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 Total Downloads : 641
 Authors : Girish Tilak Shet, Dr. N. Lashmana Swamy, Dr. H. Somashekar
 Paper ID : IJERTV3IS10595
 Volume & Issue : Volume 03, Issue 01 (January 2014)
 Published (First Online): 23012014
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
Optimization of Surface Roughness Parameters in Turning EN1A Steel on A CNC Lathe Without Coolant
Girish Tilak Shet
M.E., University Visvesvaraya College of Engineering, Bangalore 560001
Dr. N. Lashmana Swamy
Professor, Department of Mechanical Engineering University Visvesvaraya College of Engineering, Bangalore 560001,
Dr. H. Somashekar
Assistant Professor, Department of Mechanical Engineering Dr. Ambedkar Institute of Technology, Bangalore560039.
Abstract
This paper presents the optimization of surface roughness parameters in turning EN1A steel on a CNC lathe. The optimization of machining processes is essential for the achievement of high responsiveness of production, which provides a preliminary basis for survival in todays dynamic market conditions. The quantitative determination of Surface Roughness is of vital importance in the field of precision engineering. Machinability can be based on the measure of Surface Roughness. Surface Roughness depends on the factors such as Speed, Feed and Depth of Cut. In this work, the Taguchi methods, a powerful statistical tool to design of experiments for quality, is used to find the optimal cutting parameters for turning operations. Analysis of Variance has been used to determine the influencing parameters on the output responses. Using Taguchi technique, we have reduced number of experiments from 27 to 9 there by the total cost of the project is reduced by 66.66%. The results obtained are encouraging and the concluding remarks are helpful for the manufacturing industries.

Introduction
The machinability of metal is defined as the ease with which a given material may be machined with a specific cutting tool. In other words the most machinable metal is one which will permit the fastest removal of the largest amount of material per cut of a
tool with satisfactory finish. The operational characteristics of a cutting tool are generally described by its machinability which has 3 main aspects, tool life, surface finish and power required to cut. The quantitative determination of Surface Roughness is of vital importance in the field of precision engineering. Machinability can be based on the measure of Surface Roughness. Surface Roughness depends on the factors such as Speed, Feed and Depth of Cut. Other factors include cutting tool material, cutting tool geometry, machine condition, work piece material, cutting tool clamping and depend on operation carried out. The presence of coolant affects the Surface Roughness. Therefore an attempt has been made to conduct experimental investigation to optimize the Surface Roughness parameters in turning of EN1A steel on CNC lathe.
In this work, the Taguchi methods, a powerful statistical tool to design of experiments for quality, is used to find the optimal cutting parameters for turning operations. In the present study, experiment has been conducted using 9 pieces of EN1A steel to measure Surface Roughness without coolant. Measurement has been done using Stylus Type Profilometer. In which an attempt has been made to optimize machining parameters i.e., speed, feed and Depth of Cut at three levels i.e., minimum, average and maximum values to obtain better surface finish using Taguchi technique without coolant. Also an attempt has been made to optimize machining parameters i.e., speed, feed and Depth of Cut at three levels i.e., minimum, average and maximum values
to obtain better Material Removal Rate without coolant. An attempt has been made to develop regression models for Surface Roughness. In this study Analysis of Variance has been used to determine the influencing parameters on the output responses. Using Taguchi technique, we have reduced number of experiments from 27 to 9 there by the total cost of the project is reduced by 66.66%. Using Taguchi technique, we have reduced time required, man power, material etc. In this study output responses such as Surface Roughness, Material Removal Rate and machining time have been measured. In this study comparison between Actual and Theoretical values of Material Removal Rate has been made.
Traditionally, the selection of cutting conditions for metal cutting is left to the machine operator. In such cases, the experience of the operator plays a major role, but even for a skilled operator it is very difficult to attain the optimum values each time. Machining parameters in metal turning are Speed, feed rate and Depth of Cut. The setting of these parameters determines the quality characteristics of turned parts.
The Finite Element Analysis results are obtained using continuum membrane element.
Turning Operation:
Turning is the removal of material from the outer diameter of a rotating cylindrical work piece by means of single point cutting tool which is held stationary on the tool post and moved parallel to the work piece axis with suitable Speed, Feed and Depth of Cut, Turning is used to produce cylindrical surface on the work piece. In turning the diameter of the work piece, usually to a specified dimension and the length of the work piece remains same. Figure 1. Shows the turning operation.
Figure 1: Schematic Representation of Turning Operation
Where,
D1= initial diameter of the work piece before machining D2= final
diameter of the work piece after machining L = machining length of the work piece
The three primary factors in any basic turning operation are speed, feed, and Depth of Cut.
Speed(N):
Speed always refers to the spindle and the work piece. When it is stated in revolutions per minute (rpm) it tells their rotating speed. But the important feature for a particular turning operation is the surface speed, or the speed at which the work piece material is rotating fast against the stationary cutting tool. It is simply the product of the rotating speed times the circumference of the work piece before the cut is started. Every different diameter on a work piece will have a different Speed, even though the rotating speed remains the same.
Feed:
Feed always refers to the cutting tool, and it is the rate at which the tool advances along its cutting path. On most powerfed lathes, the feed rate is directly related to the spindle speed and is expressed in mm (of tool advance) per revolution (of the spindle), or mm/rev.
Depth of Cut:
Depth of Cut is practically self explanatory. It is the thickness of the layer being removed (in a single pass) from the work piece or the distance from the uncut surface of the work to the cut surface, expressed in mm. It is important to note, though, that the diameter of the work piece is reduced by two
times the Depth of Cut because this layer is being removed from both sides of the work.

