CHAPTER – 2 LITERATURE REVIEW AND PROBLEM...
Transcript of CHAPTER – 2 LITERATURE REVIEW AND PROBLEM...
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CHAPTER – 2
LITERATURE REVIEW AND PROBLEM FORMULATION
This chapter is intended to provide background information relevant to this
research. The recent research in hard turning is reviewed so that the importance of
this research in the context of other work will be evident. Gaps in the existing
study, problem formulation, objectives, methodology and scope of study have also
been addressed in this chapter. The literature review is categorized into the
following areas: past work done on residual stresses, surface roughness and tool
wear.
2.1 Literature Review
Numerous investigations have been done by the researchers reflecting the effect of
cutting parameters, Cutting speed, feed rate, depth of cut and nose radius on the
surface roughness, residual stresses, and tool wear and their combinations in hard
turning and summary of their work is illustrated below and tabulated in the
following subsections categorize their research conclusions in the same field.
2.1.1 Literature review on effect of cutting parameters on surface roughness
The following Tables (2.1- 2.6) summarize the Literature review conducted by the
various researchers.
Table 2.1 Summary of literature review-Surface roughness Author/Year Modeling Tech. Workpiece
Material / Tool
material
Cutting Parameters
Thiele, J. D.
Melkote, S.N.
(1999)
ANOVA
AISI 52100
steel /CBN
Edge Reparation 22.86
Hone 93, 98
Hone 121, 92
Hone 25.4
Chamfer (mm)
34
Work piece hardness (HRC) 41, 47, 57
Feed rate (mm/rev) 0.05, 0.10, 0.15
Kopac, J.
Bahor, M.
Sokovic, M.
(2002)
Taguchi
Techniques.
Cold-formed
carbon steel
C15 E4 (ISO) /
Cermet CCMT
09T308 NFP
T12A
Cutting speed, (m/min) 250- 400
Depth of cut (mm) 0.3- 0.5
Chou, Y.K.
Song, H.
(2004)
Not defined AISI 52100/
Alumina,
titanium-
carbide
composite
(70% Al2O3 and
30% TiC)
Nose radius (mm) 0.8, 1.6, 2.4.
Cutting speed (m/sec) 2-3
Feed rate (mm/rev) 0.05–0.6
Depth of cut (mm) 0.2
Flank Wear (mm) 0–0.2.
Noordin, M.Y.
Venkatesh, V.C.
Sharif, S.
Elting, S.
Abdullah, A.
(2004)
ANOVA AISI 1045 steel
bars / coated
carbide
Cutting speed (m/min) 240-375
Feed (mm/rev) 0.18- 0.28
Grzesik, W.
Wanat, T.
(2006)
Not defined AISI 5140
(DIN 41Cr4)/
conventional
and wiper
ceramic inserts
Turning with conventional tools
Feed (mm/rev) 0.04–0.4
Depth of cut (mm) 0.25
Cutting speed (m/min) 100
Nose radius (mm) 0.8
Turning with wiper tools
Feed (mm/rev) 0.1–0.8
Depth of cut (mm) 0.25
Cutting speed (m/min) 100
Thamizhmanii, S.
Saparudin, S.
Hasan, S.
(2007)
Taguchi
Techniques
ANOVA
SCM 440 alloy
steel / coated
ceramic
Cutting speed (m/min) 135, 185, 240
Feed rate (mm/rev) 0.04, 0.05,0.063
Depth of cut (mm) 1.00, 1.50
Singh, D.
Rao, P.V.
(2007)
The Response
Surface
Methodology.
ANOVA
AISI 52100) /
Mixed ceramic
inserts
Cutting speed (m/min) 100, 150, 200
Feed (mm/rev) 0.10,0.20, 0.32
Effective rake angle (°) 6, 16, 26
Nose radius (mm) 0.4, 0.8, 1.2
Lalwani, D.L.
Mehta, N.K.
Jain, P.K.
(2008)
Response Surface
Methodology
(RSM)
ANOVA
MDN250 steel
/Ceramics
Cutting speed (m/min) 55,74, 93
Feed rate (mm/rev) 0.04, 0.08,0.12
Depth of cut (mm) 0.1, 0.15, 0.2
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Davim, P.J.
Gaitonde, V.N.
Karnik, S.R.
(2008)
ANN Models 9SMnPb28k
(DIN) /
cemented
carbide inserts.
Feed rate (mm/rev) 0.10, 0.16, 0.25
Cutting speed (m/min) 71,141, 283
Depth of cut (mm) 0.50, 0.75,1.00
Jayant, A.
Kumar, V.
(2008)
Taguchi
Techniques
ANOVA
AISI 4140/
carbide insert
tool
Cutting speed (m/min) 100, 150, 200
Feed rate (mm/rev) 0.1, 0.15, 0.2
Depth of cut (mm) 1.0, 1.5, 2.0
Sharma, V.S.
Sharma, S.K.
Sharma, A.K.
(2008)
Artificial neural
network model
(ANN)
Adamite/
coated carbide
insert
(CCMT090304)
Cutting speed (m/min) 36.6-196
Depth of cut (mm) 0.3-1.5
Feed (mm/rev) 0.1-0.27
Approaching angle (°) 45-90
Ramesh, S.
Karunamoorthy, L.
Palanikumar, K.
(2008)
Taguchi
techniques
ANOVA
Alpha-beta
titanium alloy
(Grade 5) /
CVD–(TiN-
TiCN-Al2O3-
TiN) coated
carbide
Cutting speed (m/min) 40, 60, 80
Feed rate (mm/rev) 0.13, 0.179, 0.22
Depth of cut (mm) 0.50, 0.75, 1.00
Cakir, M.C.
Ensarioglu, C.
Demirayak, I.
(2009)
Mathematical
Modeling.
Linear Model
Second Order
Model
Exponential
Model
Cold-work tool
steel AISI
P20/ carbides
Inserts
Cutting speed (m/min) 120, 160, 200
Feed rate (mm/rev) 0.12, 0.18, 0.22.
Cutting depth (mm) 1, 1.5, 2
Kahraman, F.
(2009)
Response Surface
Methodology
(RSM)
Regression
Modeling
4140 steel /
HSS
Cutting speed (m/min) 16, 47, 92, 137, 167
Feed rate (mm/rev) 0.032, 0.1, 0.2, 0.3,
0.368
Depth of cut (mm) 0.160, 0.5, 1, 1.5, 1.84
Prasad, M.V.R.D.
Janardhana, G.R.
Rao, D.H.
