Surface Roughness Prediction Model for CNC Turning of EN-8 Steel Using Response Surface Methodology

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International Journal of Emerging Technology and Advanced Engineering

Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 5, Issue 6, June 2015)

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Surface Roughness Prediction Model for CNC Turning of

EN-8 Steel Using Response Surface Methodology

Ranganath M S

1

, Vipin

2

, Harshit

3

1

Associate Professor, 2Professor, 3Student,

1, 2_{Production and Industrial Engineering,}3_{Mechanical Engineering, Delhi Technological University, Delhi, India}

Abstract— Due to the widespread use of highly automated machine tools in the metal cutting industry, Manufacturing requires highly reliable models and methods for the prediction of output performance (surface roughness) in the machining process. In this study prediction model of surface roughness has been developed for turning EN-8 steel with uncoated carbide inserts using Response surface methodology (RSM). The model was developed in the form of multiple regression equations correlating dependent parameter surface roughness, with cutting speed, feed rate and depth of cut, in a turning process. The box behnken design was used to plan the experiment. The second order model was found suitable for present work.Moreover a good agreement between the predicted and experimental surface roughness was observed within reasonable limit.

Keywords— Box Behnken Design, Carbide Inserts, EN-8

Steel, RSM, Surface Roughness.

I. INTRODUCTION

EN-8 is a medium carbon steel generally used for manufacturing of crank shafts, automobile axle beams, bolts, studs, connecting rod, lightly stressed gears, various parts of machine tools such as spindle gears, power transmission gears. Physical and mechanical properties of En8 steel are: hardness, 201-255 BHN; yeild strength, 460 N/mm2; melting point, 1500°C; density, 7840 kg/m3; thermal conductivity, 24.7 W/m-°k; and Young’s Modulus, 200x109 N/m2. Some Properties of EN-8 steel like low specific heat, tendency to get strain hardened and diffusion between tool and work material results poor surface finish and built-up-edge formation, thereby making material difficult to machine.

The aim of the present study is to develop a theoretical model for surface roughness using the main cutting parameters such as cutting speed, feed rate, and depth of cut on EN-8 steels using response surface methodology.

A.Response Surface Methodology

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Fig.1: Steps in Response Surface Methodology (RSM). [13]

(1) To determine the factor levels that will simultaneously satisfy a set of desired specifications,

(2) To determine the optimum combination of factors that yields a desired response and describes the response near the optimum,

(3) To determine how a specific response is affected by changes in the level of the factors over the Specified levels of interest,

(4) To achieve a quantitative understanding of the system behavior over the region tested,

(5) To predict product properties throughout the region, even for a factor combinations not actually run,

(6) To find the conditions necessary for process stability (insensitive spot).

B.To generate contour surface [5]

Response surface methodology, or RSM, is a collection of mathematical and statistical techniques in which a response of interest is influenced by several variables and the objective is to optimize this response. For example, suppose that a chemical engineer wishes to find the levels of temperature (x1) and pressure (x2) that maximize the

yield (y) of a process. The process yield is a function of the levels of temperature and pressure, y = f (x1, x2) +e

Where e represents the noise or error observed in the response y. Then the surface represented by h= f (x1, x2),

which is called a Response surface. We usually represent the response surface graphically, where h is plotted versus the levels of x1 and x2. To help visualize the shape of a

response surface, we often plot the contours of the response surface as well. In the contour plot, lines of constant response are drawn in the x1, x2 planes. Each contour

corresponds to a particular height of the response surface. Objective is to optimize the response

32

Fig.2: To generate contour surface [5]

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The factorial portion is a full factorial design with all factors at three levels, the star points are at the face of the cube portion on the design which correspond to value of -1. This is commonly referred to as a face centered CCD. The center points, as implied by the name, are points with all levels set to coded level 0, the midpoint of each factor range, and this is repeated six times. Twenty experiments to be performed. For each experimental trial, a new cutting edge to be used. The latest version of the Minitab or Design Expert may be used to develop the experimental plan for response surface methodology. The same software can also be used to analyze the data collected.

C.Box-Behnken Design

The Box-Behnken design is an independent quadratic design in that it does not contain an embedded factorial or fractional factorial design. In this design the treatment combinations are at the midpoints of edges of the process space and at the center. These designs are rotatable (or near rotatable) and require 3 levels of each factor. Each design can be thought of as a combination of a two-level (full or fractional) factorial design with an incomplete block design. In each block, a certain number of factors are put through all combinations for the factorial design, while the other factors are kept at the central values. For instance, the Box–Behnken design for 3 factors involves three blocks, in each of which 2 factors are varied through the 4 possible combinations of high and low. It is necessary to include centre points as well.In present work, 4points per block will result in 12 experiments and A few more pointsadded at the centre to give roughly equal precision forresponse. For threeparameters, five additional experiments need to beconducted at the origin.The total number of experiments in the presentwork is 12+5=17 (Fig. 3). Entire experimentationneed not be replicated to find the error mean square,since this can be found out by replicating the centrepoint

Fig.3: Cube [13]

D.Turning Process Parameters[20]

The identified process parameters affecting characteristics of turned parts are: i) Cutting tool parameters–tool geometry and tool material; ii) Work piece related parameters- metallography, hardness, etc.; iii)Cutting parameters- cutting speed, feed rate and depth of cut.

