A STUDY OF THE EFFECTS OF MACHINING PARAMETERS ON SURFACE ROUGHNESS USING RESPONSE SURFACE METHOD ON EN11 ALLOY STEEL IN THE END-MILLING PROCESS

International Journal of Mechanical Engineering and Technology (IJMET), ISSN 0976 – 6340(Print),

ISSN 0976 – 6359(Online), Volume 5, Issue 11, November (2014), pp. 47-58 © IAEME

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A STUDY OF THE EFFECTS OF MACHINING

PARAMETERS ON SURFACE ROUGHNESS USING

RESPONSE SURFACE METHOD ON EN11 ALLOY STEEL

IN THE END-MILLING PROCESS

Vishesh Ranglani1, Saurabh Pratap Singh

2, Shashi Kant Tripathi

3, Rahul Davis

4

1, 2, 3, 4

(Department of Mechanical Engineering, Shepherd School of Engineering and Technology,

SHIATS, Allahabad, U.P, India)

ABSTRACT

A series of experiments to determine the character of surface of the alloy steel have been

conducted. The main objective of this work is to develop a holistic understanding of the effects of

feed rate, spindle speed, depth of cut and type of coolant on the surface roughness and to create a

model for the conducted study. Such an understanding can provide sapience about the shortcomings

of controlling the finish of machined surfaces when the process parameters are adjusted to obtain a

certain surface finish. The model, which includes the effect of spindle speed, cutting feed rate and

depth of cut, and any three variable interactions, predicted the surface roughness values.

Keywords: Surface Roughness, Milling, ANOVA, EN11.

1. INTRODUCTION

The evaluation of surface roughness of machined parts using a direct contact method has

limitations in handling the different geometrical parts to be measured. Surface roughness affects

many functional parameters, such as friction, wear and tear, light reflection, heat transmission,

ability of distributing and holding a lubricant, coating etc. Therefore, the desired surface finish is

usually specified and appropriate processes are required to maintain the quality. Hence, the

inspection of surface roughness of the work piece is very important to assess the quality of a

component. Alternately, optical measuring methods are applied to overcome the limitations of stylus

method, but, they are also sensitive to lighting conditions and noise. The technique proposed in this

work, requires no apriority information about the lighting conditions and source of noise. Metal

cutting is one of the most significant manufacturing processes in the area of material removal [1].

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Black [2] defined metal cutting as the removal of metal chips from a work piece in order to obtain a

finished product with desired attributes of size, shape, and surface roughness. The imperative

objective of the science of metal cutting is the solution of practical problems associated with the

efficient and precise removal of metal from work piece. It has been recognized that the reliable

quantitative predictions of the various technological performance measures, preferably in the form of

equations, are essential to develop optimization strategies for selecting cutting conditions in process

planning [3-5].

Milling is a machining process in which the removal of metal takes place due to the cutting

action of a revolving cutter when the work is fed through it. Milling refers to the process of breaking

down, separating, sizing, or classifying aggregate material. For instance rock crushing or grinding to

produce uniform aggregate size for construction purposes, or separation of rock, soil or aggregate

material for the purposes of structural fill or land reclamation activities. Aggregate milling processes

are also used to remove or separate contamination or moisture from aggregate or soil and to produce

"dry fills" prior to transport or structural filling.

2. MATERIALS AND METHODS

2.1. RESPONSE SURFACE METHODOLOGY (RSM)

It is a collection of mathematical and statistical techniques for empirical model building. By

careful design of experiments, the objective is to optimize a response (output variable) which is

influenced by several independent variables (input variables).

Originally, RSM was developed to model experimental responses (Box and Draper, 1987),

and then migrated into the modeling of numerical experiments. The difference is in the type of error

generated by the response. In physical experiments, inaccuracy can be due, for example, to

measurement errors while, in computer experiments, numerical noise is a result of incomplete

convergence of iterative processes, round-off errors or the discrete representation of continuous

physical phenomena[6]. In RSM, the errors are assumed to be random.

The application of RSM to design optimization is aimed at reducing the cost of expensive

analysis methods (e.g. finite element method or CFD analysis) and their associated numerical noise.

The problem can be approximated with smooth functions that improve the convergence of the

optimization process because they reduce the effects of noise and they allow for the use of

derivative-based algorithms. Venter et al. (1996) have discussed the advantages of using RSM for

design optimization applications.