(2009)
The results were
analyzed
statistically for
Signal to Noise
ratios
En31 / PCBN
Cutting speed (m/sec) 91, 137, 183
Feed (mm/rev) 0.076, 0.114, 0.152
Depth of cut (mm) 0.1, 0.15, 0.2
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Suhail, A.H.
Ismail, N.
Wong, S.V.
Abdul Jalil, N.A.
(2010)
Taguchi
Techniques.
ANOVA
Medium carbon
steel AISI 1020
/ CNMG 432
TT5100
Cutting Speed (RPM) 950, 1150, 1400
Feed (mm/rev) 0.05, 0.1, 0.15
Depth of cut (mm) 0.5, 1.0, 1.5
Chavoshi, S.Z.
Tajdari, M.
(2010)
Artificial Neural
Network (ANN)
Regression
Modeling
ANOVA
AISI 4140 /
CBN
Depth of cut (mm) 0.3
Feed (mm/rev) 0.1
Hardness (HRC) 35-65
Spindle speed (RPM) 2500-3000
Thiele et al. (1999) investigated that the effect of edge hone on the surface and
they found that the large edge hones resulted in higher average surface roughness
than small edge hones. The effect of the two-factor interaction of the edge
geometry and workpiece hardness on the surface roughness was also found to be
important. The study also concluded that the force components were found to be
significant mainly for the 93.98 and 121.92 mm edge hones.
Kopac et al. (2002) reflected in their study that the cutting speed results in a
smoother surface followed by cutting depth.
Chou et al. (2004) investigated that large tool nose radii only gave finer surface
finish but on the other hand, the specific cutting energy slightly increased with an
increase in tool nose radius resulting in comparable tool wear as compared to small
nose radius tools.
Noordin et al. (2004) in their mathematical model revealed that the feed was the
most significant factor that influences the surface roughness followed by the
cutting speed.
Grzesik and Wanat (2006) did the hard turning at constant cutting speed, depth of
cut, nose radius and at variable feed rates in conventional and wiper cuttings. It
was concluded that the turning with wiper inserts provides comparable surface
roughness to the effects obtained at lower feed rate during the turning with
conventional tools.
Thamizhmanii et al. (2006) used the Taguchi method and study had shown that the
depth of cut had significant role to play in producing lower surface roughness
followed by feed while cutting speed had lesser role on surface roughness.
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Singh and Rao (2007) predicted that the feed was the dominant factor determining
the surface finish followed by nose radius and cutting velocity. Though, the effect
of the effective rake angle on the surface finish was less, the interaction effects of
nose radius and effective rake angle were considerably significant.
Lalwani et al.(2008) predicted that the cutting speed had no significant effect on
cutting forces and surface roughness. A good surface roughness can be achieved
when cutting speed and depth of cut are set nearer to their high level of the
experimental range (93 m/min and 0.2 mm) and feed rate is at low level of the
experimental range (0.04 mm/rev).
Davim et al. (2008) concluded that cutting speed and feed rate had significant
effects in reducing the surface roughness, while the depth of cut had the least
effect. They further concluded that the surface roughness can be reduced with the
increase in cutting speed and also with the reduction in feed rate. The combination
of low feed rate and high cutting speed resulted in minimizing the surface
roughness values.
Jayant and Kumar (2008) showed that the use of high cutting speed, low feed rate
and low depth of cut led to better surface finish and low cutting force. The better
surface finish is obtained at a cutting speed of 200 m/min, feed rate mm/rev. and
depth of cut 1.5 mm. The smaller cutting force can be at a cutting speed of 200
m/min, feed rate 0.1 mm/rev and depth of cut 1 mm.
Sharma et al. (2008) concluded that the surface roughness was positively
influenced with feed and it showed negative trend with approaching angle, speed
and depth of cut. The neural network model for cutting force and surface
roughness could predict with moderate accuracy.
Cakir et al. (2009) revealed that among the cutting parameters, feed rate had the
greatest influence on surface roughness followed by cutting speed. Higher feed
rates led to higher surface roughness values, whereas cutting speed had a contrary
effect and cutting depth had no significant effect.
Ramesh et al. (2008) investigated that the feed was the factor which influenced
surface roughness followed by cutting speed. The surface roughness increased with
increase in feed but decreased with increase in cutting speed. The variance analysis
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for the two factor interaction model also showed that the depth of cut was the least
significant parameter.
Kahraman (2009) observed that cutting speed and depth of cut had negative
influence whereas the feed rate had positive influence on the surface roughness.
The surface roughness of AISI 4140 steel decreased with an increase in cutting
speed and depth of cut whereas it increased with increase in feed rate. It was
observed that the combination between high cutting speed and high feed rate
resulted in a considerable reduction in surface roughness and also the combination
between high cutting speed and high depth of cut resulted in a considerable
reduction in surface roughness.
Prasad et al. (2009) investigated that surface roughness values increased with
increase in speed and observed that the depth of cut was not influencing much on
roughness values, but the roughness values were varying nonlinearly with increase
variation of feed. Strong interaction among all input process parameters was
observed.
Suhail et al. (2010) found that the feed rate had the strongest influence on surface
roughness followed by cutting speed and last by depth of cut.
Chavoshi and Tajdari (2010) concluded that hardness had a significant effect on
the surface roughness and with the increase of hardness until 55 HRC, the surface
roughness decreased; afterwards surface roughness represented the larger values
increasingly. The 55 HRC workpiece represented the best surface roughness at
different spindle speeds. Spindle speed in range of 2,500–3,000 rpm had a partial
effect on the surface roughness. The experiments were conducted at constant depth
of cut and feed.
Most of the researchers have concluded in their investigations that the feed is
having greater influence on surface roughness followed by cutting speed and depth
of cut. However, one researcher had shown that the depth of cut had a significant
role to play in producing lower surface roughness followed by feed while cutting
speed had lesser role on surface roughness.
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2.1.2 Literature review on effect of cutting parameters on residual stress
Table 2.2: Summary of literature review- Residual stress
Author/Year Modeling Technique Workpiece Material /
Tool material
Cutting Parameters
M’Saoubi, R.
Outeiro, J.C.
Changeux, B.
Lebrun, J.L.
Dias, A.M.
(1999)
Not defined AISI 316L / Uncoated
and coated tungsten
carbide tools.
Cutting speed (m/min)75- 200
Feed rate (mm/rev) 0.1- 0.3
Width of cut (mm) 4-6
Thiele, J. D.
Melkote, S.N.
(2000)
Not defined AISI 52100 / PCBN Workpiece hardness (HRC) 57
Hone(µm) 121.9
Feed rate (mm/rev) 0.15 Chamfer
(µm) 25.4
Workpiece hardness (HRC) 41
Hone (µm) 22.9, 121.9
Feed rate (mm/rev) 0.1
El-Axir
(2002)
Response Surface
Methodology.