II. METHODOLOGY

E.Experimental Details

1)Work Piece Material:

EN-8 steel was used as work material and its chemical composition is given in table [1]

TABLE1

Chemical Composition of EN-8

2)Cutting Tool:

Uncoated Carbide Insert (CNMG120408 TTR)

3)Surface Roughness Testing Machine:

The Surface roughness testing machine used for measuring

Surface roughness (Ra) in this experimental analysis was Talysurf Meter (Taylor/Hobson Surtronic 3+) fig.[4].

4)Experimental Set Up:

Turning was carried out on CNC lathe. The various cutting parameters choosed were cutting speed, depth of cut, feed rate. The measurements of average surface roughness (Ra) were taken on talysurf meter (Taylor/Hobson Surtronic 3+).

Three measurements of surface roughness were taken at distance of 100mm per measurement of machined work piece and their average value was used for the further analysis.

EN NO.

E.S.C.

SYMB OL

CHEMICAL COMPOSITION

C% Si% Mn% S

% P%

8 HTC

.35/.45

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Fig.4:Surface roughness tester

5)Cnc Machine:

The cnc machine used for machining Purpose is of laxmi machine works model number (LL20TL3) .the specification are given in table [2].

6)Factors and Response [4]:

1) Factors taken

A-cutting speed (rpm)

B-Feed rate (mm/min)

C-Depth of cut (mm)

2) Response taken

Y -surface roughness Ra (μm)

TABLE 2

Specification Of Cnc Machine

Title Description Unit LL20T

L3

Capacity Swing over bed mm 510

Chuck dia, max. mm 200

Max turning diameter

mm 320

Max. turning length mm 310

Admit between centres

mm 420

Spindle Spindle nose type A2 – 6

Hole through spindle

mm 61

Cutting speed rpm 3500

Spindle motor power(cont./15min)

kW 7.5/11

Feed system

Cross travel X-axis mm 185

Longitudinal travel Z-axis

mm 370

Rapid traverse rate X/Z-axes

m/min 30 / 30

Turret No. of stations nos 8

Tool shank size mm 25×25

Maximum boring bar dia

mm 40

Turret indexing type Hydraulic

Tailstock Quill dia mm 75

Quill stroke mm 100

Quill taper – MT-4

CNC system

Controller – Fanuc

Machine size

Front x Side mm 2065 X

1925 Machine weight

(Approx)

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TABLE 3

Factors And Their Levels

Value type Cutting

speed(A)

(RPM)

Feed Rate

( B )

(mm/ rev)

Depth of cut

(C )

(mm)

Low 1000 0.12 0.2

intermediate 1200 0.18 0.4

High 1400 0.24 0.6

F.Modeling And Analysis-

Regression and graphical analysis were done using Design Expert 9.0 software (dx9). Goodness of fit test of model was done for analysis of the data. Analysis of variance (ANOVA) was done to test for significance of regression model and on model coefficients, and test for lack of fit i.e.to check model adaquecy.

ANOVA (Analysis of variance)-ANOVA helps us to identify which parameter is important for us after literature review ANOVA table [5] is obtained for Surface roughness. The observations made from the ANOVA table are 1.The Model F-value of 10.58 implies the model is significant 2.There is only a 0.26% 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. 3. In this case A, B, AB, A^2 are significant model terms.4. Values greater than 0.1000 indicate the model terms are not significant.

TABLE 4

Experimental Values Of Surface Roughness

Factor 1 Factor 2 Factor 3 Response Y

Run A:CUTTING

SPEED

B:DEPTH

OF CUT C:FEED RATE ROUGHNESS

RPM mm mm/rev m

1 1200 0.2 0.12 1.59

2 1000 0.2 0.18 2.92

3 1200 0.4 0.18 1.05

4 1200 0.2 0.24 1.81

5 1400 0.4 0.24 0.935

6 1200 0.4 0.18 1.05

7 1000 0.4 0.24 1.69

8 1200 0.4 0.18 1.05

9 1400 0.4 0.12 0.901

10 1200 0.4 0.18 1.05

11 1200 0.4 0.18 1.05

12 1000 0.6 0.18 1.59

13 1200 0.6 0.12 0.6

14 1400 0.2 0.18 0.693

15 1200 0.6 0.24 0.945

16 1400 0.6 0.18 1.01

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Fig.5: Normal probability plot residuals for surface roughness

