PARAMETRIC EFFECT AND OPTIMIZATION OF …thinkinglean.com/img/files/PAPER_6.pdf · Department of...

International Journal of Lean Thinking Volume 3, Issue 2 (December 2012)

Lean Thinkingjournal homepage: www.thinkinglean.com/ijlt

PARAMETRIC EFFECT AND OPTIMIZATION OF SURFACE ROUGHNESS OF EN 31 IN CNC DRY TURNING

C R Barik* Department of Mechanical Engineering

National Institute of Technical Teachers’

Training and Research

Kolkata-700106, West Bengal, India E-mail:

[email protected]

N K Mandal Department of Mechanical Engineering

National Institute of Technical Teachers’

Training and Research

Kolkata-700106, West Bengal, India

E-mail: [email protected]

A B S T R A C T K E Y W O R D S

A R T I C L E I N F O

CNC Turning, Surface Roughness, Response

Surface Method,

ANOVA,

Genetic Algorithm.

Received 05 May 2012

Accepted 13 June 2012

Available online 01 October 2012

This paper presents an experimental study of roughness

characteristics of surface roughness generated in CNC turning of

EN 31 alloy steel and optimization of machining parameters based

on Genetic Algorithm. The three level central composite design is

employed for developing mathematical model for predicting

surface roughness parameter. Response Surface Methodology is

applied successfully in analyzing the effect of process parameters

on surface roughness parameter. The second order mathematical

model in terms of machining parameters is developed based on

experimental results. The experimentation is carried out

considering three machining parameters, viz., spindle speed, feed

rate and depth of cut as independent variables and the surface

roughness parameter as response variable. It is seen that the

surface roughness parameter decreases with increase in spindle

speed and depth of cut but increases with increase in feed rate. The

adequacy of the models of surface roughness has been established

with the analysis of variance (ANOVA). And finally for optimizing

the cutting parameters, Genetic Algorithm process has been

implied to achieve minimum surface roughness.

________________________________

* Corresponding Author

1. Introduction and literature survey

A surface can be described in simple terms to be the outermost layer of an entity. An

interface can be defined to be the transition layer between two or more entities that differ

either chemically or physically or in both aspects. Hudson defines a surface or interface to exist

in any system that has a sudden change of system properties like density, crystal structure and

orientation, chemical composition, and ferro- or para-magnetic ordering. Surfaces and

interfaces can be examined closely using the high-resolution microscopy, physical and chemical

methods available. For their realization, a great number of simple and highly sophisticated

testing machines have been developed and used. These tools have been built by humans to sate

their innate curiosity of surface and interface interaction phenomena.

BARIK, MANDAL/ International Journal of Lean Thinking Volume 3, Issue 2(December 2012)

55

Surface science can be defined as a branch of science dealing with any type and any level of surface

and interface interactions between two or more entities. These interactions could be physical, chemical,

electrical, mechanical, thermal, biological, geological, astronomical and maybe even emotional.

Surface engineering is a multidisciplinary activity intended to tailor the properties of the surface of

engineering components so that their serviceability can be improved. The ASM Handbook defines surface

engineering as “treatment of the surface and near-surface regions of a material to allow the surface to

perform functions that are distinct from those functions demanded from the bulk of the material”.

Ra (CLA), arithmetic average roughness (centre line average): the arithmetic average value of

filtered roughness profile determined from deviations about the centre line within the evaluation

length; the most popular parameter for a machining process and product quality control. This

parameter is easy to define, easy to measure even in the least sophisticated profilometers and gives a

general description of surface amplitude. Though it lacks physical significance, it is established in

almost every national standard for measuring roughness. It is very common type surface roughness

parameter and widely used for the surface roughness measurement.

Fig. 1: Mean line system and definition of Ra

Surface roughness is referred to the deviation from the nominal surface of the third up to sixth order.

The order of deviation is defined in international standards. First and second order deviations are related

to form, i.e. flatness, circularity etc. and to waviness, respectively. They occur due to machine tool errors,

deformation of the work-piece, erroneous setups and clamping, vibration and work-piece material

inhomogeneties. Third and fourth order deviations are referred to periodic grooves, and to cracks and

dilapidations, which are connected to the shape and condition of the cutting edges, chip formation and

process kinematics. Fifth and sixth order deviations take place due to work-piece material structure, which

is connected to physical-chemical mechanism acting on a grain and lattice scale (slip, diffusion, oxidation,

residual stress, etc.). Different order deviations are superimposed and form the surface roughness profile,


56

Fig. 2: Different form of deviations of surfaces

To achieve high dimensional accuracy, the machining process must produce a surface with lesser

deviations from the nominal surface, i.e. the surface roughness must be as small as possible. Lower surface

roughness leads to improve the surface finish, better contact between mating surfaces, increased bearing

surface, lesser friction etc.. In a totality, the less the surface roughness is, better the quality of the surface.

