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Optimization of surface roughness, cutting force and tool wear

of nitrogen alloyed duplex stainless steel in a dry turning

process using Taguchi method

D. Philip Selvaraj a,, P. Chandramohan b, M. Mohanraj c

a School of Mechanical Sciences, Karunya University, Coimbatore 641114, Indiab Department of Mechanical Engineering, Professional Group of Institutions, Palladam 641662, Indiac Department of Mechanical Engineering, Hindusthan College of Engineering and Technology, Coimbatore 641032, India

a r t i c l e i n f o

Article history:

Received 17 January 2013

Received in revised form 24 May 2013

Accepted 25 November 2013

Available online 5 December 2013

Keywords:

Duplex stainless steel

Dry turning

Taguchi method

S/Nratio

ANOVA

Optimization

a b s t r a c t

In this work, the dry turning parameters of two different grades of nitrogen alloyed duplex

stainless steel are optimized by using Taguchi method. The turning operations were carried

out with TiC and TiCN coated carbide cutting tool inserts. The experiments were conducted

at three different cutting speeds (80, 100 and 120 m/min) with three different feed rates

(0.04, 0.08 and 0.12 mm/rev) and a constant depth of cut (0.5 mm). The cutting parameters

are optimized using signal to noise ratio and the analysis of variance. The effects of cutting

speed and feed rate on surface roughness, cutting force and tool wear were analyzed. The

results revealed that the feed rate is the more significant parameter influencing the surface

roughness and cutting force. The cutting speed was identified as the more significant

parameter influencing the tool wear. Tool wear was analyzed using scanning electron

microscope image. The confirmation tests are carried out at optimum cutting conditions.The results at optimum cutting condition are predicted using estimated signal to noise

ratio equation. The predicted results are found to be closer to experimental results within

8% deviations.

2013 Elsevier Ltd. All rights reserved.

1. Introduction

In the past few decades, the applications of stainless

steel materials have been increased enormously in various

engineering fields. The combination of good corrosion

resistance, wide range of strength levels, good formabilityand aesthetically pleasing appearance have made stainless

steels as a good choice for wide range of applications. But,

their machinability is more difficult compared to other al-

loy steels due to low thermal conductivity, high built-up

edge (BUE) formation tendency and high deformation

hardening. Duplex stainless steel (DSS) combines the ben-

efits of both ferritic stainless steel (FSS) and austenitic

stainless steel (ASS) by proper balancing of ferrite and aus-

tenite. The duplex structure improves stress-corrosion

cracking resistance, compared to ASSs, and improves the

toughness and ductility compared to FSSs [1]. Modern

DSS grades tend to be difficult to machine, by virtue of

their higher austenite and nitrogen contents. The use ofDSSs has been increased due to their high strength, higher

pitting corrosion resistance equivalent and stress corrosion

resistance [2]. DSSs are extensively being used in many

industrial sectors like desalination, chemical tankers, pres-

sure vessels, storage tanks, machinery in the pulp and pa-

per industry, and also in civil engineering applications.

They have higher contents of chromium and lower con-

tents of nickel and molybdenum and they are excellent

engineering materials[3].

Agrawal et al. [4] have been studied the machining

characteristics of cast ASSs with reference to cutting force

0263-2241/$ - see front matter 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.measurement.2013.11.037

Corresponding author. Tel.: +91 9994650780; fax: +91 4222615615.

E-mail addresses: [email protected], philipselvaraj@karunya.

edu(D. P. Selvaraj).

Measurement 49 (2014) 205215

Contents lists available at ScienceDirect

Measurement

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requirement, tool rake-face wear and chip characteristics.

It has been reported that the composition of stainless steel

work piece material influences the machinability. The wear

mechanisms when turning X5CrMnN18 ASS materials

were catastrophic failure at tool nose due to high cutting

forces and sharp edge chipping. The addition of nitrogen

to ASS increases the strength and decreases the machin-

ability [5]. In powder metallurgy produced DSSs, the

machining difficulties are increased due to the presence

of more hard oxide particles, high strength and work hard-

ening rate[6]. The surface roughness values were found to

decrease with increasing cutting speed when turning AISI

304 ASS. This can be attributed to the presence of BUE at

lower cutting speeds. The poor performance of the tool

was due to higher influence of the heat on the cutting tool

and less efficient heat dissipation at the lower cutting

speeds[7]. Similarly, Ciftci[8]investigated the machining

characteristics of ASS using chemical vapor deposition

coated carbide cutting tools. His results reported that cut-

ting speed is highly influencing the surface roughness val-

ues. In another work, Senthikumar et al.[9]evaluated the

tool life of alumina based ceramic cutting tool for machin-

ing hardened martensitic stainless steel (MSS). It has been

reported that the flank wear affects the tool life at lower

cutting speed, whereas, crater wear or notch wear affects

the tool life at higher cutting speed. Noordin et al. [10]

