicated that again the feed rate had the most significant effect on the tool life. This
equation showed that the cutting speed was effective parameter, followed by interactions of f*V,
f*TH variables. The value of regression fitness coefficient was about 0.752. This showed that the
second order model could be explained the variation to the extent of 75.2%. The standard error for
the tool life was about 0.24.
The predicted tool lives produced by different cutting tools are calculated from the regression
equations 3, 4, 5 and tabulated in Table 6. The results are combined with some errors of the
prediction model for the first order model linear regression, exponential model in addition to the
second order models. There were some differences between theoretical and experimental values for
the first order, exponential model and second order model. For example, average error was about
24.1% for the exponential model while the average error was about 31% for the second order
model, respectively. For the most of cases, however, absolute average error was less than 20% for
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the first order and second order model, except for trial 6, 8 and 9. It was observed that the multiple
regression models were supported by the exponential model and second order model. The
regression model could be found to be better prediction capability, except for trial 6, 8, 9 when
considering each experiment separately (see Table 6). It was considered that less than 20% error
was reasonable, considering that there was an inherent randomness in metal cutting process
(Risbood, 2003; Sahin, 2006). Therefore, the model constructed in the present work could be used
to predict the tool lives of the cutting tools.
3.5. Uncertainty analysis of results
The most common estimated parameter is the population means, µ.
δµ ±= T or δµδ ±≤≤− TT (6)
when δ is an uncertainty and T is the sample mean. The interval δ−T to δ+T is called the
confidence interval on the mean. However, it depended on a confidence level. The confidence level
was the probability that the population mean would fall within the specified interval:
Confidence level= αδµδ −=±≤≤− 1)( TTP (7)
α is then the probability that the mean will fall outside the confidence interval. Since the
population distribution is normal, but n<30 and σ is not known, the t-distribution must be used
with n-1 degrees of freedom (df). This concept can be restated as
[ ] ααα −=≤≤− 12/2/ tttP (8)
Substituting for t, we obtain,
=
≤−≤− 2/2//
ααµ
tnS
TtP αµ αα −=
+≤≤− 1** 2/2/n
StT
n
StTP (9)
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which can also be stated as
n
StT n *)1.(2/ −∝±=µ (10)
Confidence interval
05.02/
90.01
==−
αα
(11)
To find the 90% confidence interval for the unknown mean of the tool life of the entire populations
of cutting tools, we first found the value of )1.(2/ −∝± nt for n-1=9 df. This was obtained from
student’s t distribution table by moving down column headed 0.05 to 8 df (Sahin, 2001). The value
we got was 1.86.
Sample mean, T
02.799
1.151.10........1215.117114 =+++++== ∑n
TT i min
Variance, 2σ
n
TTin
i
2
12
)−=∑
=σ , 175.24072 =σ
Population standard deviation, 25.46=σ .
Sample standard deviation (S ) can be calculated from the following equation.
1
)( 2
12
−
−=∑
=
n
TTi
S
n
i and 06.49=S .
Estimated standard error, n
Sx =σ , 35.16=xσ .
We could get an internal estimation of a population parameter (µ) using Eq.(10).
Thus,
413.3002.7935.16*86.102.79 ±=±=µ
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438.109413.3002.79 =+=uppµ min
602.48413.3002.79 =−=lowµ min
Therefore µ is between 109.43 and 48.602 min with a 90% degree of confidence. Another method
was to use Thompson τ test. The data to be rejected could be found from the lowest and upper
points of data ),( 21 δδ ;
TT lowerupper ±= ,2,1δ (12)
42791211 =−=−= TTupperδ
9.689.68791.102 =−=−=−= TTlowerδ
Thompsonτ test can also be used to determine the rejectable points
( 06.49,86.1,9 ==== Sn στ ) (Sahin, 2001).
SST *τ= (13)
25.9106.49*86.1 ==TS
Since 4225.911 >=> δTS , the points taken as the data are not rejectable. Otherwise,
XS, values would have been re-calculated again by assuming, n=8.