Tools and equipment:
Work material:
The work material used for this experimentation is EN 1A steel. This material is widely used in the automobile industry. Chemical composition and Mechanical properties of EN 1A steel is shown in Table 1 and Table 2 respectively.
Table 1: Chemical composition of EN 1A steel
C 
Mn 
Si 
P 
S 

Min 
0.07 
0.8 
0.10 
0.07 
0.2 
Max 
0.15 
1.2 
– 
– 
0.3 
Table 2: Mechanical Properties of EN 1A steel
Condition 
Tensile 
Yield <>MPa 
Elongation % 
Cold drawn 
400 
290 
79 
Turned & Polished 
370 
230 
18 
CUTTING TOOL INSERT USED:
For machining the above work material the following Uncoated Carbide Inserts was used:

TNMG 16 04 08
Three different tool inserts are used to take into account the effect of nose radius.
Cutting tools are often designed with inserts or replaceable tips (tipped tools). In these, the cutting edge consists of a separate piece of material, brazed, welded or clamped on to the tool body. Common materials for tips include Tungsten Carbide, Polycrystalline Diamond (PCD), and Cubic Boron Nitride (CBN).
Figure 2: Uncoated Carbide Insert
Insert Designation:
The details of cutting insert TNMG 16 04 08 is mentioned below.
T: Insert Shape= Triangle 600
N: Clearance Angle= 00 No rake
M: Medium Tolerance= d+/0.05 m+/0.08 s+/0.13 G: Insert Type (Pin / Top clamping double sided) 16: means length of each cutting edge is 16 mm
04: stands for nominal thickness of the insert is 4 mm
08: stands for nose radius is 0.8mm
Properties of Carbide Inserts:
They are stable and moderately expensive. It is offered in several "grades" containing different proportions of Tungsten Carbide and binder (usually Cobalt). High resistance to abrasion. High solubility in iron requires the additions of Tantalum Carbide and Niobium Carbide for Steel usage. Its main use is in turning tool bits although it is very common in milling cutters and saw blades. Hardness up to HRC

Sharp edges generally not recommended.
CNC Lathe:
CNC Lathe (ACE) which is used for machining thegiven material is shown below in the Figure 3.
Figure 3: CNC Lathe (ACE)
Profilometer:
Figure 4: Profilometer which is used to measure Surface Roughness
For this experimentation process the Profilometer being used is the Mitutoyo SJ201P which is shown in the Figure 4.
The measurement is done using this equipment; the selected parameter is Ra as it is the most popular and is commonly used in the industries. The roughness is measured in multiple points in the work pieces and the average value is selected for the experimental data.
Table 3: L9 Orthogonal Array with Observations without Coolant.
T
r i a l
N
o
T
Pa
urnin rame
g ter
Output Responses
A C
C
S
u rf a ce R
o u g h n es s, R
a (Âµ m
)
We ight of spe cim en Bef ore Tur nin g (gm
)
Wei ght of speci men Afte r Tur ning (gm)
M
a c hi ni n g T
i m e (a ct u al
),
t (s ec
)
Ma chi nin g Ti me (the ore tica l), t (sec
)
MR R(a)
(m m3/ min
)
MR
R(t
)
(m m3/ min
)
u t ti n g S
p e e d (
r p
B F
e e d (
m m
/ r e v
)
D
e p t h O
f C
u t (
m
m
m
)
)
1
1
0
0
0
0
. 1
0
. 2
3.
1
9
197
.30
1
182.
706
7
0
58
279
0.34
120
0
2
1
0
0
0
0
. 2
0
. 4
4.
1
9
197
.30
1
181.
298
9
9
72
747
4.44
750
0
3
1
0
0
0
0
. 3
0
. 6
4.
2
9
197
.30
1
178.
660
9
4
70
145
90.1
1
168
00
4
2
0
0
0
0
. 1
0
. 4
3.
6
7
197
.30
1
180.
792
8
5
62
789
0.48
740
0
5
2
0
0
0
0
. 2
0
. 6
3.
7
9
197
.30
1
178.
520
1
9
26
186
50
226
00
6
2
0
0
0
0
. 3
0
. 2
3.
8
5
197
.30
1
184.
191
1
4
21
119
30.3
5
118
00
7
3
0
0
0
0
. 1
0
. 6
2.
8
4
197
.30
1
177.
867
1
9
28
147
58.2
4
172
00
8
3
0
0
0
0
. 2
0
. 2
3.
7
1
197
.30
1
183.
532
1
3
22
117
76.7
8
128
00
9
3
0
0
0
0
. 3
0
. 4
3.
8
2
197
.30
1
181.
373
1
1
19
229
92.8
5
334
00