EXPERIMENTATION

The EN-31 Steel rod of size 70mm in length and diameter 32mm has been used as a work piece

material for the present experiments because 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. The chemical composition heat treatment properties

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

Table 1: Chemical Composition of EN-31 Table 2: Mechanical Properties of EN-31

Table 3: Heat Treatment of EN-31

Element Objective

Hardening Temperature 802˚- 860˚ C

Quenching Medium Oil

Tempering Temperature 180˚- 225˚ C

Brinell-Rockwell Hardness 59 - 65

Element Chemical composition (wt%)

C 1.08 %

Si 0.25 %

Mn 0.53 %

S 0.015 %

P 0.022 %

Ni 0.33 %

Cr 1.46 %

Mo 0.06 %

Element Objective

Tensile Strength 750 N/mm²

Yield Stress 450 N/mm²

Reduction of Area 45%

Elongation 30%

Modulus of elasticity 215 000 N/mm²

Density 7.8 Kg/m3

Hardness 63 HRC


57

EN-31 steel is basically applied for bearing purpose. This steel is mainly known as bearing steel. The

EN-31 bearing steel is applied for roller bearing components such as 1) Brakes 2) Cylinders 3) Conical and

Needle rollers. The cutting tool material and tool holder used for this experiment are CERATIZIT make

Carbide tool inserts

Specification : CNMG 1200408EN-TMR (insert)

Tool Holder Specification : MCLNL 2020-K12.

Fig. 3: CNC Turning for experimentation

Fig. 4: Experimentation


58

For measurement of surface roughness, a stylus type surface roughness profilometer has been used during

the experiment. The details of surface roughness profilometer used are as follows

Make : Mahr Federal Inc.

Model : Pocket Surf® PS1

Pick-up : Inductive skidded pick-up, 5 µm (200 µin) stylus tip

Measuring Force : 0.7 mN (approx)

Cut-off length (lc) : 0.25 mm, 0.8 mm, 2.5 mm

Traversing Length (Lt) : 1.75 mm, 5.6 mm, 17.5 mm

Short Cut-off : Selectable

Evaluation Length (ln) : 1.25 mm, 4.0 mm, 12.50 mm

Sampling Lengths : Number ‘n’ selectable – 1 to 5

Fig. 5(a): Different parts of surface roughness profilometer

Fig. 5(b): Different parts of surface roughness profilometer


59

The working ranges of the parameters for subsequent design of experiment, based on Response Surface

Methodology with rotatable design have been selected. In the present experimental study spindle speed,

feed rate and depth of cut have been considered as process variables. The process variables with their

units (and notations) are listed in Table

Table 4: Process variables with boundation

VARIABLES

LEVEL

-1 0 -1

Speed(rpm) 1000 1500 2000

Feed(mm/rev) 0.10 0.15 0.20

Depth of Cut(mm) 0.1 0.2 0.3

Table 5: Design of Experiment with coded values and Observed Responses

Sl. No.

Std. Order

Run Order

Speed Feed Rate

Depth of Cut

Ra

1 17 1 0 0 0 0.918

2 4 2 1 1 -1 1.11

3 5 3 -1 -1 1 1.167

4 18 4 0 0 0 0.922

5 16 5 0 0 0 0.918

6 9 6 -1 0 0 0.938

7 8 7 1 1 1 0.952

8 2 8 1 -1 -1 0.425

9 12 9 0 1 0 1.05

10 20 10 0 0 0 0.925

11 14 11 0 0 1 1.035

12 1 12 -1 -1 -1 0.48

13 6 13 1 -1 1 1.107

14 19 14 0 0 0 0.92

15 10 15 1 0 0 0.868

16 15 16 0 0 0 0.926

17 13 17 0 0 -1 0.776

18 3 18 -1 1 -1 1.213

19 7 19 -1 1 1 1.051

20 11 20 0 -1 0 0.806


60

RESULT AND DISCUSSION

The Analyses of variance (ANOVA) for the adequacy of the models are then performed in the

subsequent steps. The F ratio is calculated for 95% level of confidence for each response. The values which

are less than 0.05 are considered significant and the values greater than 0.05 are not significant and the

models are adequate to represent the relationship between machining response and the machining

parameters. Since the turning is non-linear process in nature the linear polynomial will not be able to

predict the responses accurately therefore the Second-order model (quadratic model) is used. It is

observed from the adequacy test by ANOVA that linear terms, speed, feed rate and depth of cut are

significant.