recommended higher insert radius, low feed rate and low

depth of cut to obtain better surface finish under dry turn-

ing operation. The flank wear rate of Cubic Boron Nitride

(CBN) tool was more compared to Polycrystalline Cubic

Boron Nitride (PCBN) tool while machining MSS due to

more abrasion and diffusion [11]. Krolczyk et al. [12]

developed a mathematical model using response surface

method (RSM) to predict the surface roughness of DSS in

dry turning. The cutting parameters considered were cut-

ting speed, feed rate and depth of cut. They found that

the feed rate is the main influencing factor on the surface

roughness. Bouzid Sai et al. [13]investigated the residual

stresses, microstructure, surface roughness and micro

hardness of carbon steels and DSS materials during milling

operations. They found that a high value cutting speed

with less feed rate has improved the quality of the machin-

ing surface. Depth of cut has less influence on the surface

characteristics.

Muthukrishnan and Davim[14]optimized the machin-

ing parameters of Al/SiC metal matrix composites using

ANOVA and ANN analysis and reported that feed rate has

high physical influence on the surface roughness. Similarly,

Palanikumar [15] used Taguchi method to optimize the

drilling parameters of glass fiber-reinforced plastics com-

posites. It has been reported that feed rate was the more

influential parameter than spindle speed. In another work,

Mandal et al.[16]applied Taguchi method and regression

analysis to assess the machinability of AISI 4340 steel with

newly developed Zirconia Toughened Alumina ceramic in-

serts. Their results reported that the main contributing fac-

tors for the tool flank wear are depth of cut and the cutting

speed. The feed rate has less influence on the flank wear.

Similarly, Asiltrk and Akkus [17] conducted dry turning

tests on hardened AISI 4140 steel (51 HRC) with coated

carbide cutting tools. They used Taguchi method to opti-

mize the cutting parameters. Their results reported that

the feed rate has the more significant effect on surface

roughness (Ra- roughness average and Rz-average maxi-

mum height of the profile). The cited literatures confirmed

that limited investigations have been carried out on the

machining characteristics of nitrogen alloyed DSS. Hence,

an attempt has been made in this work to optimize the cut-

ting parameters to minimize the surface roughness, cutting

force and tool wear during dry turning operations of nitro-

gen alloyed DSS.

2. Taguchi method

Taguchi method is widely used for optimizing indus-

trial/production processes. The Taguchi design optimiza-

tion method can be divided into three stages: (a) system

design, (b) parameter design and (c) tolerance design.

Among the three stages, the parameter design stage is con-

sidered to be the important stage [1820]. The steps fol-

lowed in the Taguchi parameter design are: selecting the

proper orthogonal array (OA); running experiments basedon the OA; analyzing data; identifying the optimum condi-

tion; and conducting confirmation runs [21]. Many

researchers have been used Taguchi method to optimize

the various machining operations like turning, end milling,

drilling, etc. in various alloys[2228].

3. Experimental details and data analysis

The experiments are designed using Taguchis design of

experiment method. This research work was carried out at

Centre for Research in Design and Manufacturing engineer-

ing (CRDM), Karunya University, Coimbatore, India. The

experimental data are analyzed by using the signal to noiseratio (S/Nratio) and the analysis of variance (ANOVA). The

S/Nratio analysis is used to find out the optimum machin-

ing conditions. The ANOVA analysis is used to find the per-

centage contribution of the cutting speed and feed rate on

surface roughness, cutting force and tool wear.

3.1. Work piece material

The work piece materials selected for investigation are

the cast DSS ASTM A 995 grade 5A and grade 4A with the

compositions as shown inTable 1. The mechanical proper-

ties of the material investigated are given in Table 2. The

diameter and length of the work piece used in the experi-

mentation are 80 mm and 300 mm, respectively. One end

of the work piece is held in a chuck and other end is sup-

ported with a tailstock. Generally the length-to-diameter

ratio used is 3:16:1, while machining with the tail stock

to prevent deflection. The length-to-diameter ratio of the

specimen used in this work is 3.75:1, which falls within

the acceptable range.

3.2. Experimental procedure

The turning tests are conducted on a medium duty Kir-

loskar Turn master-35 Lathe with a variable speed be-

tween 100 and 1500 rpm and a power rating of 2.2 kW.