3.6. Analysis of variance
The ANOVA was used to investigate which design parameters significantly affect the quality
characteristics of the tool life for the turning process and to check the adequacy of the models
under development. Examination of the calculated value of variance ratio (F), which is the variance
of the factor divided by the error variance for all control factors. The results of ANOVA of tool
lives in machining hardened steels are shown in Table 7. In addition to degree of freedom (DF),
mean of squares (MS), sum of squares (SS), F-ratio and P-values associated with each factor level
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were presented. This analysis was performed for a confidence level of 90%. The F value for each
design parameters was calculated. The calculated value of the F showed a high influence of the
cutting speed (V) on the tool life since F-calculation was equal to 9.178 while F-table was about
9.0, but the feed rate (f), and hardness of the tool (TH) had an insignificant effect on the tool life
since F-test was equal to 5.563, 7.209, respectively. The last column of the above table indicated
the percentage of each factor contribution (P) on the total variation, thus exhibiting the degree of
influence on the result. It was important to observe the P-values in the table. From the analysis of
Table 7, the only factor A (P≈41.63%) showed a significant effect, cutting tool’s hardness
(P≈32.68%), and feed rate (P≈25.22%) were not significant on it.
4.0 Conclusions
The following conclusions could be drawn from results of tool lives of different cutting tools when
machining hardened bearing steels.
The L9 (34) orthogonal arrays were adopted to investigate the effects of cutting speed, feed rate and
hardness of cutting tools on the tool life. The results showed that the cutting speed exerted the
greatest effect on the tool wear, followed by the hardness of cutting tool, lastly the feed rate. The
estimated S/N ratio using the optimal testing parameter for the tool life was calculated. The
regression model was also supported by the exponential model and second order model as well.
Furthermore, CBN/TiC cutting tools showed the best performance than those of other tools. The
improvements of the S/N ratio from the initial testing parameters to the optimal cutting parameters
were ranged from 18% to 51% depending on the ANOVA results or S/N ratios. Moreover, the
ANOVA indicated that the cutting speed was significant but other parameters were insignificant
effect on the tool life at 90% confidence level. The percentage contribution of cutting speed was
about 41.63 on the tool life when machining the hardened steels.
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Acknowledgements
This research project was financed by the National Turkish University of Gazi in Turkey. The
author also wishes to acknowledge the technical assistance by Dr. Motorcu for carrying out this
work.
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References
Abouelatta, O.B., Mádl, J., 2001. Surface roughness prediction based on cutting parameters and
vibrations in turning operations. J.Mater.Process.Technol.118, 269-277.
Al-Ahmari, A.M.A., 2007. Predictive machinability models for a selected hard material in turning
operations. J.Mater.Proces.Technol.190, 305-311.
Arsecularatne, J.A., Zhang, L.C., Montross, C., 2006. Wear and tool life of tungsten carbide,
PCBN and PCD cutting tools. Int.J.Mach.Tools&Manuf. 46, 482-491.
Avila, R.F., Abrao, A., 2001. The effect of cutting fluids on the machining of hardened AISI 4340
steel. J.Mater.Process.Technol.119, 21-26.
Barry, J., Byrne, G., 2001. Cutting tool wear in the machining of hardened steels, Part I: cubic
boron nitride cutting tool wear. Wear 247, 139-151.
Benga, G.C., Abrao, A.M., 2003. Turning of hardened 100Cr6 bearing steel with ceramic and
PCBN tools. J.Mater.Process.Technol. 143-144, 237-241
Che Haron, C.H., Ginting, A., Goh, J.H., 2001. Wear of coated and uncoated carbides in turning
tool steel. J.Mater.Proces.Technol.116, 49-54.
Cheng, T.-C., Hong, H., 2003. Comparison of the tool life of tungsten carbide coated by multi-
layer TiCN and TiAlCN for end mills using the Taguchi method.
J.Mater.Process.Technol.123, 1-4.
