optimization of cutting parameter for turning en-31

May 2016, Volume 3, Issue 5 JETIR (ISSN-2349-5162)

JETIR1605018 Journal of Emerging Technologies and Innovative Research (JETIR) www.jetir.org 86

OPTIMIZATION OF CUTTING PARAMETER

FOR TURNING EN-31 MATERIAL USING RSM

Mahesh kumar sharma1, Sanjay singh

2 , Rakesh kumar

3

1M.Tech Research Scholar,

2,3Assistant Professor

1Department of production engineering,

2Mechanical Engineering Department

1, 2Jagannath University, Jaipur, Rajasthan, India

ABSTRACT: This paper presents the effect of process parameter in turning operation to predict surface roughness. The

turning process by using lathe machine. Applications of the turning process can be found in many industries ranging from

large engine manufactures to small die shops. The parameters that affect the turning operation are vibration, tool wear,

surface roughness etc. Among this surface roughness is an important factor that affects the quality in manufacturing

process. The main objective of this paper is to predict the surface roughness on EN -31 alloy steel by optimizing the input

parameters such as spindle speed, feed rate and depth of cut by using coated carbide tool. A second order mathematical

model is developed using regression technique and optimization is carried out using Box-Behnken of response surface

methodology. The application of response surface methodology for optimizing the input parameters such as spindle speed

(rpm), feed rate (mm/min) and depth of cut (mm), the output parameter surface roughness can also be optimized for

economical production. Therefore, this study attempts the application of response surface methodology to find the optimal

solution of the cutting conditions for giving the minimum value of surface roughness using design- expert 9.0 software.

Keywords Turning process, Surface roughness, Response Surface method, DOE, ANOVA

I. INTRODUCTION

Metal cutting is one of the vital processes and widely used manufacturing processes in engineering industries .Highly competitive

market requires high quality products at minimum cost. Industries in which the cost of raw material is a big percentage of the cost

of finished goods, higher productivity can be achieved through proper selection and use of the materials. . To improve

productivity with good quality of the machined parts is the main challenges of metal industry there has been more concern about

monitoring all aspects of the machining process. Surface finish is an important parameter in manufacturing engineering and it can

influence the performance of mechanical parts and the production costs. The ratio of costs and quality of products in each

production phase has to be monitored and quick corrective actions have to be taken in case of deviation from desired output .

Surface roughness measurement presents an important task in many engineering applications. Many life attributes can be also

determined by how well the surface finish is maintained .Turning is a very important machining process in which a single point

cutting tool removes unwanted material from the surface of a rotating cylindrical work piece. The cutting tool is fed linearly in a

direction parallel to the axis of rotation .Carbide cutting tools are very popular in metal cutting industry for the cutting of various

hard materials such as, alloy steels, die steels, high speed steels, bearing steels, white cast iron and graphite cast iron. In the past

few decades there had been great advancements in the development of these cutting tools. Coating is also used on cutting tools to

provide improved lubrication at the tool/chip and tool/work piece interfaces and to reduce friction, and to reduce the temperatures

at the cutting edge. During machining, coated carbide tools ensure higher wear resistance, lower heat generation and lower cutting

forces, thus enabling them to perform better at higher cutting conditions than their uncoated counterparts. The use of coated tools

are becoming increasingly demanding among the other tool materials. More than 40% of all cutting tools are coated in modern

industry today Surface properties such as roughness are critical to the functional ability of machine components. Increased

understanding of the surface generation mechanisms can be used to optimize machining process and to improve functional ability

of the component . Numerous investigators have been conducted to determine the effect of parameters such as feed rate, tool nose

radius, cutting speed and depth of cut on surface roughness in hard turning operation . The surface roughness decreases with

increasing nose radius. Large nose radius tools have produced better surface finish than small nose radius tool.

II. EXPERIMENTION DETAILS

A.Work piece Material

The EN-31 is a high quality alloy steel giving good ductility and shock resisting properties combined with resistance to wear. This

steel is basically known as bearing steel and used for bearing production in industrial sector.Application of this material with its

properties are used to make axels, gears, camshafts, driving pinion and link components for transportation and energy products as

well as many applications in general mechanical engineering.The chemical composition, mechanical properties and heat treatment

properties properties of the work-piece materials are shown in table 1, table 2 and table 3

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Element

Chemical composition(wt%)

Element

Objective

C 1.08 %

Tensile Strength

750 N/mm²

Si 0.25 %

Yield Stress

450 N/mm²

Mn 0.53 %

Reduction of Area

45%

S 0.015 %

Elongation

30%

P 0.022 %

Modulus of elasticity

215 000 N/mm²

Ni 0.33 %

Density

7.8 Kg/m3 Cr 1.46 %

Hardness

63 HRC Mo 0.06 %

Table 1: Chemical Composition

of EN-31

Table 2: Mechanical Properties of

EN-31

Element Objective

Hardening Temperature 802˚- 860˚ C

Quenching Medium Oil

Tempering Temperature 180˚- 225˚ C

Brinell-Rockwell Hardness 59 - 65

Table 3: Heat Treatment of EN-31

B.Cutting Insert

The tool insert chosen was a coated carbide tool (Kennametal make) whose specifications are shown below. Coated carbide

tools are found to perform better than uncoated.