Five different
materials namely;
stainless steel- 304,
steel-37, 7001 and
2024-aluminum alloys
and brass/HSS
Cutting speed (m/sec) 0.236,
0.467, 0.93, 1.88, 3.77
Feed (mm/rev) 0.025, 0.05, 0.10,
0.2, 0.4
Tensile Strength (kg/mm2) 177,
255, 360, 490, 615
Rech, J.
Moisan, A.
(2003)
Not defined Case-hardened
27MnCr5 / CBN
Cutting speed (m/min) 50 - 250
Feed rate (mm/rev) 0.05-- 0.2
Depth of cut (mm) 0.15
Dahlman, P.
Gunnberg, F.
Jacobson, M.
(2004)
Three Different
One-Factor Designs
Were Used, Since Each
Factor Needed To Be
Closely Investigated.
AISI 52100 /CBN Test 1. Rake Angle (°) -6, -21, -
41,-61
Depth of cut (mm) 0.1
Feed (mm/rev) 0.1
Speed Constant (m/min) 110
Test 2
Rake Angle (°) 21
Depth of cut Constant (mm) 0.1
Feed (mm/rev) 0.1, 0.2, 0.3, 0.5
Speed (m/min) 110 Test 3
Rake Angle Constant (°) -21
Depth of cut (mm) 0.1, 0.25,0.45
Feed Constant (mm/rev) 0.1
Speed (m/min) 110
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Liu, M.
Takagi, J.
Tsukuda, A.
(2004)
Not defined JIS SUJ2/CBN
Nose radius (mm) 0.4, 0.8, 1.2
Cutting speed (m/min) 120 Feed
(mm/rev) 0.1 Depth of cut (mm)
0.1, 0.2
Capello, E.
(2005)
ANOVA
The UNI-ISO Fe370,
C45 and
39NiCrMo / Carbide
(TCMT16T302-04-
08)
Depth of cut (mm) 0.2, 0.5, 1
Feed rate (mm/rev) 0.05, 0.1, 0.25
Nose radius (mm) 0.20, 0.4, 0.8
Entrance Angle (°) 45, 60, 90
Hua, J.
Umbrello, D.
Shivpuri, R.
(2006)
FEM
AISI 52100
Not defined
Umbrello, D.
Ambrogioa, G.
Filice, L.
Shivpuri, R.
(2007)
ANN Approach AISI 52100
Cutting speed (m/min) 120, 180
Feed rate (mm/rev) 0.35, 0.85
Work Piece Hardness (HRC) 56,
62 Hone edge radius (mm) 0.025,
0.15 Rake Angle (°) −6, -11, -15
Chamfer Angle (°) 10, 20, 30
Batalha, G.F.
Delijaicov, S.
Aguiar, J.B.
Bordinassi, E.C.
Stipkovic Filho,M.
(2007)
Factorial
Empirical Model
DIN 100 CrMn6
hardened steel / CBN
Cutting speed (m/min) 150, 210
Feed (mm/rev) 0.05, 0.15 Depth
of cut (mm) 0.05, 0.2
Nose radius (mm) 0.4, 0.8
Ulutan, D.
Alaca, B.E.
Lazoglu, I.
(2007)
ANOVA
100Cr6 (JIS SUJ2)
Cutting speed (mm/min) 120
Feed (mm/rev) 0.1
Nose radius (mm) 0.4, 0.8, 1.2
Depth of cut (mm) 0.1, 0.2
Outeiro, J.C
Pina, J.C
M’saoubi, R.
Pusavec, F.
Jawahir, I.S.
(2008)
3D Numerical Modelling
AISI 316L and
Inconel 718/ uncoated
and PVD coated
(TiAlN-2 mm)
cemented carbide
Cutting edge radius (mm) 25, 44
Nose radius (mm) 0.8
Rake angle (o) 6, 4.29
Cutting speed (m/min) 55, 70, 125
Feed (mm/rev) 0.15, 0.2, 0.05
Depth of cut (mm) 0.5, 2.5
Xueping, Z. Erwei, G. Liu, R. (2009)
Taguchi
Hardened bearing
steel / CBN
Cutting speed (m/sec) 0.5, 2.5, 4.5
Depth of cut (mm) 0.025, 0.080,
0.135
Feed rate (mm/rev) 0.05, 0.15,
0.25
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Jaharah, A.G.
Wahid, S.W.
Hassan, C.H.
Nuawi, M.Z.
Mohd Nizam
Rahman, A.
(2009)
FEM
ANOVA
AISI 1045/ Uncoated
Carbide
Cutting speed (m/min) 100 – 300
Feed (mm/rev) 0.15
Nose radius (mm) 0.4 Depth of
cut (mm) 0.18
Rizzuti, S.
Umbrello, D.
Filice, L.
Settineri, L.
(2010)
FEA
AISI 1045/ Uncoated
carbide TNMG-432
Cutting speed (m/min) 175 Feed
rates (mm/rev) 0.05, 0.2
Tool edge radius (mm) 15, 30, 55,
75
Caruso, S.
Outeiro, J.C.
Umbrello, D.
M’saoubi, R.
(2010)
FEM
AISI H13 / PCBN Cutting speed (m/min) 100 – 200
Feed rate (mm/rev) 0.05 - 0.15
Width of cut (mm) 2.15
M'saoubi et al. (1999) predicted that the influence of feed rate on the generated
surface residual stresses was relatively small. It was seen however, that increasing
values of feed rate tend to increase the compressive stress values in the sub-
surface.
Thiele et al. (2000) conducted an examination of ‘through-thickness’ residual
stresses and showed that large edge hone tools produced deeper, more compressive
residual stresses than were produced by small edge hone tools or chamfered tools.
El-Axir (2002) showed that the residual stress continued to decrease across the
section becoming either tensile or compressive at large depths. The researcher
further investigated that the maximum residual stresses always occurs beneath the
machined surface rather than on the nearest layer to the machined surface.
Rech and Moisan (2003) revealed in their examination of the machined surfaces
using three-dimensional topography that feed rate was the main parameter that
influenced the surface roughness compared to the influence of cutting speed,
whereas cutting speed was the major parameter that influenced the residual stress
level. Cutting speed tend to increase the external residual stress, irrespective of the
feed rate in the range of 50 to 150 m/sec.