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The cutting tools used are carbide inserts (Taegu Tec make)

coated with TiC and TiCN with a specification of SNMG

120408 MT TT5100. The inserts are clamped on a pin and

hole type tool holder (Taegu Tec make) with a specification

of PSBNR 2525M12. The surface roughness is measured

using a TR-100 surface roughness tester. Cutting force ismeasured using Kistler piezoelectric dynamometer (model

9257B). Cutting force measurements are interfaced with a

computer using data acquisition system. The tool wear is

observed using scanning electron microscope (SEM) JEOL

JSM-6390 model. The schematic diagram of the experi-

mental set-up is depicted inFig. 1. Dry machining is more

popular in manufacturing as a means of reducing overhead

costs and protecting the environment[29]. It has great sig-

nificance for the factors of both economics and environ-

ment [30]. Hence, the experiments were carried out

under dry condition (without using cutting fluid).

3.3. Experimental plan

The cutting parameters for turning operation are cut-

ting speed, feed rate and depth of cut. The influence of cut-

ting speed and feed rate are more significant compared to

depth of cut[12,13,27]. Hence, cutting speed and feed rate

are selected as the main cutting parameters in this study.

Depth of cut was maintained at constant value in the turn-

ing operation [31] and in the milling operation[32]. For

turning DSS material using carbide cutting tool, the cutting

speed range is 80120 m/min as per the standard pub-

lished by International Molybdenum Association (IMOA

1999). The feed rate range is selected based on the avail-ability of the range of feed in the machine utilized for

experimentation. The depth of cut is taken constant as

0.5 mm based on the investigation reported by Thakur

et al. [31]. The experiments are planned using the Tagu-

chis OA. The machining tests were conducted according

to a 3-level and 2-factor L9OA. The experiments were con-

ducted at three different cutting speeds (80, 100 and

120 m/min) with three different feed rates (0.04, 0.08

and 0.12 mm/rev) and a constant depth of cut (0.5 mm).

The cutting parameters and their levels are indicated in

Table 3. The experimental layout for the L9 OA is shown

inTable 4.

3.4. Analysis of the S/N ratio

TheS/N ratio is the ratio of the mean to the standard

deviation. It is used to measure the quality characteristic

deviating from the desired value. TheS/Nratio (g) is given

by the following equation discussed by Yang and Tarng

[21].

g 10logM:S:D 1

Here, M.S.D is the mean square deviation for the output

characteristic. To obtain optimal cutting performance,

the-lower-the-better quality characteristic for surface

roughness, cutting force and tool wear must be taken.

The M.S.D. for the-lower-the-better quality characteristic

of surface roughness, cutting force and tool wear can be gi-

ven by the following equations discussed by Yang and

Tarng[21].

M:S:D 1

m

Xm

i1

S2i

2

M:S:D 1

m

Xm

i1

F2i

3

M:S:D 1m

Xm

i1

T2i

4

Here,m is the number of tests, Si,FiandTiare the values of

the surface roughness, cutting force and tool wear, respec-

tively for theith test. The estimatedS/Nratio (g) has been

used to predict and verify the quality characteristic at the

optimal level. The estimated S/Nratio g at the optimal level

Table 1

Chemical composition of ASTM A 995 grade 5A and 4A DSS (wt%).

Alloy C Si Mn S P Cr Ni Mo Cu N Fe

5A 0.028 0.67 0.87 0.005 0.028 25.10 6.63 4.16 0.17 Bal.

4A 0.028 0.65 0.71 0.006 0.027 22.16 5.66 3.33 0.14 0.24 Bal.

Table 2Mechanical properties of ASTM A 995 grade 5A and 4A DSS.

Alloy Tensile

strength (MPa)

Yield strength

(MPa)

Elongation

(%)

Hardness

(BHN)

5A 741 546 32.2 223

4A 732 595 30.2 212

Tool

Work Piece TailstockChuck

KistlerDynamometer

Chargeamplifier

Computer

Fig. 1. Schematic diagram of experimental set-up.

Table 3

Cutting parameters and their levels.

Symbol Cutting parameters Level 1 Level 2 Level 3

V Cutting speed (m/min) 80 100 120

F Feed rate (mm/rev) 0.04 0.08 0.12

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of the design parameters can be calculated by the follow-

ing equation discussed by Yang and Tarng[21].

g gm

Xo

i1

gi

gm

5

Here, gm is the total mean S/Nratio, gi is the meanS/N

ratio at the optimal level, and o is the number of the main

design parameters that affect the quality characteristic.

3.5. Analysis of the variance

ANOVA is carried out to identify the design parameters

that significantly affect the response. The total sum of the

squared deviations (SST) is calculated by using the follow-

ing equation discussed by Yang and Tarng[21].