Table 6: ANOVA Table for Ra (before elimination) Estimated Regression Coeffecients

Term Coef SE Coef T P

Constant 0.915009 0.004493 203.656 0.000

Speed -0.038700 0.004133 -9.364 0.000

Feed Rate 0.139100 0.004133 33.657 0.000

Depth of Cut 0.130800 0.004133 31.649 0.000

Speed*Speed -0.002273 0.007881 -0.288 0.779

Feed Rate*Feed Rate 0.022727 0.007881 2.884 0.016

Depth of Cut*Depth of Cut 0.000227 0.007881 0.029 0.978

Speed*Feed Rate -0.010875 0.004621 -2.354 0.040

Speed*Depth of Cut -0.000125 0.004621 -0.027 0.979

Feed Rate*Depth of Cut -0.211125 0.004621 -45.691 0.000

S = 0.0130693 PRESS = 0.00991408

R2 = 99.77% R2 (pred) = 98.66% R2 (adj) = 99.56%

Table 7: ANOVA Table for Ra after Backward Elimination

Term Coef SE Coef T P

Constant 0.91460 0.003641 251.176 0.000

Speed -0.03870 0.003641 -10.628 0.000

Feed Rate 0.13910 0.003641 38.201 0.000

Depth of Cut 0.13080 0.003641 35.922 0.000

Feed Rate*Feed Rate 0.02150 0.005150 4.175 0.001

Speed *Feed Rate -0.01087 0.004071 -2.671 0.019

Feed Rate*Depth of Cut -0.21113 0.004071 -51.860 0.000

S = 0.0115147 PRESS = 0.00388003

R2 = 99.77% R2 (pred) = 99.48% R2 (adj) = 99.66%


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The levels of significance for the linear terms, speed, feed rate and depth of cut are depicted in the

Table 6. The fit summary recommends that the quadratic model is statistically significant for analysis of Ra.

For the appropriate fitting of Ra, the non-significant terms (p-value is greater than 0.05) are eliminated by

the backward elimination process. The ANOVA Table for the curtailed quadratic model for Ra is shown in

Table 7. The reduced model results indicate that the model is significant (R2 and adjusted R2 are

99.77%and 99.66%, respectively), and lack of fit is non-significant (p-value is less than 0.05). After

eliminating the non-significant terms, the final response equation for Ra is given as follows

Ra = 0.91460 – 0.03870 × speed + 0.13910 × feed rate + 0.13080 × depth of cut +

0.02150 × feed rate2 – 0.01087 × speed × feed rate – 0.21113 × feed rate × depth of cut

20 experiments were conducted in duplicate and the average values of Ra with design matrix were

tabulated in Table 8. To analyze the data, checking of goodness of fit of the model is very much required.

The model adequacy checking includes the test for significance of the regression model, test for

significance on model coefficients, and test for lack of fit. For this purpose, analysis of variance (ANOVA) is

performed. The fit summary recommended that the quadratic model is statistically significant for analysis

of Ra.

Table 8: Analysis of Variance for Ra

Source DF Seq SS Adj SS Adj MS F P

Regression 6 0.739399 0.739399 0.123233 929.44 0.000

Linear 3 0.379551 0.379551 0.126517 954.21 0.000

Square 1 0.002311 0.002311 0.002311 17.43 0.001

Interaction 2 0.357536 0.357536 0.178768 1348.29 0.000

Residual Error 13 0.001724 0.001724 0.000133

Lack-of-Fit 8 0.001664 0.001664 0.000208 17.48 0.003

Pure Error 5 0.000060 0.000060 0.000012

Total 19 0.741123


62

0.020.010.00-0.01-0.02-0.03

99

95

90

80

70

60

50

40

30

20

10

5

1

Residual

Pe

rce

nt

Normal Probability Plot(response is Ra)

Fig. 6: Normal probability Plot of Residuals for Ra

1.31.21.11.00.90.80.70.60.50.4

0.01

0.00

-0.01

-0.02

-0.03

Fitted Value

Re

sid

ua

l

Versus Fits(response is Ra)

Fig. 7: Plot of Residuals vs. Fits for Ra

The parametric analysis has been carried out to study the influences of the input process parameter such

as Speed, Feed Rate and Depth of Cut on the process responses, i.e. Ra in dry turning process. Three-

dimensional response surface plots were formed based on the RSM quadratic models to evaluate the


63

change of response surface. These plots can also give further assessment of the correlation between the

process parameters and responses.