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Dahlman et al. (2004) revealed that rake inclination had the strongest influence on
the residual stresses. The compressive stresses became greater with increased feed
rate. Different cutting depths did not generate different stress levels. The results
showed that it was possible to produce tailor-made residual stress levels by
controlling the tool geometry and cutting parameters. A greater negative rake angle
gave higher compressive stresses as well as a deeper affected zone below the
surface. With increased rake angles, the maximum stress position was moved
further into the material. The result of tests performed revealed that compressive
stresses were always generated below the surface.
Liu et al. (2004) showed that the tool nose radius affected the residual stress
distribution significantly. It was further investigated that as the tool wear increased,
the residual stress at the machined surface shifted to tensile stress range and the
residual compressive stress beneath the machined surface increased greatly. The
tool nose radius affected the residual stress at the machined surface significantly at
early cutting stage. The residual stresses at the machined surface shifted to tensile
range with the increase of the tool nose radius. It was concluded that the effect of
the nose radius on the residual stress distribution decreased greatly with the
increase of the tool wear.
Capello (2005) predicted that the depth of cut does not influence the level of
residual stresses, while the main role was played by feed rate and nose radius, and
a mild influence was exerted by entrance angle. The optimal nose radius derived
from a compromise between residual stresses and surface roughness, as a large
nose radius enhances the surface finish but increases residual stresses, and vice
versa.
Hua et al. (2006) showed that the compressive residual stresses in both the axial
and circumferential directions of the machined surface can be obtained by
choosing a higher feed rate; Using the same cutting parameters, larger compressive
residual stresses were generated if the material was heat treated to higher
workpiece hardness; Large hone radius tool produced more compressive residual
stress and deeper beneficial length than small hone radius tool; Chamfer tool
helped to increase compressive residual stress but its effect was less than that of
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increasing the hone radius. Therefore, it was recommended that chamfer plus hone
radius should be used to obtain best residual stress profile.
Umbrello et al. (2007) presented a predictive model based on the artificial neural
network (ANN) approach that can be used both for forward and inverse
predictions. The three layer neural network was trained on selected data from
chosen numerical experiments on hard machining of 52100 bearing steel, the
numerical results showed that more compressive residual stress in both axial and
circumferential direction of the machined surface were obtained if higher values of
the feed rate were chosen.
Batalha et al. (2007) concluded that, to have high compression residual stresses,
the machining is to be done with low values of cutting depths, high feed rate values
and low values for the tool nose radius. The residual stresses were so much more
compressive, as larger the feed rate and the smaller the cutting depths parameters.
Ulutan et al. (2007) concluded in their study that the maximum value of
compressive stresses along the feed direction beneath the surface was observed to
decrease with increase in nose radius. Both these trends and magnitude of stresses
were matched closely by the simulations. Experimental measurements were
observed to die out at depths closer to the machined surface, whereas simulations
predict existence of residual stresses at deeper levels. With increasing depth of cut,
tensile residual stresses at the surface predicted by the model, decreased
consistently for all nose radii. As far as the effect of nose radius was concerned,
increasing nose radius led to a shift in tensile direction as opposed to simulation
results. For the maximum nose radius of 1.2 mm, stress along the feed direction
even became compressive if the depth of cut was increased to 0.2 mm in
experiments; on the other hand, the dependence of the surface stress on the depth
of cut was not clear.
Outeiro et al. (2008) concluded that the residual stresses were tensile at surface and
gradually shifted to compressive values beneath the surface before stabilizing at
the level corresponding to that found in the work material before machining
(around zero MPa). For the range of cutting conditions investigated, residual
stresses generated by turning AISI 316L were also tensile, and high at the
machined surface, although not as high as those obtained by turning Inconel 718.
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The higher surface residual stresses were generated when machining was done
with the uncoated tool than the coated tool. Moreover, higher residual stress values
were obtained on the transient surface than on the machined surface.
Xuepinga et al. (2009) in their experiments showed that compressive residual
stress dominated the hard turned surface up to a depth of 0.1mm for the average
residual stress along the subsurface, the cutting speed had the most significant
impact, followed by depth of cut, and finally feed rate. The optimal combination of
cutting speed, depth of cut, and feed rate was found to be (0.5 m/sec, 0.135mm,
0.25mm/rev) among.
Jaharah et al. (2009) concluded from the simulation results that the minimum
temperature of 605°C on the cutting edge was obtained using rake and clearance
angles of -5° and 5° respectively with cutting speed of 100 mm/min, and feed rate
of 0.15mm/rev. The minimum effective stress of 1700MPa was achieved using
rake and clearance angles of -5° and 5° respectively with cutting speed of
300mm/min, and feed rate of 0.25mm/rev. Analysis of ANOVA for cutting
temperature on the cutting edge revealed that the cutting speed contribute 80.17%,
followed by feed rate of 16.12%, clearance angle of 2.4% and rake angle of 1.31%.
Rizzuti et al. (2010) concluded that the tensile residual stresses were found on the
machined surface, while compressive residual stresses were observed below the
surface. It was demonstrated that the reliability of any FE numerical model for
predicting the residual stresses is strictly related to the proper prediction of both
mechanical and thermal aspects. Experimental residual stresses increased with an
increase in the edge radius up to a 30 micron edge radius; however, there was a
decrease in the values of residual stresses for edge radii of 55 and 75 µm.
Caruso et al. (2010) concluded that the surface residual stresses increase, becoming
more compressive, as those cutting speed, width of cut and feed rate increased. The
developed FE model was able to re-produce experimentally observed surface
residual stresses in orthogonal machining of AISI H13 tool steel. This work
showed that it was possible to simulate complex machining process, such as metal
cutting.
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It is also concluded from the study of above mentioned researchers that the
parameters like feed rate, cutting speed, depth of cut have a significant role in
producing compressive residual stresses. The surface residual stresses increase,
become more compressive, as the cutting speed, width of cut and feed rate
increase.
2.1.3 Literature review on effect of cutting parameters on tool wear
Table 2.3: Summary of literature review -Tool Wear
Author/Year Modeling
Tech.
Workpiece Material / Tool
material
Cutting Parameters
Endres, W.J.
Kountanya, R.K.
(2002)
Not defined AISI 1040/carbides
Corner radius (mm) 0.2, 0.8, 1.2,1.6
Feed (mm/rev) 0.022, 0.037, 0.083
Cutting speed (m/min) 183 Depth of
cut (mm) 2.5
Yen, Y.C. Jain, A. Altan, T. (2004)
FEM
AISI 1020/ Uncoated
cemented
carbide
For the hone tool
Cutting velocity (m/min) 130
Feed rate (mm/rev) 0.2
Rake Angle (◦) 12
Relief Angle (◦) 5
Edge Radius (mm) 0.01, 0.05, 0.1
For the chamfer tool
Cutting velocity(m/min) 130
Feed rate (mm/rev) 0.2
Rake angle (◦) −7
Relief angle (◦) 7
Chamfer angle (◦) 15, 25
Chamfer width (mm) 0.1, 0.2
Bosheh, S.S.