SSTXn

i1gi gm

2

6

Here, n is the number of experiments, gi is the mean S/N

ratio for the ith experiment and gm is the total mean S/N

ratio. The two sources of the SST are: the sum of the

squared deviations (SSd) due to each design parameter

and the sum of the squared error (SSe).

4. Results and discussion

The experimental results of surface roughness, cutting

force and tool wear with their corresponding S/N ratio

are shown inTables 57, respectively for 5A and 4A Grade

DSS. The meanS/Nratio for cutting speed at level 1 is cal-

culated by averaging theS/Nratios for the experiments 1

3. The mean S/Nratio for feed rate at level 1 is calculated

by averaging the S/N ratios for the experiments 1, 4 and

7. Similarly, the mean S/Nratio for cutting speed and feed

rate at levels 2 and 3 are calculated.

4.1. S/N ratio and ANOVA results

The S/Nresponse table for surface roughness, cutting

force and tool wear of 5A and 4A grade DSS are shown in

Tables 8 and 9, respectively. The S/Nresponse graph for

surface roughness of 5A and 4A grade DSS is depicted in

Fig. 2. In the S/N response graph, V1 stands for cutting

speed at level 1 (80 m/min), V2 stands for cutting speed

at level 2 (100 m/min),V3 stands for cutting speed at level

3 (120 m/min),F1stands for feed rate at level 1 (0.04 mm/

rev),F2stands for feed rate at level 2 (0.08 mm/rev) andF3stands for feed rate at level 3 (0.12 mm/rev). The greater

S/Nratio corresponds to the smaller variance of the output

characteristic around the desired value. From Fig. 2, the

higherS/Nratio for surface roughness of 5A and 4A grade

are obtained at cutting speed level 2 and feed rate level

Table 4

Experimental layout using an L9 orthogonal

array.

Experimental

number

Cutting parameter

level

Cutting

speed (A)

Feed

rate (B)

1 1 1

2 1 2

3 1 3

4 2 1

5 2 2

6 2 3

7 3 1

8 3 2

9 3 3

Table 5

Experimental results for surface roughness and S/Nratio of 5A and 4A grade

DSS.

S.

no.

Cutting

Speed

(m/min)

Feed rate

(mm/rev)

Surface

roughness Ra

(lm)

S/Nratio (dB)

5A

Grade

4A

Grade

5A

Grade

4A

Grade

1 80 0.04 1.20 0.60 1.58 4.44

2 80 0.08 1.25 0.68 1.94 3.35

3 80 0.12 1.32 0.85 2.41 1.41

4 100 0.04 1.05 0.53 0.42 5.51

5 100 0.08 1.17 0.57 1.36 4.88

6 100 0.12 1.25 0.64 1.94 3.88

7 120 0.04 1.16 0.58 1.29 4.73

8 120 0.08 1.24 0.60 1.87 4.44

9 120 0.12 1.30 0.76 2.28 2.38

Table 6

Experimental results for cutting force and S/Nratio of 5A and 4A grade DSS.

S.no. Cuttingspeed

(m/min)

Feed rate(mm/rev) Cutting forceFc (N) S/Nratio (dB)

5A

Grade

4A

Grade

5A

Grade

4A

Grade

1 80 0.04 44.5 33.2 32.97 30.42

2 80 0.08 53.6 39.3 34.58 31.89

3 80 0.12 58.9 45.8 35.40 33.22

4 100 0.04 36.9 28.2 31.34 29.00

5 100 0.08 44.1 34.4 32.89 30.73

6 100 0.12 50.9 38.1 34.13 31.62

7 120 0.04 35.2 25.7 30.93 28.20

8 120 0.08 38.7 28.8 31.75 29.19

9 120 0.12 46.5 35.6 33.35 31.03

Table 7

Experimental results for tool wear and S/Nratio of 5A and 4A grade DSS.

S.

no.

Cutting

speed

(m/min)

Feed rate

(mm/rev)

Tool wear Vb

(mm)

S/Nratio (dB)

5A

Grade

4A

Grade

5A

Grade

4A

Grade

1 80 0.04 0.113 0.102 18.94 19.83

2 80 0.08 0.122 0.115 18.27 18.79

3 80 0.12 0.134 0.130 17.46 17.72

4 100 0.04 0.146 0.141 16.71 17.02

5 100 0.08 0.184 0.176 14.70 15.09

6 100 0.12 0.204 0.201 13.81 13.94

7 120 0.04 0.268 0.252 11.44 11.97

8 120 0.08 0.329 0.319 9.66 9.92

9 120 0.12 0.353 0.348 9.04 9.17

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1. Therefore, the optimal cutting parameters for surface

roughness of 5A and 4A grade DSS are the cutting speed

at level 2 (100 m/min) and the feed rate at level 1

(0.04 mm/rev).