[image:6.612.59.274.144.341.2]

Fig.6: Plot for direct effect of various factors on surface roughness

TABLE 5 Anova Table

ANOVA for Response Surface Quadratic model

Analysis of variance table [Partial sum of squares-Type III]

Sum of Mean F

p-value

Source Squares df Square Value Prob>

F

Model 4.62 9 0.51 10.58 0.0026 Significant

A-CUTTING

SPEED

2.28 1 2.28 47.01 0.0002

B-DEPTH

OF CUT 1.03 1 1.03 21.20 0.0025

C-FEED

RATE 0.058 1 0.058 1.19 0.3118

AB 0.68 1 0.68 13.98 0.0073

AC

5.290E-004 1

5.290E-004 0.011 0.9198

BC

3.906E-003 1

3.906E-003 0.081 0.7848

A^2 0.32 1 0.32 6.59 0.0372

B^2 0.22 1 0.22 4.50 0.0715

C^2

7.252E-003 1

7.252E-003 0.15 0.7105

Residual 0.34 7 0.049

Lack of Fit 0.34 3 0.11

Pure Error 0.000 4 0.000

Cor Total 4.96 16

TABLE 6 R-Squared Test

Std. Dev. 0.22 R-Squared 0.9315

Mean 1.27 Adj R-Squared 0.8434

C.V. % 17.38 Pred R-Squared -0.0960

PRESS 5.43 Adeq Precision 12.910

Design-Expert® Software ROUGHNESS

Color points by value of ROUGHNESS:

2.92

0.6

Externally Studentized Residuals

N

o

r

m

a

l

%

P

r

o

b

a

b

il

it

y

Normal Plot of Residuals

-8.00 -6.00 -4.00 -2.00 0.00 2.00 4.00 6.00 8.00

1 5 10 20 30 50 70 80 90 95 99

Design-Expert® Software Factor Coding: Actual ROUGHNESS (uM)

Actual Factors A: CUTTING SPEED = 1200 B: DEPTH OF CUT = 0.4 C: FEED RATE = 0.18

-1.000 -0.500 0.000 0.500 1.000 0.5

1 1.5 2 2.5 3

A

A B

B C

C

Perturbation

Deviation from Reference Point (Coded Units)

R

O

U

G

H

N

E

S

(

u

M

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A negative "Pred R-Squared" implies that the overall mean is a better predictor of our response than the current model."AdeqPrecision"measures the signal to noise ratio. A ratio greater than 4 is desirable.Our ratio of 12.910 indicates an adequate signal. This model can be used to navigate the design space. Normal probability plot of the residuals for SR (Fig. 4) indicates that residuals are falling on a straight line, indicating that the errors are normally distributed.

G.Regression Equation-

Regression equation obtained using designexpert 9 software for surface roughness Ra (Y) as

R

a= 20.09262-0.023144× s -19.16875×d +5.67292×f

+0.010294× s × d -9.58333E-004 × s × f + 2.60417 × d × f +6.88750E-006× s2 +5.69375 x d2-11.52778 x f2

III. RESULTS AND DISCUSSIONS

The interaction effect of process parameters on the surface roughness is discussed below.

Fig.7: Surface interaction dimensional views and view of cutting speed and depth of cut over surface roughness

Fig7 shows the interaction and direct effect of depth of cut and cutting speed on surface roughness. The above interaction figure evidenced that the cutting speed and depth of cut on the surface roughness of turning process has a significant effect.

From the Fig7it has been concluded that the lower cutting speed(1000 rpm) with the increase in depth of cut(0.2 to 0.6 mm) has a significant effect on surface roughness (2.8-1.25μm) whearesat higher cutting speed (1400rpm) with the increase in depth of cut (0.2 to 0.6 mm) has no significant effect on surface roughness (0.84 to 0.73μm). The conclusion can also be verified fromthe ANOVA table5.

Fig.8: Surface interaction dimensional views and Direct effect views of feed rate and depth of cut Over surface roughness

Fig.8 shows the interaction and direct effect of feed rate and depth of cut on surface roughness. The above interaction figure evidenced that the feed rate and depth of cut on the surface roughness of turning process has a significant effect. From the Fig.8 it has been concluded that at higher feed rate with increase in depth of cut the surface roughness deceases whereas lower feed rate and depth of cut decreases the surface roughness more significantly. To obtain the quality surface of Ra between 0.75 and 0.80 feed rate less than 0.18 and depth of cut less than 0.6 should be preferred.