Mativenga, P.T.
(2006)
Not defined AISI H13/ Ceramic coated
(Al2O3
+ TiCN)
Depth of cut (mm) 0.05 Feed
(mm/rev) 0.1 Cutting speed (m/min)
100-700
Sharma, V.S.
Sharma, S.K.
Sharma, A.K.
(2007)
Adaptive
Neuro fuzzy
Inference
system
(ANFIS)
Cast iron (FG15) / Uncoated
carbide CCMT060204 TTS
Cutting speed (m/min) 94,188 Feed
(mm/rev) 0.06, 0.08 Depth of cut
(mm) 0.7
Singh, H.
(2008)
Taguchi design
ANOVA
En24/ TiC coated
carbide
Cutting speed (m/min) 190, 250,
310
Feed (mm/rev) 0.14,0.16, 0.18
Depth of cut (mm) 0.70, 0.85, 1.00
46
Thamizhmanii, S.
Hasan, S.
(2008)
Not defined AISI 440 C martensitic
stainless steel and SCM 440
alloy steels / CBN
Cutting speed (m/min) 100,125,
150, 175, 200 Feed (mm/rev) 0.10,
0.20, 0.30 Depth of cut (mm) 1
Quiza, R.
Figueira, L.
Davim. P.J.
(2008)
ANN Taguchi
Method
ANOVA
D2 AISI steel / Mixed
alumina inserts with ref.
CC650 (ISO code-CNGA
120408 T01020
Cutting speed (m/min) 80, 115, 150
Feed (mm/rev) 0.05, 0.10,0.15
Depth of cut (mm) 0.2
Sahin, Y.
(2009)
ANOVA AISI 52100/CBN Depth of cut (mm) 0.2 Feed rates
(mm/rev) 0.06, 0.084, 0.117 Cutting
speed (m/min) 100, 140, 196
Attanasio, A.
Ceretti, E.
Fiorentino, A.
Cappellini, C.
Giardini, C.
(2010)
3D FEM
Model
AISI 1045/ Uncoated carbide
tools
Feed (mm/rev) 0.1, 0.2 Cutting
speed (m/min) 200, 260 Depth of
cut (mm) 1.5
Nabahani (2001) has found that PCBN tools showed reduced tool flank wear and
delivered a good surface quality compared to the various carbide tools. The failure
of these tools is the result of plastic deformations under combined significant
mechanical and thermal stresses in the vicinity of the cutting edge and the low-
wear rate of the PCBN is primarily attributed to a reduced chemical reactivity in
contact of titanium alloys.
Endres and Kountanya (2002) showed that there was a clear effect of corner radius
on wear and it was further examined that a corner radius of around 0.8 mm
provided greatly reduced wear both at the lead edge and at the tool tip, and with
lower feeds it showed some shift of the wear-minimizing corner radius toward 1.2
mm.
Yen et al. (2004) in their study analyzed the effect of various tool edge geometries
on the process variables by using FEM cutting simulation. Accordingly an
engineering analysis of tool wear for the tool geometries is also possible, as tool
wear is directly related to cutting temperature, tool stresses and chip sliding
velocity. Furthermore, the tool edge geometry may be optimized in terms of
minimum tool wear for the given cutting conditions and tool and workpiece
materials.
47
Bosheh and Mativenga (2006) concluded that white layer depth reduced as the
cutting speed increased. It was clear that the flank wear increased with an increase
in cutting speed. An increase in cutting speed leads to reduction in the tool life.
The reason for the increase in the flank wear was the increase in temperature of the
cutting edge, as the cutting speed increased. Crater wear also increased with an
increase in cutting speed.
Sharma et al. (2007) constructed a model using Adaptive Neuro Fuzzy Inference
System (ANFIS). The model made use of cutting forces, tool accelerations and AE
(RDCavg) signals in order to estimate the tool wear. This technique provided a
method for fuzzy modeling procedure to learn information about a data set, in
order to compute the membership function parameters that best allow the given
input/output data. They further concluded that the constructed model is capable of
estimating the tool wear rate for particular cutting parameters (for which it has
been trained).
Singh (2008) concluded that cutting speed had much significant effect on tool life
followed by depth of cut and lastly feed.
Thamizhmanii and Hasan (2008) experimentally showed that the maximum flank
wear occurred at cutting speeds of 175 and 200 m/min, feed rate of 0.30 and 0.10
mm/rev respectively for stainless steel. The maximum flank wear measured at
cutting speed of 175 m/min at low feed rate of 0.10 for SCM 440. At cutting speed
of 150 and 175 m/min and feed rate of 0.30 mm/rev, the flank wear were observed
to be high.
Davim et al. (2008) revealed that higher cutting speeds results higher tool wear.
They argued that wear was due to two- and three-body abrasions, and that wear
was accelerated by interference between the reinforcement particles that were
associated with a critical weight percentage. Based on these observations, an
analytical model was developedto predict the critical reinforcement weight ratio as
a function of the densities of the reinforcement and the matrix and of the radius of
the reinforcement particles and the cutting tool edge.
Sahin (2009) indicated that the cutting speed was of higher significance but other
parameters were also having significant effects on the tool lives at 90% confidence
48
level. The CBN cutting tool showed the best performance than that of ceramic
based cutting tool.
Attanasio et al. (2010) showed in their proposed model that the increase of the
cutting velocity or of the feed rate generated deeper crater, while the crater position
and the crater extension were mainly influenced by the feed rate.
It has been observed that the tool life is dependent upon a number of factors but the
most prominent are tool material composition, cutting conditions and tool
geometry. The prominent cutting conditions affecting tool wear include: feed rate,
depth of cut, cutting speed, while tool geometry parameters affecting tool wear
include: rake angle for up-sharp tool, chamfer length and angle, rake angle for
chamfered tool, hone radius, rake angle for honed tool, tool nose radius. It is also
concluded that the cutting speed is having more influence on tool wear. The reason
for the increase in the tool wear is the increase in temperature of the cutting edge,
as the cutting speed increased.
2.1.4 Literature review on effect of cutting parameters on surface roughness
and residual stress
Table 2.4 Summary of literature review surface roughness and residual stress Author/Year Modeling
Tech.
Workpiece Material /
Tool material
Cutting Parameters
Arunachalam, M.
Mannan, M.A.