TheS/Nresponse graph for cutting force of 5A and 4A

grade DSS is shown in Fig. 3. FromFig. 3, the higher S/N

ratio for cutting force of 5A and 4A grade are obtained at

cutting speed level 3 and feed rate level 1. Therefore, the

optimal cutting parameters for cutting force of 5A and 4A

grade DSS are the cutting speed at level 3 (120 m/min)

and the feed rate at level 1 (0.04 mm/rev).

The S/Nresponse graph for tool wear of 5A and 4A grade

DSS is shown inFig. 4. FromFig. 4, the higher S/Nratio for

tool wear of 5A grade are obtained at cutting speed level 1

and feed rate level 1. Hence the optimal cutting parameters

for tool wear of 5A and 4A grade DSS are the cutting speed

at level 1 (80 m/min) and the feed rate at level 1 (0.04 mm/

rev).

Similarly, Table 10 shows that the ANOVA results of

surface roughness, cutting force and tool wear of 5A grade

DSS. It was observed that the feed rate is the more signifi-

cant cutting parameter affecting the surface roughness.

The cutting parameters influencing the surface roughness

are feed rate followed by cutting speed. ANOVA results

showed that feed rate and cutting speed are affecting the

surface roughness of 5A grade DSS by approximately 64%,

and 31%, respectively. It was observed that the cutting

speed and the feed rate are the significant cutting parame-

ters affecting the cutting force. The feed rate and cutting

speed affecting the cutting force of 5A grade DSS by

approximately 53% and 45%, respectively. It was observed

that the cutting speed is the more significant and the feed

rate is the less significant cutting parameter affecting the

tool wear. The cutting speed and the feed rate affect the

tool wear of 5A grade DSS by approximately 92%, and 7%,

respectively.

Similarly, Table 11 shows that the ANOVA results of

surface roughness, cutting force and tool wear of 4A grade

Table 8

S/Nresponse table for surface roughness, cutting force and tool wear for 5A

grade DSS.

Cutting parameter MeanS/Nratio (dB) Maxmin

Level 1 Level 2 Level 3

Surface roughness

Cutting speed 1.98 1.24 1.81 0.74

Feed rate 1.10 1.72 2.21 1.11

Cutting force

Cutting speed 34.32 32.79 32.01 2.31

Feed rate 31.75 33.07 34.29 2.54

Tool wear

Cutting speed 18.22 15.07 10.05 8.17

Feed rate 15.77 14.21 13.44 2.33

Table 9

S/Nresponse table for surface roughness, cutting force and tool wear for 4A

grade DSS.

Cutting parameter MeanS/Nratio (dB) Maxmin

Level 1 Level 2 Level 3

Surface roughness

Cutting speed 3.07 4.76 3.85 1.69

Feed rate 4.89 4.22 2.56 2.33

Cutting force

Cutting speed 31.84 30.45 29.47 2.37

Feed rate 29.21 30.60 31.96 2.75

Tool wear

Cutting speed 18.78 15.35 10.35 8.43

Feed rate 16.27 14.60 13.61 2.66

Fig. 2. S/Ngraph for surface roughness-5A and 4A grade DSS.

Fig. 3. S/Ngraph for cutting force-5A and 4A grade DSS.

Fig. 4. S/Ngraph for tool wear 5A and 4A grade DSS.

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DSS. The feed rate and cutting speed affecting the surface

roughness of 4A grade DSS by about 63% and 31%, respec-

tively. The feed rate and cutting speed affect the cutting

force of 4A grade DSS by about 56% and 42%, respectively.

The cutting speed and the feed rate affect the tool wear of

4A grade DSS by about 91% and 9%, respectively.

4.2. Comparison of predicted and experimental results at the

optimal cutting conditions

The results at optimum cutting condition are predicted

using estimated signal to noise ratio equation discussed by

Yang and Tarng[21]. The predicted and experimental sur-

face roughness, cutting force and tool wear of 4A and 5A

grade DSS using the optimal cutting parameters are com-

pared inTables 12 and 13, respectively. The optimal cut-

ting parameters for surface roughness of 5A and 4A grade

DSS are the cutting speed at level 2 (100 m/min) and the

feed rate at level 1 (0.04 mm/rev). It is denoted as V2F1 in

theTables 12 and 13. The optimal cutting parameters for

cutting force of 5A and 4A grade DSS are the cutting speed

at level 3 (120 m/min) and the feed rate at level 1

(0.04 mm/rev). It is denoted as V3F1. The optimal cutting

parameters for tool wear of 5A and 4A grade DSS are the

cutting speed at level 1 (80 m/min) and the feed rate at le-

vel 1 (0.04 mm/rev). It is denoted as V1F1. The experimen-

tal results are closer to the predicted values within 8%

deviations.