Table 4: Specification of Cutting Tool

ISO ANSI Grade Dimensions

Catalog Catalog

D L10 S R ε D1

Number Number

mm in mm in mm in mm in mm in

SNMG SNMG KCU25 12.70 0.5 12.70 0.5 4.76 0.1875 5.16 0.203

120408 432MS

The chosen insert was a square type negative insert meaning that it was rotatable and reversible so that a total number of 8

cutting edges can be generated. KCU25 takes advantage of PVD coating technology including special surface treatments that

improve machining performance in high-temperature materials. The coating on the insert is TiAlN (Titanium Aluminium




Nitride).

C. Experiment Apparatus

The hard turning of work piece in dry turning conditions were conducted on lathe (BANKA 34 THUNDERBOLT ,Model: 34)

having following specifications: Power required : 2 HP, Spindle speed: 60-1025 rpm, Spindle nose : loo type,spindle bore 38

mm,Swing over cross slide 190 mm,Swing over bed 350 mm.

D.Design Of Experiment

In this investigation three factors were studied and their low level and high level are given in Table 5. The levels are selected

according to the recommendation of manufacturer. Response surface methodology was used for the planning of experiments

because it was widely used for involving several factors for a response.

Table 5: Factors and Levels of Process Parameters

Factor Level (-1) Level (+1)

Cutting Speed (m/min) 90 202

Feed (mm/rev) 0.06 0.16

Depth of cut (mm) 0.1 0.3

The design layout is produced by the software Design Expert Version 9 (Stat-Ease Inc) is given table 6

Table 6: Experimental layout and desired Response

Run Factor 1

A:speed

m/min

Factor 2

B:feed

mm/rev

Factor 3

C:doc

mm

Response 1

Ra

micro meter

Std

5 1 90 0.11 0.1 0.3

14 2 146 0.11 0.2 1.4

15 3 146 0.11 0.2 1.02

2 4 202 0.06 0.2 1.42

7 5 90 0.11 0.3 0.7

17 6 146 0.11 0.2 1.28

6 7 202 0.11 0.1 0.53

9 8 146 0.06 0.1 0.46

12 9 146 0.16 0.3 0.4

11 10 146 0.06 0.3 0.66

1 11 90 0.06 0.2 0.82

10 12 146 0.16 0.1 0.4

3 13 90 0.16 0.2 0.4

8 14 202 0.11 0.3 0.56

4 15 202 0.16 0.2 0.5

16 16 146 0.11 0.2 1.2

13 17 146 0.11 0.2 1.18

III. RESULT AND DISCUSSION

3.1 ANALYSIS OF RESULTS AND PLOTS

The results obtained from the experiment were fed into design expert v 9.0 for further analysis.

3.1.1 ANOVA

The analysis of variance (ANOVA) (shown in Table 7) was used to study the significance and effect of the cutting parameters

on the response variables i.e. Ra




Table 7: ANOVA for Surface Roughness

ANOVA table (partial sum of squares) for response surface quadratic model (response: surface

roughness, Ra )

Source Sum of squares d.f. Mean square F Prob. > F

Model 2.15 9 0.24 6.42 0.0114 significant

A-speed 0.078 1 0.078 2.09 0.1914

B-feed 0.34 1 0.34 9.23 0.0189

C-doc 0.050 1 0.050 1.33 0.2866

AB 0.063 1 0.063 1.68 0.2366

AC 0.034 1 0.034 0.92 0.3700

BC 1.000E-002 1 1.000E-002 0.27 0.6206

A^2 0.16 1 0.16 4.26 0.0779

B^2 0.24 1 0.24 6.33 0.0401

C^2 1.05 1 1.05 28.14 0.0011

Residual 0.26 7 0.037

Lack of Fit 0.18 3 0.061 3.13 0.1493not significat

Pure Error 0.078 4 0.019

Cor Total 2.42 16

The Model F-value of 6.42 implies the model is significant. There is only a 1.14% chance that an F-value this large could

occur due to noise.Values of "Prob > F" less than 0.0500 indicate model terms are significant.In this case B, B^2, C^2 are

significant model terms.Values greater than 0.1000 indicate the model terms are not significant.If there are many insignificant

model terms (not counting those required to support hierarchy),model reduction may improve your model.The "Lack of Fit F-

value" of 3.13 implies the Lack of Fit is not significant relative to the pure error. There is a 14.93% chance that a "Lack of Fit

F-value" this large could occur due to noise. Non-significant

Table 8 - Various R2 statistics for Surface Roughness

A negative "Pred R-Squared" implies that the overall mean is a better predictor of your response than the current model."Adeq

Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The ratio of 6.565 indicates an adequate signal.