1

0.50

0

0.75

1.00

-1

1.25

0 -11

Ra

Speed

Feed Rate

Surface Plot of Ra vs Speed, Feed Rate

Fig. 8: Variation in Ra according to change in Speed and Feed Rate

1

0.50

0

0.75

1.00

-1

1.25

0 -11

Ra

Speed

Depth of Cut

Surface Plot of Ra vs Speed, Depth of Cut

Fig. 9: Variation in Ra according to change in Speed and Depth of Cut

Figure 8 illustrates the response surface plot of Ra with respect to input parameters Speed and Feed Rate.

The value of Ra is shown to decrease with decrease of Feed Rate but constant with this range of Speed. In

the Figure 9 response surface plot of Ra with Speed and Depth of Cut is depicted. Ra decreases with

decrease in Depth of Cut but constant with this range of Speed.

1

0.50

0

0.75

1.00

-1

1.25

0 -11

Ra

Feed Rate

Depth of Cut

Surface Plot of Ra vs Feed Rate, Depth of Cut

Fig. 10: Variation in Ra according to change in Feed Rate and Depth of Cut


64

Figure 10 also depicts that the Ra decreases with the decrease in respective Feed Rate and Depth of Cut.

The process optimization is done by using genetic algorithm. The result of the optimizations i.e. the

optimized values of the input process variables are represented here through the graph. The output

process combinations are in coded forms and are decoded to their actual form to get the desired values.

Here plots of each output elements are shown below.

Fig. 11: Plot showing selection and best fitness for Ra

The Fig. 11 plot shows the best fitness value, mean and the selection for the output ‘Ra’. The best fitness

value i.e. the optimized value or predicted value is 0.4273 µm.

After optimization process the results i.e. the outputs are obtained in coded forms. The optimized coded

and equivalent actual values of input process parameters for all the responses are shown in the table

below.

Table 9: Optimization Result

Response

Optimized Value

Input Process Variables in Coded Form Input Process Variables in Actual Form

Speed Feed Rate Depth of Cut Speed (rpm)

Feed Rate (mm/rev)

Depth of Cut (mm)

Ra 1.0000 -1.0000 -0.9999 2000 0.1 0.10001


65

The predicted results according to the optimized input process variables are given in the table 10.

Table 10: Predicted Response at Optimized Process Inputs

Responses Predicted Response

Ra (µm) 0.4273

Confirmation test is done to check the adequacy of the developed RSM model and the experimental

results thus found on the basis of optimized process parameters are given in table 11. It could be noticed

that the predicted results are in acceptable zone with respect to the experimental results and thus it is

concluded that the developed model seems to be satisfactory.

Table 11: Result of Conformation Test

Response

Unit

Response Value

Predicted Experimental

Ra µm 0.4273 0.438

CONCLUSION

The result of optimization considering the response showed the optimum condition for the response with

the optimum value or the best fitted value. According to the confirmation test the developed model seems

to be satisfactory because the predicted result is in an acceptable zone with respect to the experimental

result.

REFERENCES

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31 steel using Response Surface Methodology”, International Journal of Applied Research in

Mechanical Engineering, Volume-1, Issue-1

Ahmed S. G., (2006), “Development of a Prediction Model for Surface Roughness in Finish Turning of

Aluminium”, Sudan Engineering Society Journal, Volume 52, Number 45, pp. 1-5.

Chakraborty RC, (2010), “Fundamentals of Genetic Algorithms: AI Course”, Lecture 39-40, Notes, Slides.

www.myreaders.info/html/artificial_intelligence.html

Feng C. X. (Jack) and Wang X., (2002), “Development of Empirical Models for Surface Roughness

Prediction in Finish Turning”, International Journal of Advanced Manufacturing Technology,

Volume 20, pp. 348–356.

http://www.myreaders.info/html/artificial_intelligence.html


66

Özel T. and Karpat Y., (2005), “Predictive modeling of surface roughness and tool wear in hard turning

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