The interaction fig.9 evidenced that the cutting speed and feed rate on the surface roughness of turning process has a significant effect. As the cutting speed increases from 1000 rpm to 1400 rpm the surface roughness value is reduced from 2.8 to 0.6 μm, where feed rate has the inverse relationship on surface roughness compared to the cutting speed. From the result it is concluded that thechange in feed rate (0.12-0.24 mm/rev) has asignificant effect on surface roughness (1.5-1.9μm)at lower cutting speed(1000rpm) whereas the change in feed rate (0.12-0.24 mm/rev) has no significant effect on surface roughness (0.62-0.7μm)at higher cutting speed (1400 rpm).

Design points above predicted value

Design points below predicted value

X1 = A: CUTTING SPEED X2 = B: DEPTH OF CUT

Actual Factor C: FEED RATE = 0.18

0.2

0.3

0.4

0.5

0.6

1000 1100 1200 1300 1400 0 0.5 1 1.5 2 2.5 3

R

O

U

G

H

N

E

S

(

u

M

)

A: CUTTING SPEED (RPM) B: DEPTH OF CUT (mm)

Design points above predicted value

Design points below predicted value

X1 = B: DEPTH OF CUT X2 = C: FEED RATE

Actual Factor A: CUTTING SPEED = 1200

0.12 0.15 0.18 0.21 0.24

0.2 0.3 0.4 0.5 0.6 0

0.5 1 1.5 2 2.5 3

R

O

U

G

H

N

E

S

(

u

M

)

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The conclusioncan also be verified from the ANOVA table 5.

Fig.9: Surface interaction dimensional views and Direct effect views of cutting speed and feed rate over surface roughness.

Validation of The Model-

The confirmatory test is conducted and regression model has been developed using box- behnken design of RSM are verified. Table 7 shows the comparison of predicted versus experimental value of surface roughness. The experimental values are found to be within 95% confidence interval which shows the validity of the model.

[image:8.612.55.272.153.338.2]

Fig.10: Contour plot for cutting speed and depth of cut

TABLE 7 Verification Of Experiments

IV. CONCLUSIONS

On the basis of the experimental results and derived analysis, the studies may be concluded as below:

 The consequences of the experimental data is used for predicting the outcome of assorted input machining parameters suchas cutting speed, feed and depth of cut on the surface roughness when machining EN-8 using carbide insert.

 A quadratic equation is developed and projected.

 Cutting speed has the strongest effect on the surface roughness among the selected parameters; it is inversely proportional to the response.

 Surface roughness is inversely proportional to depth of cut

Design-Expert® Software Factor Coding: Actual ROUGHNESS (uM) X1 = A: CUTTING SPEED X2 = C: FEED RATE Actual Factor B: DEPTH OF CUT = 0.378378

0.12 0.15 0.18 0.21 0.24

1000 1100

1200 1300

1400 0

0.5 1 1.5 2 2.5 3

R

O

U

G

H

N

E

S

(

u

M

)

A: CUTTING SPEED (RPM) C: FEED RATE (mm/rev)

Design Points

2.92

0.6

ROUGHNESS (uM) = 1.05 Std # 13 Run # 8

X1 = A: CUTTING SPEED = 1200 X2 = B: DEPTH OF CUT = 0.4

Actual Factor C: FEED RATE = 0.18

1000 1100 1200 1300 1400

0.2 0.3 0.4 0.5 0.6

ROUGHNESS (uM)

A: CUTTING SPEED (RPM)

B

:

D

E

P

T

H

O

F

C

U

T

(

m

)

1

1.5

2

2.5

5

Run

Experimental value of roughness

Predicted value of roughness

Error percent

m m %

1 1.59 1.455 8.8

2 2.92 2.857 2.12

3 1.05 0.884 14.4

4 1.81 1.694 6.4

5 0.935 0.759 18.4

6 1.05 1.05 0

7 1.69 1.91 -11.5

8 1.05 .884 14.4

9 0.901 0.688 23.4

10 1.05 0.884 14.4

11 1.05 0.884 14.4

12 1.59 1.317 17.4

13 0.6 .762 -21.2

14 0.693 0.906 -23.5

15 0.945 0.994 -4.9

16 1.01 1.073 -5.8

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 Surface roughness shows little variation with feed

rate.it is directly proportional to feed rate.

 Surface roughness is affected by factors in order as cutting speed followed by depth of cut.

 A good surface roughness was obtained atthe feed rate (0.12-0.15 mm/rev) andcutting speed (1200-1400 rpm).

 The change in feed rate (0.12-0.24 mm/rev)has a significant effect on surface roughness(1.5-1.9μm) at lowercutting speed(1000rpm).

 The change in feed rate (0.04-0.08mm/rev) has no significant effect on surfaceroughness (0.62-0.70μm) at highercutting speed (1400 rpm).

 A feed rate less than 0.16 and depth of cutfrom 0.5 to 0.6mm have been preferred forobtaining good surface finish.

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