This model can be used to navigate the design space.

Final Equation in Terms of Coded Factors:

Roughness=+1.22+0.099* A-0.21* B+0.079* C-0.13* AB-0.093* AC- 0.050*BC-

0.19*A^2-0.24*B^2-0.50*C^2

The equation in terms of coded factors can be used to make predictions about the response forgiven levels of each factor. By

default, the high levels of the factors are coded as +1 and thelow levels of the factors are coded as -1. The coded equation is

useful for identifying therelative impact of the factors by comparing the factor coefficients.

Final Equation in Terms of Actual Factors:

Roughness=-4.62497+0.028065*Speed+25.20186*feed+24.26911*doc-0.044643*speed*feed

-0.016518*speed*doc-10.00000*feed*doc-6.19420E-005*Speed^2-94.70000*feed^2-49.92500

*doc^2

=

Std. Dev. 0.19 R-Squared 0.8919

Mean 0.78 Adj R-Squared 0.7529

C.V. % 24.82 Pred R-Squared -0.2637

PRESS 3.05 Adeq Precision 6.565




The equation in terms of actual factors can be used to make predictions about the response forgiven levels of each factor. Here,

the levels should be specified in the original units for each factor. This equation should not be used to determine the relative

impact of each factor because the coefficients are scaled to accommodate the units of each factor and the interceptis not at the

center of the design space .Proceed to Diagnostic Plots (the next icon in progression). Be sure to look at the:

(1) Normal probability plot of the studentized residuals to check for normality of residuals.

(2) Studentized residuals versus predicted values to check for constant error.

(3) Externally Studentized Residuals to look for outliers, i.e., influential values.

(4) Box-Cox plot for power transformations.

If all the model statistics and diagnostic plots are OK, finish up with the Model Graphs icon.

From Table 7, we can see that the P-Value for the model is 0.033 which is lesser than the significance value of 0.05. Hence,

the model is significant. The lack-of-fit has a P-value of 0.11and hence, it is insignificant, which is desirable. Feed is found to

be the most influential parameter affecting the surface roughness with the lowest P-value among all three parameters.

Fig. 1:NORMAL PLOT OF RESIDUALS

Fig. 1 the normal probability plot of the residuals (i.e. error = predicted value from model−actual Value) for surface roughness

is shown in Fig. 1, Fig. 1 reveals that the residuals lie Reasonably close to a straight line, giving support that terms mentioned

in the model are the only significant (Montgomery,2001)

Design-Expert® SoftwareRoughness

Color points by value ofRoughness:

1.42

0.3

Externally Studentized Residuals

No

rm

al

% P

ro

ba

bil

ity

Normal Plot of Residuals

-4.00 -2.00 0.00 2.00 4.00

1

5

10

20

30

50

70

80

90

95

99




Fig. 2 PERTURBATIONS

Fig 2 shows the variation of surface roughness with speed, feed and depth of cut. It can be observed from the fig that

increasing depth of cut increases the surface roughness marginally and it can be considered not affecting surface roughness

for this range. It can also be seen from the graph that surface roughness decreases with increase in cutting speed upto a

certain point then further increase in cutting speed leads to increase in surface roughness. Increasing feed increases the

surface roughness, this is anticipated as it is well known that for a given tool nose radius, the theoretical surface roughness

(Ra= f 2/(32×re)) is mainly a function of the feed rate (Shaw, 1984).

Fig. 3 CONTOUR

Design-Expert® SoftwareFactor Coding: ActualRoughness (micrometer)

Actual FactorsA: speed = 146B: feed = 0.11C: doc = 0.2

-1.000 -0.500 0.000 0.500 1.000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

A

AB

B

C

C

Perturbation

Deviation from Reference Point (Coded Units)

Ro

ug

hn

es

s (

mic

ro

me

ter)


Design Points1.42

0.3

X1 = A: speedX2 = B: feed

Actual FactorC: doc = 0.2

90 118 146 174 202

0.06

0.08

0.1

0.12

0.14

0.16

Roughness (micrometer)

A: speed (m/min)

B:

fee

d (

mm

/re

v)

0.8

0.8

1

1.25




Figure 4 - Surface roughness 3D surface in cutting speed and feed rate plane at depth of cut of 0.1mm

