chapter – 2 literature review and problem...

33

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


Cutting speed (m/min) 100

Nose radius (mm) 0.8

Turning with wiper tools

Feed (mm/rev) 0.1–0.8



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


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

35

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


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


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


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

36

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


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.

37

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

38

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.

39

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


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


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

40

Liu, M.

Takagi, J.

Tsukuda, A.

(2004)

Not defined JIS SUJ2/CBN


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


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



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


Rake angle (o) 6, 4.29


Feed (mm/rev) 0.15, 0.2, 0.05


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

41

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.

42

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

43

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.

44

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.

45

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


Rake Angle (◦) 12

Relief Angle (◦) 5

Edge Radius (mm) 0.01, 0.05, 0.1

For the chamfer tool

Cutting velocity(m/min) 130


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


Feed (mm/rev) 0.05, 0.10,0.15


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.


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


Feed rate (mm/rev) 0.05, 0.10, 0.15

Cutting depth (mm) 0.05, 0.10, 0.15


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.


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



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


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


Feed rate (mm/rev) 0.05, 0.10, 0.15


Thamizhmanii, S.

Kamarudin, K.

Rahim, E.A.

Saparudin, A.

Hasan, S.

(2007)

Taguchi Design,

ANOVA

SCM 440 high strength

alloy steel / CBN


Depth of cut (mm) 0.20, 0.30, 0.40


Grzesik, W.

(2008)

Not defined DIN 41Cr4, AISI 5140 /

Ceramic

Conventional Turning SNGN 120408

T01020


Feed rate (mm/rev) 0.04–0.4 Depth

of cut (mm) 0.25


Turning With Wiper Tools CNGA

120408 T01020 WG

Feed Rates (mm/rev) 0.1–0.8



Noordin, M.Y. Zainal, A.M. Hendriko, D.K. (2008)

Regression. AISI D2/ coated ceramic


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




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


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


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


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



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



Pavel, R.

Sinram, K.

Combs, D.

Deis, M.

Marinescu, I.

(2011)

ANOVA 1137 Steel Shafts/ PCBN Nose radius (mm) 0.8


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.

chapter – 2 literature review and problem...

Documents