2.2. METHODOLOGY ADOPTED FOR THE PROPOSED DESIGN

1. To design the experiment using Design of Experiment techniques.

2. To obtain a combination of the optimal levels of the parameters in order to minimize surface

roughness with the application of response surface method (RSM).

EN 11(Fig. 10) was chosen to be the specimen material in the proposed work in order to

study the effect of four different parameters (Depth of cut, feed, spindle speed & different coolants)

on the Surface Roughness of the finished specimens using L18 orthogonal design. Therefore the

milling operations and measurements of surface roughness have been done 18 times on the work

pieces for each of the following cases. The work piece were machined by HSS cutting tool wet the

cutting conditions respectively.



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Table 1: Material Composition

Material Carbon (%) Nickel (%) Chromium (%) Molybdenum (%)

EN11 0.4 1.5 1.0 0.23

The Rockwell hardness number was 84 HRC for the EN11 work piece material.

EN11 is a high quality, high tensile, alloy steel. It combines high tensile strength, shock

resistance, good ductility and resistance to wear. EN11 is available from stock in round bar, flat bar

and plate.

EN11 is most suitable for the manufacture of parts such as roller bearing components such as

brake, cylindrical, conical & needle rollers, producing components with enhanced wear resistance.

EN11 is capable of retaining good impact values at low temperatures; hence it is frequently specified

for harsh offshore applications such as hydraulic bolt tensioners and ship borne mechanical handling

equipment. EN11 is a high carbon alloy which is widely used in roller component such as brake,

cylindrical, conical & needle rollers due to their exception thermal resistance and ability to retain

mechanical properties at elevated service temperatures over 1000 °C. However a high carbon alloy is

cut material due to their high degree of hardening and compressive strength and abrasion resistance.

The difficulty of machining EN11 results in to shorter tool life and severe surface abuse to machined

surface.

The Initial dimensions of the specimen for Milling Operation:

Length (mm) = 11±0.5

Breath (mm) = 2±0.5

Height (mm) = 2±0.5

In this experiment four different control factors have been taken into consideration to find out their

influence on surface roughness. All the four parameters are at three levels each. Values of variables

at different level for Milling Operation is as shown in the Table 2.

Table 2: Factors at different levels for Milling Operation

Factors Level 1 Level 2 Level 3

Depth of cut (A) D1 D2 D3

Feed (B) F1 F2 F3

Spindle Speed (C) S1 S2 S3

Coolant s(D) C1 C2

The degree of freedom (DF) of a three level parameter is 2 (number of levels-1) and two level

parameter is 1. The minimum required degree of freedom in the experiment is the sum of all factors.

Table 3: Degrees of Freedom

Factors A B C D Total

Degree of Freedom 2 2 2 1 7

The selection of which orthogonal array to use depends upon:

i. The number of factors.

ii. The number of levels for the factors of interest.



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Total DF for this experiment is 7 as shown in Table 3. As the degree of freedom required for

the experiment is 7 so the orthogonal array that is to be selected should have degree of freedom

higher than 7. The most suitable orthogonal array that can be used for this experiment is L18.

In this experiment, the assignment of factors was carried out using MINITAB 17 Software.

The standard L18 orthogonal array Table 4 as suggested by MINITAB using Taguchi for the

particular experiment are listed in Table 7.

Table 4: Standard L18 Orthogonal Array

Experiment

No.

Depth of Cut

A

Feed rate

B

Spindle Speed

C

Coolant

D

1. 1 1 1 C1

2. 1 2 2 C1

3. 1 3 3 C1

4. 2 1 1 C1

5. 2 2 2 C1

6. 2 3 3 C1

7. 3 1 1 C1

8. 3 2 2 C1

9. 3 3 3 C1

10 1 1 1 C2

11 1 2 2 C2

12 1 3 3 C2

13 2 1 1 C2

14 2 2 2 C2

15 2 3 3 C2

16 3 1 1 C2

17 3 2 2 C2

18 3 3 3 C2

Numerous investigators have conducted experiments to determine the effect of parameters

such as feed rate, spindle speed, depth of cut, Coolant on surface roughness in milling operation.

The values of the input process parameters for the Milling Operation Table 5 are as under:



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Table 5: Details of the Milling Operation

Using the L18 orthogonal array the trial runs have been the conducted on Milling Machine

for milling operations.

Table 6: List of Hardware

S.No. Item Specifications

1.

Milling machine

Size – 165 cm.

Motor -Three Phase motor.

It is shown in Fig. 1.

2.

Cutting Tool

Material of the cutting tool

Multipoint

HSS It is show in Fig. 2.