Spowage, A.C. (2004)
Not defined Inconel 718/ CBN and
ceramic
Cutting speed for CBN (m/min) 150,
225, 300, 375
Cutting speed for Mixed Ceramic
(m/min) 450
Depth of cut (m/sec) 0.05 - 0.5
Feed (mm/rev) 0.15
Gunnberg, F.
Escursell, M.
Jacobson, M.
(2006)
CCF Test
Plan
Fit Models
18MnCr5 case carburized
steel/ PCBN
Cutting speed (m/min) 110, 170, 230
Feed rate (mm/rev) 0.05, 0.10, 0.15
Cutting depth (mm) 0.05, 0.10, 0.15
Nose radius (mm) 0.8, 1.6, 4.5
Rake angle (◦) 6, 15, 21
Arunachalam et al. (2004) showed that mixed ceramic cutting tools induced tensile
residual stresses with a much higher magnitude than CBN cutting tools. It was
further investigated that the residual stresses and the surface roughness generated
by CBN cutting tools were more sensitive to cutting speeds than depth of cut. With
49
the increase in the cutting speed, the residual stress values changed from
compressive to tensile residual stresses. The use of coolant results in either
compressive residual stresses or lowers the magnitude of the tensile residual
stresses, whereas dry cutting always resulted in tensile residual stresses. They
suggested that round CBN cutting tools should be used at slow cutting speeds (150
m/min) and small depths of cut (0.05 mm) and with the use of coolant to achieve
compressive or minimal tensile Residual stresses and good surface finish.
Gunnberg et al. (2006) revealed that with the Increase in feed the higher
compressive stresses were generated. The cutting did not affect residual stresses. A
more negative rake angle produced more compressive stress in both models. It
further concluded that by controlling the cutting parameters, it was possible to
generate tailor-made stresses in the product, which can prolong the service life of
the machined component.
It can be concluded that the depth of cut is having less influence on residual
stresses as well as on surface roughness than cutting speed and feed.
2.1.5 Literature review on effect of cutting parameters on surface roughness
and tool wear:
Table 2.5 Summary of literature review surface roughness and tool wear
Author/Year Modeling Tech. Workpiece Material / Tool material
Cutting Parameters
Huang, Y.
Liang, S.Y.
(2004)
Analytical
Modeling
AISI 52100 / CBN Cutting speed (m/sec) 1.52, 2.29
Feed rate (mm/rev) 0.076, 0.168
Depth of cut (mm) 0.203, 0.102
Ozel, T.
Karpat, Y. (2005)
Regression
Neural Networks.
AISI H-13/CBN
Hardness (HRC) 51.3- 54.7
Edge Geometry Honed –Chamfered
Cutting speed (m/min)100 -200 Feed
Rates (mm/rev) 0.1-0.2
Tamizharasan, T.
Selvaraj, T. Noorul
Haq, A.
(2006)
Not defined Engine crank pin material
/ PCBN
Cutting speed (m/min) 100, 150, 200
Feed rate (mm/rev) 0.06, 010, 0.14
Depth of cut (mm) 0.2, 0.3, 0.4
50
Ozel, T. Karpat, Y. Figueira, L. Davim, J.P. (2007)
Multiple Linear
Regression
Models Neural
Network Models
AISI D2 steel / Ceramic
wiper inserts
Cutting speed (m/min) 80, 115, 150
Feed rate (mm/rev) 0.05, 0.10, 0.15
Depth of cut (mm) 0.2
Thamizhmanii, S.
Kamarudin, K.
Rahim, E.A.
Saparudin, A.
Hasan, S.
(2007)
Taguchi Design,
ANOVA
SCM 440 high strength
alloy steel / CBN
Cutting speed (m/min) 125, 175, 225
Depth of cut (mm) 0.20, 0.30, 0.40
Feed rate (mm/rev) 0.04, 0.05
Grzesik, W.
(2008)
Not defined DIN 41Cr4, AISI 5140 /
Ceramic
Conventional Turning SNGN 120408
T01020
Nose radius (mm) 0.8
Feed rate (mm/rev) 0.04–0.4 Depth
of cut (mm) 0.25
Cutting speed (m/min) 100
Turning With Wiper Tools CNGA
120408 T01020 WG
Feed Rates (mm/rev) 0.1–0.8
Depth of cut (mm) 0.25
Cutting speed (m/min) 100
Noordin, M.Y. Zainal, A.M. Hendriko, D.K. (2008)
Regression. AISI D2/ coated ceramic
Cutting speed (m/min) 115, 145, 183
Feed rate (mm/rev) 0.1, 0.125, 0.16
Kamely, M.A.
Noordin, M.Y.
Ourdjini, A.
Venkatesh, V.C.
Razali, M.M.
(2008)
Neural Network
Modeling
Particle Swarm
Optimization.
AISI D2/ CBN coated
with TiN/Al2O3/TiCN,
CVD and mixed ceramic
(Al2O3 + TiCN) coated
with TiN
Cutting speed (m/min) 100, 140, 200
Feed rate (mm/rev) 0.6
Depth of cut (mm) 0.4
Gusri, A.I.
Che Hassan, C.H.
Jaharah, A.G.
Yanuar, B.
Yasir, A.
Nagi, A.
(2008)
Taguchi Design
ANOVA
Ti-6Al-4V ELI / Coated
and uncoated cemented
carbide
Cutting speed (m/min) 55, 75, 95
Feed rate (mm/rev) 0.15, 0.25, 0.35
Depth of cut (mm) 0.10, 0.15, 0.20
Aneiro, F.M.
Coelho, R.T.
Brandão, L.C.
(2008)
ANOVA AISI 4340 / Coated
carbide
Cutting speed (m/min) 150, 200
Feed rate (mm/rev) 0.07, 0.17 Depth
of cut (mm) 0.2, 0.4
51
Huang and Liang (2004) conducted the comparison between the predicted model
and the measurement and showed reasonable agreement. The results suggested that
adhesion was the main wear mechanism over the investigated range of cutting
conditions.
Ozel and Karpat (2005) concluded that the decrease in the feed rate resulted in
better surface roughness but slightly faster tool wear development, and the increase
in cutting speed resulted in significant increase in tool wear development but also
resulted in better surface roughness. Increase in the workpiece hardness resulted in
better surface roughness but higher tool wear. Overall it was noticed that CBN
inserts with honed edge geometry performed better both in terms of surface
roughness and tool wear development.
Ezugwu et al. (2005) have developed a three-layered BP ANN model for the
analysis and prediction of the relationship between cutting conditions and process
parameters. The inputs of ANN were the cutting speed, feed rate, depth of cut,
Noordin, M.Y.
Zainal, A.M.
Hendriko, D.K.