Chou, Y.K., Evans, C.J., Barash, M.M., 2002. Experimental investigation on CBN turning of
hardened AISI 52100 steel. J.Mater.Process.Technol. 124, 274-283.
Chou, Y.K., Song, H., 2004. Tool nose radius effects on finish hard turning.
J.Mater.Process.Technol.148, 259-268.
Choudry, S.K., Bartarya, G., 2003. Role of temperature and surface finish in predicting tool wear
using neural network and design of experiments. Int.J.Mach.Tools&Manufac.43,747-753.
Davim, J.P., Figueira, L., 2006. Machinability evaluation in hard turning of cold work tool steel
(D2) with ceramic tools using statistical techniques. J.Mater.&Des.28, 1186-1191.
Page 22 of 38
Accep
ted
Man
uscr
ipt
22
Davim, J.P., 2001. A note on the determination of optimal cutting conditions for surface finish
obtained in turning using design of experiments. J.Mater.Process. Technol.116, 305-308.
Davim, J.P., Gaitonde, V.N., Karnik, S.R., 2007. Investigations into the effect of cutting conditions
on surface roughness in turning of free machining steel by ANN models. J.
Mater.Proces.Technol. (in press).
Dawson, T.G., Kurfess, T.R., 2002. Machining hardened steel with ceramic-coated and uncoated
CBN cutting tools. Soc.Manuf.Eng. 156, 1-7.
Diniz, A.E., Oliveria, A.J., 2008. Hard turning of interrupted surfaces using CBN tools.
J.Mater.Proces.Technol. 195, 275-281.
Feng, X., Wang, X., 2002. Development of empirical models for surface roughness prediction in
finish turning. Int.J.Adv. Manuf. Technol. 20, 348-356.
Grzesik,W., Wanat,T., 2006. Surface finish generated in hard turning of quenched alloy steel parts
using conventional and wiper ceramic inserts. J.Mach.Tools&Manufac.46, 1988-1995.
Hua, J., Shivpuri, H., Cheng, X., Bedehar, V., Matsumoto, Y., Hashimoto, Y., Watkins, T.R., 2005.
The effect of feed rate, workpiece hardness and cutting edge on the residual stress.
Mater.Sci.&Eng. A 394, 238-248.
Huang,Y., Dawson, T.G., 2005. Tool crater wear depth of modelling in CBN hard turning. Wear
258, 1455-1461.
Jang, D.Y., Hsiao, Y.T., 2000. Use of ceramic tools in hard turning of hardened AISI M2 steel.
Tribol.Transac. 43, 641-646.
Jindal, P.C., Santhanam, A.T., Schleinkofer, U., Shuster, A.F., 1999. Performance of PVD TiN,
TiCN, and TiAlN coated cemented carbide tools in turning. Int.J.Recfrac.Met.&Hard
Mater.17, 163-170.
Kishawy, H.A., Elbestawri, M.A., 2001. Tool wear and surface integrity during high speed turning
of hardened steel with cubic boron nitride tool. J.Eng.Manuf. 215, 755-767.
Page 23 of 38
Accep
ted
Man
uscr
ipt
23
Kơpac, J., Bahor, M., Sokovic, M., 2002. Optimal machining parameters for achieving the desired
surface roughness in fine turning cold pre-formed steel work processing.
Int.J.Mach.Tools&Manuf.42, 707-716.
Khrais, S.K., Lin, Y.J., 2006. Wear performance and tool performance of TiAl PVD coated inserts
during machining of AISI 4140 steel. Wear 262, 64-69.
Kumar, A.S., Durai, A.R., Sornakumar, T., 2003. Machinability of hardened steel using alumina
based ceramic cutting tools. Int. J.Refrac.Met.& Hard Mater.21, 109-117.
Kumar, A.S., Durai, A.R., Sornakumar, T., 2006. Wear behavior of alumina based ceramic cutting
tools on machining stainless steel and EN24 steel. Tribol.Inter. 39, 191-197.