3.

Depth of Cut Measurement

Venire Caliper

It is show in Fig. 3.

4. Surface Roughness

Measurement Device

Model No. TR 110 P

Which are used to measure the job in the surface

roughness by the surface roughness tester

It is shown in Fig. 4.

Fig.1: Milling Machine Fig. 2: High Speed Steel(HSS) Cutting tool

Factors Level 1 Level 2 Level 3

Depth of cut (mm) 0.5 1.0 1.5

Feed Rate (mm/rev) 0.000025 0.000125 0.000375

Spindle Speed (rpm) 250 330 510

Coolant C1 C2



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Fig. 3: Venire Calipers Fig. 4: Roughness tester machine

2.2.1 Surface Roughness Terminology

Ra- Arithmetic means value of the deviation of the profile within sampling length

Rz- The maximum height of irregularities is the distance b/w maximum depth of the profile

peaks and profile valley within of sampling length

Rq- Square root of the arithmetic mean of the square of profile deviation (Yi) from mean within

sampling length.

Rt- Total peak-to-valley height .It is the sum of the height of highest peak and the depth of

deepest valley over the evaluation length.

The work piece can be safely turned in the three jaw chuck without supporting the free end work.

Fig. 5: Work Piece Mounted On Vice during Milling Operation

The work pieces were fixed in accordance with the experimental design, and each measured

for surface roughness around the part. Surface roughness was measured with the work piece fixture

and the measurements were taken across the lay. The total length of the work piece (44 mm) was

divided into 2 parts and the surface roughness measurements were taken of each 22 mm around each

work piece.



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The factors (Depth of cut, Feed rate and Spindle Speed, Different Coolants) were varied at

three levels for both milling operations. The measured response was impact surface roughness.

Analysis of the results was carried out analytically as well as graphically. All the statistical

calculations and plots were generated by MINITAB 17 software.

ANOVA plots of the experimental data have been created to calculate the significance of

each factor for each response. Often, researchers choose 90%, 95%, or 99% Confidence Levels; but

since most of the researchers have chosen 95% Confidence Level, so for this research work also 95%

Confidence Level has been chosen. Thus ᾱ = 0.05 was selected for all statistical calculations. The

response surface method uses the Signal-to-Noise ratio (S/N) to express the scatter around a target

value. A high value of S/N implies that the signal is much higher than the random effects of the noise

factors.

Table 7: Results of Experimental Trial Runs for Milling Operation

The response variables measured were surface roughness, surface roughness tester TR 110P is

used to measure the surface roughness for end milling operation. The single generated value is

measure after working. Surface roughness tester TR 110P is used to measure average surface

roughness.

The experimental come out result for surface roughness (Ra) are given in the Table 7. Values

of Ra are desirable. Thus the data sequences have the smaller-the-better characteristic, the “smaller-

the-best” methodology by using MINITABE 17 to find the result.

Experiment

No.

Depth of Cut

A

Feed Rate

B

Spindle

Speed C

Coolant

D Ra

1. 0.5 0.000125 250 C1 0.67

2. 0.5 0.00025 330 C1 0.2

3. 0.5 0.000375 250 C1 0.41

4. 1 0.000125 330 C1 0.46

5. 1 0.00025 510 C1 0.52

6. 1 0.000375 250 C1 0.37

7. 1.5 0.000125 330 C1 0.53

8. 1.5 0.00025 510 C1 0.5

9. 1.5 0.000375 250 C1 0.47

10 0.5 0.000125 330 c2 0.96

11 0.5 0.00025 510 c2 0.28

12 0.5 0.000125 250 c2 0.35

13 1 0.00025 330 c2 0.72

14 1 0.000375 510 c2 0.73

15 1 0.000125 250 c2 0.33

16 1.5 0.00025 330 c2 0.75

17 1.5 0.000375 510 c2 0.34

18 1.5 0.00025 250 c2 0.39



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Table 8: Response Table for Signal to Noise Ratios (Smaller is better)

Level Coolant Depth of Cut

(mm)

Feed Rate (mm/rev) Spindle Speed (rpm)

1 7.151 7.622 5.807 7.621

2 6.199 6.046 7.181 5.324

3 6.357 7.008 6.972

Delta 0.951 1.576 1.374 2.296

Rank 4 2 3 1

Table 9: Response Table for Means

Level Coolant Depth of Cut

(mm)

Feed Rate (mm/rev) Spindle Speed (rpm)