4.3. Effect of cutting speed and feed rate on surface roughness

The influence of cutting speed on surface roughness of

5A and 4A grade DSS is illustrated in Fig. 5, for three differ-

ent feed rates. When cutting speed increases the surface

roughness value decreases up to 100 m/min. However, fur-

ther increase in cutting speed increases the surface rough-

ness values. The increasing cutting speed from 80 to

100 m/min reduces the surface roughness value due to

the reduction in BUE formation tendency. However, further

increase in cutting speed from 100 to 120 m/min, increases

the surface roughness due to the increase in cutting tool

nose wear at higher cutting speeds. Similar trend was re-

ported in turning operation of ASS[8].

The influence of feed rate on surface roughness of 5A

and 4A grade DSS is shown in Fig. 6, for three different cut-

ting speeds. The increasing feed rate increases the surface

Table 10

Results of the ANOVA for surface roughness, cutting force and tool wear for 5A grade DSS.

Cutting parameter Degrees of freedom Sum of squares Mean square Fratio Contribution (%)

Surface roughness

Cutting speed 2 0.9015 0.4507 11.40 30.91

Feed rate 2 1.8567 0.9283 23.49 63.67

Error 4 0.1581 0.0395 5.42

Total 8 2.9163 100

Cutting force

Cutting speed 2 8.2854 4.1427 56.92 45.38

Feed rate 2 9.6825 4.8412 66.52 53.03

Error 4 0.2911 0.0727 1.59

Total 8 18.2590 100

Tool wear

Cutting speed 2 101.8719 50.93595 233.62 92.05

Feed rate 2 7.9206 3.96030 18.1644 7.16

Error 4 0.8721 0.218025 0.79

Total 8 110.6646 100

Table 11

Results of the ANOVA for surface roughness, cutting force and tool wear for 4A grade DSS.

Cutting parameter Degrees of freedom Sum of squares Mean square Fratio Contribution (%)

Surface roughness

Cutting speed 2 4.2927 2.14635 12.07 31.48

Feed rate 2 8.6334 4.3167 24.28 63.31

Error 4 0.7112 0.1778 5.21

Total 8 13.6373 100

Cutting force

Cutting speed 2 8.50954 4.25475 66.38 42.31

Feed rate 2 11.3442 5.67210 88.49 56.41

Error 4 0.2564 0.06410 1.28

Total 8 20.1101 100

Tool wear

Cutting speed 2 107.8299 53.91495 637.67 90.60

Feed rate 2 10.8447 5.42235 64.13 9.11

Error 4 0.3382 0.08455 0.29

Total 8 119.0128 100

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roughness due to the increase in friction between work

piece and tool interface and increases the temperature in

the cutting zone. Hence, the shear strength of the material

reduces and behaves in a ductile fashion [31]. The sticky in

nature of DSS is the cause for the increased surface rough-

ness. The surface roughness can be minimized by employ-

ing the combination of lower level feed rate (0.04 mm/rev)

with medium level cutting speed (100 m/min).

4.4. Effect of cutting speed and feed rate on cutting force

The influence of cutting speed on cutting force of 5A

and 4A grade DSS is shown in Fig. 7, for three different feedrates. The cutting force decreases with increasing cutting

speed. Higher cutting force is required at lower cutting

speed due to the higher coefficient of friction between

the tool and work piece, results. At higher cutting speeds,

the temperature generation rate is higher which makes

the material soft at the cutting zone, which helps in remov-

ing the material at lower cutting forces. As the cutting

speed increases, the chip gets thinner and cutting forces re-

duced. The decrease in cutting force is due to reduction in

contact area and partly due to the drop in shear strength in

the flow zone as the temperature increases with increase

in cutting speed[31].

The influence of feed rate on cutting force of 5A and 4Agrade DSS is shown in Fig. 8, for three different cutting

speeds. The cutting force is increased with increasing feed

rate at all the selected cutting speeds. As the feed rate is in-

creased, the amount of material in contact with the tool

also increases. This implies an increased tool-work contact

Table 12

Comparison between predicted and experimental results for surface

roughness, cutting force and tool wear of 5A grade DSS at optimum cutting

condition.

Optimal cutting parameters

Prediction Experiment

Surface roughness

Level V2F1 V2F1Surface roughness (lm) 1.08 1.05

S/Nratio (dB) 0.66 0.42

Cutting force

Level V3F1 V3F1Cutting force (N) 34.36 35.2

S/Nratio (dB) 30.72 30.93

Tool wear

Level V1F1 V1F1Tool wear (mm) 0.105 0.113

S/Nratio (dB) 19.54 18.94

Table 13

Comparison between predicted and experimental results for surface

roughness, cutting force and tool wear of 4A grade DSS at optimum cuttingcondition.