Lim, C.Y.H., Lau, S., Lim, C., 2001. Work materials and effectiveness of coated tool.
Surf.&Coat.Technol.146-147, 298-304.
Lima, J.G., Avila, R.F., Abrao, A.M., Faustin, M., Davim, J.P., 2005. Hard turning:AISI 4340 high
strength alloy steel and AISI D2 cold work tool steel. J.Mater.Process.Technol.169, 388-395.
Luo, S.Y., Liao, Y.S., Tsai, Y.Y.,1999. Wear behaviour of CBN cutting tools when turning AISI
4340 hardened alloy steel. J.Mater.Proces.Technol. 88, 114-121.
Montogomery, D.C., 2001. Design and Analysis of Experiments, fourth ed.,Wiley, New York.
M’Saoubi, R., Chandrasekaran, H., 2004. Investigation of the effects of tool micro-geometry and
coating on tool temperature during orthogonal turning of quenched and tempered steel.
J.Mach.Tools&Manufac.44, 213-224.
Ozel, T., Karpak, Y., 2005. Predictive modeling of surface roughness and tool wear in hard turning
using regression and neural networks. Int.J.Mach.Tools&Manuf.45, 467-479.
Ozel, T., Nadgir, A., 2002. Prediction of flank wear by using back propagation neural network
modelling when cutting hardened H-13 steel with chamfered and honed CBN tools.
Int.J.Mach.Tools&Manufac. 42, 287-297.
Page 24 of 38
Accep
ted
Man
uscr
ipt
24
Pawade, R.S., Joshi, S.S., Brahmankar, B.K., 2008. Effect of machining parameters and cutting
edge geometry on surface integrity of high speed turned Inconel 718.
J.Mach.Tools&Manufac.48, 15-28.
Poulachon, G., Moisan, A., Jawahir, I.S., 2001. Tool wear mechanisms in hard turnining with
PCBN tools. Wear 250, 576-586.
Poulachon, G., Bandyopadhyay, B.P., Jawahir, I.S.,Pheulpin, S., Seguin, E., 2004. Wear behavior
of CBN tools while turning various hardened steels. Wear 256, 302-312.
Poulachon, G., Albert, A., Schluraff, M., Jawahir, I.S., 2005. An experimental investigation of
work material microstructure effects on white layer formation in PCBN hard turning.
Int.J.Mach.Tools&Manufac. 45, 211-218.
Risbood, K.A., Dixit, U.S., Sahasrabudhe, A.D., 2003. Prediction of surface roughness and
dimensional deviation by measuring cutting forces and vibrations in turning process. J.
Mater.Proc.Technol.132, 203-214.
Sahin, Y., 2001. Metal Cutting Prenciples Vol. 2. Nobel Yayın&Dağıtım, Ankara, Turkey
(Turkish).
Sahin, Y., 2006. Machinability of AISI 1050, 4140 and E52100 steel’s by different cutting tools
and development of statistical models. Research Report of Gazi University, TEF 07/2003-38
Number Coded Project, Ankara, Turkey.
Sahin, Y., Motorcu A.R., 2004. Prediction of surface roughness in the machining the carbon steels
by cutting tools. In: S.Ghosh., J.K.Lee., J.C.Castro (Eds.), AIP Conf.Proc.712, Proceedings
of 8th Int.Confer.on Numerical Methods in Industrial Forming Applications, Ohio State
University, OH, USA, pp.1414-1422.
Sahin, Y., Motorcu, A.R., 2008. Surface roughness model in machining hardened steel with cubic
boron nitride cutting tools. Int.J.Refrac.Met.&Hard Mater. 26, 84-90.
Sahin, Y., Motorcu, A.R., 2004. A model for surface roughness in turning AISI 4140 steel using
coated carbide cutting tools. In: M.Akkok., A.Erdem., S.Engin Kılıc., E.I.Konuksever.,
Page 25 of 38
Accep
ted
Man
uscr
ipt
25
E.Tonuk (Eds.), Proceedings of 11th Int. Conf.on Mach.Des.&Prod. UMTIK2004, Middle
East Technical University (METU), Antalya/Turkey, pp.269-284.