1 0.4589 0.4783 0.5500 0.4271

2 0.5389 0.5217 0.4800 0.6033

3 0.4967 0.4640 0.4740

Delta 0.0800 0.0433 0.0860 0.1762

Rank 3 4 2 1

Table 10: Analysis of Variance

Source DF Adj SS Adj MS F-

Value

P-Value

Model 9 0.174778 0.019420 0.3 0.945

Linear 3 0.017613 0.005871 0.10 0.960

Depth of Cut (mm) 1 0.003922 0.003922 0.06 0.806

Feed Rate (mm/rev) 1 0.001090 0.001090 0.02 0.897

Speed (rpm) 1 0.011150 0.011150 0.18 0.680

Square 3 0.123873 0.041291 0.68 0.589

Depth of Cut (mm)Depth of Cut (mm) 1 0.008579 0.008579 0.14 0.717

Feed Rate (mm/rev)Feed Rate (mm/rev) 1 0.010444 0.010444 0.17 0.689

Speed (rpm)*Speed (rpm) 1 0.106344 0.106344 1.75 0.222

2-Way Interaction 3 0.040513 0.013504 0.22 0.878

Depth of Cut (mm)*Feed Rate (mm/rev) 1 0.038733 0.038733 0.64 0.448

Depth of Cut (mm)*Speed (rpm) 1 0.000926 0.000926 0.02 0.905

Feed Rate (mm/rev)*Speed (rpm) 1 0.001428 0.001428 0.02 0.882

Error 8 0.485800 0.060725

Lack-of-Fit 7 0.434600 0.062086 1.2 0.606

Pure Error 1 0.051200 0.051200

Total 17 0.660578



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Fig. 6: Versur order between Residual and observation

Fig. 7: Histogram graph between frequency and Residual (response surface roughness)

In Fig. 6-7, the signal to noise ratio select for the current work was “smaller to better’’

According to Fig. 6, at the first level of depth of cut (0.5), first level of feed rate (0.000375)

mm/rev, first spindle speed (250 rpm) and first level of different Coolants (C1 type) respectively.

The surface roughness on the machined surface was found to be minimum. At Main effects versus

order between Residual and observation.



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Fig. 8: Versus Fits between Residual and Fitted value

Fig. 9: Normal Probability plot

According to Fig. 8 and Fig. 9, the Fits graph and Normal probability plot show the affect on

surface roughness as the result of the analysis. Response Surface method has been successfully used

to show the affect of the various parameters on the surface roughness and probability graph confirms

the same. This comparative study utilized an efficient method for determining the optimum milling

operation parameters in the four different cases for surface finish under varying noise conditions,

through the use of the Response process. Conclusions can be summed up with the following:



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i. The use of a standard L18 orthogonal array, with four control parameters required 2 work

pieces to conduct the experimental portion in each case.

ii. In milling of EN11 (0.4% C) by High Speed Steel tool, the cutting the combination obtained

for the optimal levels of the parameters was spindle speed (250 rpm) followed by feed

(0.000375 mm/rev) and depth of cut (0.5 mm) and Different Coolant.

Fig. 10: EN11 Specimen Fig. 11: Milling operation on EN11 Specimen

CONCLUSION

The present work has successfully demonstrated the application of Response surface method

for multi objective optimization of process parameters in end milling EN11 metal based alloy. The

conclusions can be drawn from the present work are as follows

i. The highest response surface result 0.73 was observed for the experimental Process, shown in

experiment result (Table 7).

ii. The order of importance for the controllable factors to the minimum force, in sequence, is the

spindle speed, depth of cut, feed rate and different Coolant; the order to minimum surface

roughness, in sequence, is the spindle speed, feed rate, depth of cut and different coolants.

iii. However, it is observed through ANOVA that the spindle speed is the most influential

control factor among the four end milling process parameters investigated in the present

work, when minimization of cutting forces, minimization of surface roughness are

simultaneously considered.

In this research work, the material used is EN11 with 0.4% carbon. The experimentation can

also be done for other materials having more hardness to see the effect of parameters on

Surface Roughness. In each case interaction of the different levels of the factors can be

included and study can be extended. In DOE the number of trials can be repeated with the

same combinations of factors and their interactions to obtain more than one response

(Surface Roughness).

3. ACKNOWLEDGEMENTS

Student’s Workshop, Department of Mechanical Engineering, Shepherd School of

Engineering and Technology, SHIATS, Allahabad. New Metal Testing Laboratory, Allahabad.



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