Optimal cutting parameters

Prediction Experiment

Surface roughness

Level V2F1 V2F1Surface roughness (lm) 0.52 0.53

S/Nratio (dB) 5.76 5.51

Cutting force

Level V3F1 V3F1Cutting force (N) 25.38 25.7

S/Nratio (dB) 28.09 28.20

Tool wear

Level V1F1 V1F1Tool wear (mm) 0.098 0.102

S/Nratio (dB) 20.22 19.83

Fig. 5. Cutting speed vs. surface roughness-5A and 4A grade DSS.

Fig. 6. Feed rate vs. surface roughness-5A and 4A grade DSS.

Fig. 7. Cutting speed vs. cutting force-5A and 4A grade DSS.


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length. Due to this, the value of cutting force also increases.

In addition to increased contact length, the force resisting

deflection is high, which is due to the higher amount of

material in contact with the tool. This also contributes to

an increase in cutting forces. The minimum cutting force

can be obtained by employing the combination of lower le-

vel feed rate (0.04 mm/rev) with higher level cutting speed

(120 m/min).

4.5. Effect of cutting speed and feed rate on tool wear

The influence of cutting speed on tool wear of 5A and

4A grade DSS is shown in Fig. 9, for three different feed

rates. The tool wear increases with the increase in cutting

speed. Increase in cutting speed will increase the cutting

temperature at the cutting edge of the tool. The higher cut-

ting temperature causes the tool to lose its strength and

plastic deformation occurs. Therefore, the extent of tool

wear and cutting edge deformation increases. The tool

wear was significantly increased with increase in cutting

speed.The influences of feed rate on tool wear of 5A and 4A

grade DSS is depicted inFig. 10, for three different cutting

speeds. The tool wear increases with increase in feed rate.

The larger the feed, the greater is the cutting force per unit

area of chip-tool contact on the rake face and work-tool

contact on the flank face. However, it has been observed

that the effect of changes in feed rate on tool wear is rela-

tively lesser than that of proportionate changes in cutting

speed. The minimum tool wear was obtained by employing

the combination of lower feed rate (0.04 mm/rev) with

lower cutting speed (80 m/min).

4.6. Tool wear analysis

The worn surfaces of the cutting tool inserts used in the

machining processes of the 5A and 4A grade DSS work

piece material are examined using SEM. Figs. 11 and 12

illustrate the SEM images of the worn tool inserts used

for machining 5A and 4A grade DSS, respectively. It is ob-

served that wear predominantly occurred in three regions

during the tests, at the depth of cut line, rake surface and

the cutting edge. The SEM images inFig. 11ac are exam-

ined at three different cutting conditions. The lowest wear

was observed on the edge of the cutting tool used at

80 m/min cutting speed and 0.04 mm/rev feed rate

(Fig. 11a). The tool wear at the cutting edge is 0.113 mm.

The tool wear is extended on the cutting tool used at

100 m/min cutting speed and 0.08 mm/rev feed rate

(Fig. 11b). The tool wear at the cutting edge is 0.184 mm.

More wear was observed on the rake surface of the tool.

The highest wear was observed on the cutting tool used

at 120 m/min and 0.12 mm/rev feed rate (Fig. 11c).

Similarly, the less tool wear was observed on the edge

of the cutting tool used at 80 m/min cutting speed and

0.04 mm/rev feed rate (Fig. 12a). The tool wear at the cut-

ting edge is 0.102 mm. The tool wear is extended on the

cutting tool used at 100 m/min cutting speed and

0.08 mm/rev feed rate (Fig. 12b). The tool wear at the cut-

ting edge was 0.176 mm. More wear was observed on the

rake surface of the tool. The highest wear was observed

on the cutting tool used at 120 m/min and 0.12 mm/rev

feed rate (Fig. 12c). More wear was observed on the flank

and rake surface of the tool. The tool wear at the cutting

edge was 0.348 mm.

Tool wear is generally influenced by abrasion, diffu-

sion, thermal softening and notching. It was observed

from Figs. 11a and12a, there is less wear on the cutting

edge flank face and rake surface at a cutting speed of

80 m/min, a feed rate of 0.04 mm /rev and a depth ofFig. 8. Feed rate vs. cutting force-5A and 4A grade DSS.

Fig. 9. Cutting speed vs. tool wear-5A and 4A grade DSS.

Fig. 10. Feed rate vs. tool wear-5A and 4A grade DSS.