(2008)
ANOVA
Regression
Model
AISI D2/ Coated ceramic
tool
Cutting speed (m/min) 115, 145, 183
Feed (mm/rev) 0.1, 0.125, 0.16
Yallese, M.A. Chaoui, K. Zeghib, N. Lakhdar, B. Rigal, J.B. (2009)
Mathematical
Model
100Cr6 (AISI 52100)/
CBN
Feed Rates (mm/rev) 0.08, 0.2
Depth of cut (mm) 0.2, 0.6
Cutting speed (m/min) 90, 180
Gaitonde, V.N.
Karnik, S.R.
Figueira, L.
Davim, P.J
(2009)
RSM
based
mathematical
models
AISI D2 /Ceramic Cutting speed (m/min) 80, 115, 150
Feed rate (mm/rev) 0.05, 0.10, 0.15
Machining time (min) 5, 10, 15
Dawson, T.G.
Kurfess, T.R.
(2000)
Not defined AISI52100/CBN Cutting speed (m/min) 91.4, 182.9
Feed rate (mm/rev) 0.076, 0.152
Depth of cut (mm) 0.203, 0.508
Pavel, R.
Sinram, K.
Combs, D.
Deis, M.
Marinescu, I.
(2011)
ANOVA 1137 Steel Shafts/ PCBN Nose radius (mm) 0.8
Feed rate (mm/rev) 0.025, 0.229
Cutting speed (m/min) 100 - 150
Depth of cut (mm) 0.102 - 0.254
52
cutting time, and coolant pressure. The outputs were tangential force, axial force,
spindle motor power, machined surface roughness, average flank wear, maximum
flank wear and nose wear. A very good performance of the neural network, in
terms of agreement with experimental data, was achieved. The model can also be
used for the optimization of the cutting process for efficient and economic
production.
Tamizharasan et al. (2006) showed that at increased speeds the performance of
operation changed to a considerable extent. The depth of cut had only negligible
effect on surface finish and flank wear of cutting tool, and the feed, too, had little
effect.
Ozel et al. (2007) revealed that the best tool life was obtained in lowest feed rate
and lowest cutting speed combination.
Thamizhmanii et al. (2007) in their study found that cutting speed was significant
parameter to achieve lowest surface roughness as main effects and interactions
between ‘cutting speed-feed rate’ and ‘cutting speed - depth of cut’ was significant
on surface roughness which contributes 32 % and 13 % of the total variation. The
depth of cut had less significant effect on the roughness. On the flank wear result,
cutting speed had significant effect on tool wear. The depth of cut also had effect
on flank wear and it was clear that by reducing the depth of cut, the flank wear can
be controlled.
Grzesik (2008) investigated that in finish MC-HT (mixed ceramic hard turning)
wear of tool flank faces were active secondary cutting (trailing) edge.
Noordin et al. (2008) stated that the empirical surface roughness models show such
that the obtained surface roughness was proportional to feed and inversely
proportional to cutting speed. Considering both the tool life and the surface
roughness, a combination of low cutting parameters is the optimum solution to
make the coated ceramic tools last long.
Kamely et al. (2008) revealed that tool life decreased with increase in cutting
speeds. In the tool life testing, it was shown that mixed (Al2O3 + TiCN) ceramic
coated with TiN performed better than CBN cutting tools. At lower cutting speed
of 100 m/min, the lowest average surface roughness was obtained by using the
53
CBN-Low CVD coated with TiN/Al2O3 /TiCN, followed by mixed ceramic coated
with TiN, CBN-High coated with TiN/Al2O3 /TiCN, CVD tools. Under the present
experimental conditions the results showed that mixed ceramic cutting tools
produced better surface finish (0.28 – 0.4 µm) at all cutting speed compared to
coated CBN cutting tools i.e.(0.34-0.55 µm).
Gusri et al. (2008) showed that the cutting speed and type of tool had a very
significant effect on the tool life, and the feed rate and type of tool had also a very
significant effect on the surface roughness. It was also concluded that increase in
cutting speed will reduce the tool life significantly and also the change of tool
geometry. The feed rate was the most significant factor that affected the surface
roughness value, and followed by the type of tool. They also presented optimized
cutting conditions.
Federico et al. (2008) observed that feed rate was the most significant parameter
affecting surface roughness, tree replicas showed basically the same pattern, i.e., at
the beginning, the tool wear rate was high up to approximately the first 1000 m and
after that, the growth rate was low.
Noordin (2008) observed that the tool life decreased with the increase in cutting
speed and feed. The longest life time of the tools was achieved at low cutting speed
and low feed where the tool lasted for eighteen minutes. The decrease in feed
improved the surface roughness values. Cutting speed was generally found to be
inversely proportional to the surface roughness achieved.
Mohamed et al. (2009) investigated that the cutting speed improved the surface
quality alternatively; an increase of feed or depth of cut deteriorated surface quality
with feed as a determinative factor.
Gaitonde et al. (2009) predicted that the combination of low feed rate, less
machining time, and high cutting speed is necessary for minimizing the surface
roughness. The maximum tool wear occurs at a cutting speed of 150m/min for all
values of feed rate. For a specified value of cutting speed or feed rate, the tool wear
increases with increase in machining time.
Dawson (2000) revealed that increased cutting speed diminished tool life more
than increased feed rates or radial depths of cut. They further concluded that, a low
54
CBN content tool should be selected to maximize tool life, while increased radial
depths of cut or feed rates should be used to maximize material removal rates
instead of increased cutting speed (within recommended ranges).
Pavel et al., revealed in their investigations that the maximum flank wear land
width (VBmax) was a function of cutting length. The cutting parameters
considered were: depth of cut 0.18 m, cutting speed 125 m/min, and feed 0.15
mm/rev. The main wear mechanism for the PcBN inserts was found to be the
abrasion of the binder material by the hard particles of the workpiece and the loose
CBN grains pulled out during the cutting process. Feed was found to be the most
significant factor of influence followed by the depth of cut, which had a much
lower influence, however. The cutting speed had the lowest significance.
These researchers have demonstrated that the best tool life was obtained in lowest
feed rate and lowest cutting speed combination. They further experienced that an
increase of feed or depth of cut deteriorated surface quality with feed as a
determinative factor. The surface roughness was proportional to feed and inversely
proportional to cutting speed. It was also concluded that considering both the tool
life and the surface roughness, a combination of low cutting parameters is the
optimum solution. The feed rate was the most significant factor that affected the
surface roughness value, and followed by the type of tool. It was also observed that
the tool life decreased with the increase in cutting speed.