Sahin, Y., Sur, G., 2005. The effects of various multilayer ceramic coatings on the wear of carbide
cutting tools when machining metal matrix composites. Surf.&Coat.Technol.199, 112-117.
Sick, B., 2002. On-line and indirect tool wear monitoring in turning with artificial neural
netwworks: a review of more than a decade of research. Mechan. Syst.&Sig.Proces.16, 487-
546.
Scheffer, C., Kratz, H., Heyns, P., Klocke, F., 2003. Development of a tool wear monitoring system
for hard turning. Int.J.Mach.Tools&Manufac. 43, 973-985.
Thiele,. J.D., Melkote, S.N., R.A.Reascoe., T.R.Watkins., 2000. Effects of cutting edge geometry
and workpiece hardness on surface residual stresses in finish hard turning of AISI52100
steel. ASME J.Manufac. Sci.& Eng.122, 467-479.
Yang, W.H., Tarng, Y.S.,1998. Design optimization of cutting parameters for turning operations
based on the Taguchi method. J.Mater.Proces.Technol. 84, 123-129.
Yen, Y.C., Jain, A., Altan, T., 2004. A finite-element analysis of orthogonal machining using
different tool edge geometries. J.Mater.Proces.Technol.146, 72-81.
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FIGURES
Fig. 1. Main effect plots for tool lives in machining steels: a) S/N ratio (dB), b) Mean (min)
Fig.2. Surface plots of tool lives (Tmean) vs machining varibles (V, f, TH). a) Cutting speed versus
feed rate, b) Cutting speed versus hardness of tool, c) Feed rate versus hardness of tool.
Figure
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196140100
42
39
36
33
30
0,11760,08400,0600
366022502145
42
39
36
33
30
V
Mea
no
fSN
rati
os
(dB
)
f
TH
Main Effects Plot for SN ratios
a)
196140100
120
100
80
60
40
0,11760,08400,0600
366022502145
120
100
80
60
40
V
Mea
no
fto
ol
life
(min
)
f
TH
Main Effects Plot for Means
b)
Fig. 1. Main effects plot for tool lives in machining steels: a) S/N ratio (dB), b) Mean (min)
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0
0
0,12
,100
40
80
0,08
120
10150 0,06
200
Tmean
f
V
a)
3
3500
0000
40
80
2500100
120
1502000
200
Tmean
TH
V
b)
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3
3500
0000
40
80
25000,06
120
0,080,10 2000
0,12
Tmean
TH
f
c)
Fig.2. Surface plots of tool lives (Tmean) vs. machining varibles (V, f, TH). a) Cutting speed versus
feed rate, b) Cutting speed versus hardness of tool, c) Feed rate versus hardness of tool
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TABLES
Table 1. Cutting tool’s type, geometry , designation and some properties
Table 2. Experimental design and results for tool lives and their S/N ratios
Table 3. S/N response table of tool lives in machining hardened steels
Table 4. Optimumum levels of control factors
Table 5. Confirmation test results and comparison with calculated values
Table 6. Experimental results produced by different cutting tools and their theoretical values with
absolute average errors for first and second order models
Table 7. Results of analysis of variance for tool lives in machining hardened steels
Table
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Table 1. Cutting tool’s type, geometry, designation and some properties
Type of cutting
tool
Tool
designation
Chemical composition of
material
Hardness
(HV)
Thermal
conductivity
(W.m-1K-1)
Mixed ceramic
tool (KY1615)
TNGA
160408
Al2O3 (70 %)
+TiC (30%)
2145 28
Coated ceramic
tool (KY4400)
TNGA
160408
Al203 (70%)+ TiC (30
%)+TiN
2250 32
Cubic boron
nitride
(CBN/TiC)
CNGA12040
8-L0
CBN (50 %)+TiC
(40%)+WC(6%)+AlN,AlB
2 (4%)
3660 44
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Table 2. Experimental design and results for tool lives and their S/N ratios
Control factors and their uncoded values
Experimental and theoretical values
Trial
number
V(m/min) f (mm/rev) TH
(HV)
Measured tool life ,T
(min)S/N ratio
(dB)
1 100 0.06 2145 114 41.138
2 100 0.084 2250 117.5 41.401
3 100 0.1176 3660 121 41.656
4 140 0.06 2250 108.5 40.709
5 140 0.084 3660 110 40.828
6 140 0.1176 2145 17.5 24.861
7 196 0.06 3660 97.5 39.780
8 196 0.084 2145 10.1 20.086
9 196 0.1176 2250 15.1 23.580
Mean S/N ratio (dB) 34.893
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Table 3. S/N response table of tool lives in machining hardened steels
Average S/N ratio (dB)Symbol Control factors
Level 1 Level 2 Level 3Max.-Min.