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cut of 0.5 mm. It was due to the abrasive action of micro

hard particle present in the material and plastic defor-

mation. At this cutting speed, the cutting tool tempera-

ture was lower compared to the cutting speeds of

100 m/min and 120 m/min. Hence, abrasion is the major

factor influencing the tool wear at lower cutting speed. It

was also observed from Figs. 11b and12b, the wear rate

increases and there were more wear on the cutting edge,

flank face and rake surface at a cutting speed of 100 m/

min, a feed rate of 0.08 mm/rev and a depth of cut of

0.5 mm. Micro chipping was observed on the flank face

and rake surface. It was observed from Figs. 11c and

12c, the wear rate further increased, at a cutting speed

of 120 m/min, a feed rate of 0.12 mm/rev and a depth

of cut of 0.5 mm. Micro chipping and notch wear

were observed on the rake surface and flank face. At this

cutting speed, the cutting tool temperature was

higher compared to cutting speeds of 80 m/min and

100 m/min. The major factors such as diffusion, thermal

softening and notching influencing the tool wear at

120 m/min cutting speed. Hence, tool wear is mainly

due to abrasion at lower cutting speed and due to diffu-

sion, thermal softening and notching at higher cutting

speed. The results confirmed that the flank wear, rake

wear and notch wear were quite severe at higher cutting

speed [9].

Fig. 11. SEM images of tool inserts used for machining 5A grade DSS

(d= 0.5 mm), (a) V= 80m/min and f= 0.04 mm/rev, (b) V= 100 m/min

andf= 0.08mm/rev and (c)V= 120 m/min andf= 0.12 mm/rev. Fig. 12. SEM images of tool inserts used for machining 4A grade DSS

(d= 0.5 mm), (a) V= 80m/min and f= 0.04 mm/rev, (b) V= 100 m/min

andf= 0.08 mm/rev and (c)V= 120 m/min andf= 0.12 mm/rev.


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4.7. Effect of work piece material on surface roughness,

cutting force and tool wear

The surface roughness, cutting force and tool wear val-

ues for 4A grade DSS are lower compared to 5A grade DSS

due to the difference in chemical compositions, which

leads to the difference in the formation of micro grains

[4]. The 4A grade DSS material has lower strength and low-

er hardness compared to 5A grade DSS. The lower percent-

age of molybdenum presents in 4A grade DSS decreases the

strength and hardness[8]. Lower cutting force is required

for shearing 4A grade DSS leads to good surface finish

and less tool wear compared to 5A grade DSS.

4.8. Comparison between dry and wet turning operation

The comparison of surface roughness and cutting force

values of machined work piece during dry and wet turning

of 4A and 5A grade DSSs are depicted inFigs. 13 and 14,

respectively. The result reveals that the surface roughness

and the cutting force values of wet turning operation arereduced by about 510% compared to the dry turning oper-

ation. The application of cutting fluid reduces chip friction

during wet turning. Lower friction at the tool-chip inter-

face can lead to lower cutting temperature which results

in less tool wear and better surface finish. Moreover, low

friction at the tool-chip interface will reduce the tool-chip

contact length which in turn decreases the cutting force

[33,34].

5. Conclusion

The Taguchi optimization method was successfully

used to identify the optimal cutting parameters of two dif-

ferent grades of nitrogen alloyed DSS during dry turning

operations. The following specific conclusions are made

from this work.

A cutting speed of 100 m/min and a feed rate of

0.04 mm/rev are found to give the lowest surface

roughness for both 5A and 4A grade DSS. A cutting

speed of 120 m/min and a feed rate of 0.04 mm/rev

are found to give the lowest cutting force for both 5A

and 4A grade DSS. A cutting speed of 80 m/min and afeed rate of 0.04 mm/rev are found to give the lowest

tool wear for both 5A and 4A grade DSS.

ANOVA analysis indicates that the feed rate and the cut-

ting speed were affecting the surface roughness of 5A

grade DSS by approximately 64% and 31%, respectively.

The feed rate and the cutting speed were affecting the

cutting force of 5A grade DSS by approximately 53%

and 45%, respectively. The cutting speed and the feed

rate were affecting the tool wear of 5A grade DSS by

approximately 92% and 7%, respectively.

ANOVA analysis indicates that the feed rate and the cut-

ting speed were affecting the surface roughness of 4A

grade DSS by about 63% and 31%, respectively. The feedrate and the cutting speed were affecting the cutting

force of 4A grade DSS by about 56% and 42%, respec-

tively. The cutting speed and the feed rate were affect-

ing the tool wear of 4A grade DSS by about 91% and 9%,

respectively.

The tool wear was due to abrasion at lower cutting

speeds and due to diffusion, thermal softening and

notching at higher cutting speeds.

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