55
2.1.6 Literature review on effect of cutting parameters on residual stress and
tool wear:
Table 2.6: Summary of literature review residual stress and tool wear
Author/Year Modeling Tech. Workpiece Material
/ Tool material
Cutting Parameters
Chen, L.
ElWardany, T.
Nasr, M.
Elbestawi, M.A.
(2006)
Lagrangian and Eulerian
(ALE) FEM
AISI H13 / PCBN
Honed edge radii 20±5 mm Chamfered with 20o by 0.1 mm K-Land.
Feed 0.07, 0.17 mm Cutting speed (m/min)150 Depth of cut (mm) 0.5, Approach angle (◦) 5 Top and side rake angles (◦) 5
Uhlmann, E.
Reimers, W.
Byrne, F.
Klaus, M.
(2010)
Not defined Aluminium silicon
alloys / CVD
diamond coated
cemented carbide
Cutting Speeds (m/min) 200,
500
Feed rate (mm/rev) 0.1 Depth
of cut (mm) 0.5
Chen et al., (2006) observed that honed edges could be employed for hard turning
when tensile principal stresses in the tool were maintained at a low magnitude.
Chamfered edges produced less compressive residual stresses on the surface.
However, away from the machined surface, compressive residual stresses penetrate
deeper into the workpiece. The cutting edge temperature increased with higher
feed and chamfer edge. At 0.07 mm feed, the tool wear rate was higher for hone
edge when compared to the chamfer edge even with the latter generating a higher
temperature.
Uhlmann et al. (2010) in their study analyzed the residual stresses of two tungsten
carbide specifications prior to and following the deposition of a nanocrystalline
CVD diamond coating. It was determined that large compressive residual stresses
with significant depth profiles were present in both tungsten carbide tools prior to
the coating process. These compressive stresses were, however, reduced from
approximately 1,400 MPa to 350 MPa in the near-surface area following the
deposition of the diamond coating. The residual stresses of the diamond coatings
were also analyzed and it was found that there is no depth profile present in the
coatings. Interestingly, those coatings deposited on the tungsten carbide substrates
56
with 10% cobalt exhibited tensile stresses, while those on substrates with 6%
cobalt possessed compressive stresses.
It has been observed from the literature review that very little work has been
reported to optimize the cutting parameters for better tool life and residual stresses
distribution combined.
2.2 Gaps in the existing study and problem formulation
To compete globally in manufacturing sectors, it becomes imperative to obtain
optimal cutting as well as geometric parameters to ensure better surface integrity
and lower machining cost. It was addressed in the literature that nearly 70 to 80 %
part are machined before they are put into final use and machining alone contribute
towards 15 to 20% of the total cost of the product.
Detections and minimization of machining variables such as residual stresses, tool
tip wear, surface roughness etc are the burning issues which need to be explored.
Hence it became important to provide suitable cutting parameters for different tool-
work material combinations, to enhance overall productivity of the manufacturing
industries.
The machining characteristics of AISI H11 tool steel have not been reported much
so far. This material could be used for making dies etc.
Most of the literature has revealed that researchers have attempted their study with
single insert of tool material/geometry.
A numbers of theoretical models have also been devised and presented by
researchers to establish a relationship between cutting parameters and machining
variables, mostly by taking one or two machining variables. The machine tool
structure and cutting process dynamics, however, are so complex that these
theoretical models cannot be completely relied upon. There is also a need for
models which could consider number of machining variables at different cutting
parameters so as to provide optimized results. The following figure depicts the
number of papers reviewed on various output parameters and the following
conclusions are important from this review:
57
Figure 2.1 Number of papers on various output parameters
Scanty work has been reported on tool wear, surface roughness and residual
stresses combination, residual stress and tool wear combination. Hence there is an
impressing need to obtain complete understanding and quantify the effect of
cutting parameters on above said responses.
2.3 Objectives of present research
It is now concluded that the assessment, modelling and optimization of machining
variables such as residual stresses, surface roughness and tool wear by varying cutting
parameters like cutting speed, feed, depth of cut and nose radius are the prominent
factors which need to be investigated. The present study is focused on hard turning of
AISI H11 tool steel with Ceramic cutting tools. The study is to investigate the
following objectives:
• To investigate the effect of tool geometry (nose radius) on residual stresses,
surface roughness and tool wear during hard turning of AISI H11with
ceramic tools.
• To investigate the effect of process parameters (Cutting speed, Feed and
Depth of cut) on residual stresses, surface roughness and tool wear during
hard turning of AISI H11with ceramic tools.
• Development of a model for predicting residual stresses, surface roughness
and tool wear.
58
• Optimization and validation of cutting parameters for residual stresses,
surface roughness and tool wear
• The comparison of regression and ANFIS models with actual experimental
values of residual stresses, surface roughness and tool wear. To check the
effectiveness of both the modelling techniques a Chi-Square (χ2) test for
goodness of fit will be conducted.
2.4 Methodology
Keeping in view the proposed objectives, the following methodology have been
adopted to meet the set objectives in hard turning of AISI H11tool steel using ceramic
tools.
• Longitudinal turning of AISI H11 steel rod has been performed on a rigid,
high-precision turning centre by using ceramic inserts for various
combinations of cutting parameters.
• Response Surface Methodology (RSM) and the BOX-Behnken Design of
experiments have been used to find out optimum number of experiments to
be conducted to achieve the said objectives.
• Surface roughness of machined surface has been measured using a surface
analyzer during experimentation.
• Residual stresses have been evaluated through X-ray diffractrometer.
• Tool wear has been evaluated through Tool Makers Microscope.
• Analysis has been carried out using analysis of variance (ANOVA). The
significance of the regression model and significant model term i.e cutting
speed, feed, depth of cut and nose radius are clearly highlighted. Further, 3-
D response surface plots, interaction plots and perturbation plots are also
represented.
• Models have been developed to correlate output variables such as residual
stresses, surface finish and tool wear, with the input variables i.e. cutting
speed, feed, depth of cut and nose radius.
59
2.5 Scope of study
The data obtained from this study will provide a better understanding of the effect
of process parameters and tool geometry on residual stresses, surface finish
developed in the work piece and tool wear during hard turning of Alloy steel. The
results obtained will enrich the existing database and may be helpful in selecting
the optimum values of process parameters during machining of different grades of
tool steel.
Organizations involved in some kind of machining activity on machine tools will
be benefited by this study. Further, information about relationship between cutting
parameters and machining variables if obtained on-line or off-line could be used to
establish economic optimization of machining operations. The study will help the
users of AISI H11 material to use the tailor made optimized cutting parameters to
improve the quality of their product and enhance the surface integrity and more
over to reduce the time and cost of production.