Figure 5 - Surface roughness 3D surface in cutting speed and feed rate plane at depth

of cut of 0.2mm


Design points above predicted valueDesign points below predicted value1.42

0.3



0.06 0.08

0.1 0.12

0.14 0.16

90

118

146

174

202

-0.5

0

0.5

1

1.5

2

Ro

ug

hn

es

s (

mic

ro

me

ter)

A: speed (m/min)

B: feed (mm/rev)



0.3



0.06 0.08

0.1 0.12

0.14 0.16

90

118

146

174

202

-0.5

0

0.5

1

1.5

2

Ro

ug

hn

es

s (

mic

ro

me

ter)

A: speed (m/min)

B: feed (mm/rev)




Figure 6 - Surface roughness 3D surface in cutting speed and feed rate plane at depth of cut

of 0.3mm

Figure 4, 5, 6 shows the Surface roughness in 3D surface in cutting speed and feed rate plane at three depth of cuts (0.1, 0.2,

0.3mm) respectively. All have curvilinear profile in accordance to the quadratic model fitted. It can clearly be seen that

surface roughness is increasing with increase in the feed rate and it varies with cutting speed, first decreases then increases as

discussed before.

Figure 7 - Surface roughness 3D surface in cutting speed and depth of cut plane at

feed of 0.06mm/rev



0.3



0.06 0.08

0.1 0.12

0.14 0.16

90

118

146

174

202

-0.5

0

0.5

1

1.5

2

Ro

ug

hn

es

s (

mic

ro

me

ter)

A: speed (m/min)

B: feed (mm/rev)



0.3

X1 = A: speedX2 = C: doc

Actual FactorB: feed = 0.06

0.1

0.15

0.2

0.25

0.3

90

118

146

174

202

-0.5

0

0.5

1

1.5

2

Ro

ug

hn

es

s (

mic

ro

me

ter)

A: speed (m/min)

C: doc (mm)




figure 8 - Surface roughness 3D surface in cutting speed and depth of cut plane at feed of 0.1005 mm/rev

figure 9 - Surface roughness 3D surface in cutting speed and depth of cut plane at feed of 0.16

mm/rev

Figure 7, 8, 9 - shows the Surface roughness in 3D surface in cutting speed and depth of cut plane at three feed rates (0.10,

0.15, 0.2 mm/rev) respectively. It can clearly be seen that surface roughness does not vary much with the increase in depth of

cut.


1.42

0.3



0.1

0.15

0.2

0.25

0.3

90

118

146

174

202

-0.5

0

0.5

1

1.5

2

Ro

ug

hn

es

s (

mic

ro

me

ter)

A: speed (m/min)

C: doc (mm)



0.3



0.1 0.15

0.2 0.25

0.390

118

146

174

202

-0.5

0

0.5

1

1.5

2

Ro

ug

hn

es

s (

mic

ro

me

ter)

A: speed (m/min)

C: doc (mm)




Figure 10- Optimation contour highlighting optimized (lowest) value of surface roughness at a Particular setting.

Figure 10 - Shows the optimization contour for surface roughness in feed and cutting speed plane. The figure shows the

predicted optimized value (minimum value) of surface roughness which can be obtained in the given sets of feed, speed,

depths of cut. The solution obtained through the Design Expert software is shown below. At this set of speed, feed and depth

of cut one can get the lowest value of surface Roughness.

Table 8- Solution:

Surface Roughness 0.595 µm

Feed 0.11mm/rev

DOC 0.3 mm

Speed 90 m/min

Thus the minimum value of roughness that can be achieved in given ranges of parameter values is 0.595 μm, which is

obtained when feed is approximately at medium level (0.115mm/rev), depth of cut is near to higher level (0.3mm) and speed

is also at low level (90m/min).

IV. CONCLUSION:

This project presents the findings of an experimental investigation of the effect of cutting speed, feed rate and depth of cut on

the surface roughness in turning of EN-31material using coated carbide tool and following conclusions are drawn. Quadratic

model is fitted for surface roughness. The results show that the surface roughness does not vary much with experimental

depth of cut in the range of 0.1 to 0.3 mm. A quadratic model best fits the variation of surface roughness with feed rate, speed

and depth of cut. Good surface finish can be achieved Thus the minimum value of roughness that can be achieved in given

ranges of parameter values is 0.595 μm, which is obtained when feed is approximately at medium level (0.11mm/rev), depth

of cut is near to higher level (0.3mm) and speed is also at low level (90m/min).Contour plots can be used for selecting the

cutting parameters for providing the given desired surface roughness.

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Design Points1.42

0.3



90 118 146 174 202

0.06

0.085

0.11

0.135

0.16Roughness (micrometer)

A: speed (m/min)

B: fe

ed

(

mm

/r

ev

)

0.20.4

0.6

0.6

0.8

Prediction 0.595




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