V Cutting speed (m/min) 41.4 35.47 27.82 13.58
f Feed rate (mm/rev.) 40.54 34.11 30.03 10.51
TH
Cutting tool’s
hardness (HV) 28.70 35.23 40.75 12.06
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Table 4. Optimumum levels of control factors
Main control factors Symbol Optimum level Optimum value
Cutting speed V 1 100
Feed rate F 1 0.06
Hardness of cutting tool TH 3 CBN/TiC
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Table 5. Confirmation test results and comparison with calculated values
Optimal control
factors
Verification test results for tool
life and S/N ratio, ηi,ver
Calculation test results
for tool life and S/N
ratio, ηi,cal
V f TH Tver (min) S/N ratio (dB) S/N ratio
(dB)
Tcal (min)
Difference
(dB)
S/N ratio (dB) based on optimal parameters (1,1,3)
1 1 3 165.510 44.376 52.905 398.107 8.529
S/N ratio (dB) based on ANOVA results (1)
1 - - - - 41.40 117.489 2.976
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Table 6. Experimental results produced by different cutting tools and their theoretical values with some errors for first and second order models
Experimental Regression
equation
Exponential equation Second order equation n
terms of main in terms of
interaction effects
Trial
num.
Average
measured
tool
life,T
(min)
Theoretic
al tool
life (min)
Absol.
errors,
%
lnTmean Theoretic
al tool
life
Absol.
errors,
%
Theoretical
tool life
(min)
Absol.
errors,
%
Theoretical
tool life
(min)
Absol.
errors, %
1 114.0 123.33 -8.1 4.736 5.00 -5.57 148.41 -30.00 131.72 -15.54
2 117.5 103.82 11.69 4.766 4.496 5.69 89.65 23.37 100.00 14.95
3 121.0 117.36 3.00 4.796 4.899 -2.17 134.10 -10.82 120.56 0.36
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4 108.5 95.13 2.18 4.687 4.319 -7.85 75.11 30.77 93.36 13.95
5 110.0 117.84 -7.12 4.70 4.723 -0.47 112.50 -2.27 113.88 -3.52
6 17.5 36.74 -109.9 2.862 3.010 -5.17 20.28 -15.94 29.13 -66.40
7 97.5 96.51 1.01 4.580 4.546 0.74 94.25 3.33 96.99 0.52
8 10.1 24.58 -143.33 2.313 2.833 -22.52 16.99 -68.31 20.16 -99.60
9 15.1 -4.10 127.15 2.715 2.327 14.29 10.25 32.11 -5.38 64.3
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Man
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Table 7. Results of analysis of variance for tool lives in machining hardened steels
Symbol Degree of
freedom
(DF)
Sum of
squares (SS)
Mean of
squares (MS)
F-calculation F-table Contribution, P
(%)
V 2 278.102 139.051 9.178 9.00 41.63
f 2 168.450 84.225 5.563 9.00 25.22
TH 2 218.312 109.156 7.209 9.00 32.68
Error 2 30.28 15.14 - - 4.53
Total 8 667.892 83.486 100