EXPERIMENTATION
Optimization of Machining Parameters (3 factors and 3 level analyses) and studies on Surface Roughness, MRR and Machining Time using TNMG 16 04 08 without Coolant in CNC lathe (ACE) using L9:
In this experiment the turning operation was done on the work piece i.e., EN 1A Steel (Length 100 mm and Diameter 20 mm) on a CNC lathe. TNMG 16 04 08 Insert was used for turning. 3 factors were selected i.e., Speed (rpm), Feed (mm/rev) and Depth of Cut (mm) at 3 levels i.e., (Minimum, Average and Maximum) and coolant was not used. Surface Roughness was measured using Profilometer (Talysurf) and the readings are tabulated in Table 3.
To find Minimum number of experiments to be conducted:
3) To calculate Machining Time (t) (theoretical) following formula is used
For 3 Factors and 3 Levels, the minimum number
of Experiments to be conducted is shown in the
t =
min
following Table 3.
Table 4: Factors, Levels and Degrees of Freedom
Factor Code
Factor
No of Levels
Degrees of Freedom
A
Speed
3
2
B
Feed
3
2
C
Depth of Cut
3
2
Total Degrees of freedom
6
Minimum number of Experiments
7
Taguchis standard L9 Orthogonal Array was used do conduct the experimentation. is mentioned below in Table 5.
The formulae used to find MRR (actual), MRR (theoretical) and Machining time theoretical are as follows

MRR (a) represents Actual Material Removal Rate in mm3/min
L= length of surface to be machined.
Table 5: Standard L9 Orthogonal Array
Trial No.
Speed (rpm)
Feed (mm/rev)
DEPTH OF CUT
(mm)
1
1
1
1
2
1
2
2
3
1
3
3
4
2
1
2
5
2
2
3
6
2
3
1
7
3
1
3
8
3
2
1
9
3
3
2
Table 5 shows the standard L9 orthogonal array which is used in the present study.
Work piece Material
EN 1A Steel
Lathe Used
CNC Lathe (ACE )
Inserts Used
Uncoated Carbide Insert
(KORLOY Make)
Insert Designation
TNMG 16 04 08 (ISO
Designation)
Tool holder
MTJNL 25 * 25 * H 16 (ISO
Designation)
Speed (rpm)
1000,2000,3000
Feed (mm/rev)
0.1,0.2,0.3
Depth of Cut (mm)
0.2,0.4,0.6
Environment
COOLANT OFF
Table 6: Experimental Conditions
MRR (a) =
.
mm3/min
Where,
Wi denotes initial weight of the specimen before machining in gm.
Wf denotes final weight of the specimen after machining in gm.
t denotes machining time in seconds.

MRR (t) represents Theoretical Material Removal Rate in mm3/min
MRR (t) = f * d* V* 1000 mm3/min
Here, f denotes feed in mm/rev, d denotes Depth of Cut in mm and V denotes Speed in m/min


RESULTS AND DISCUSSION

In this experiment turning operation was done on the work piece i.e., EN 1A Steel on a CNC lathe. Uncoated Carbide Insert was used for turning. 3 factors were selected i.e., Speed (rpm), Feed (mm/rev) and Depth of Cut (mm) at 3 levels and coolant was not used. Surface Roughness was measured using Profilometer (Talysurf) and the readings are tabulated in Table 4.
Studies Related to Surface Roughness without Coolant:
The following studies were conducted associated with Surface Roughness

Regression Model For Surface Roughness.

General Linear Model for Surface Roughness.

Analysis of Variance For Surface Roughness.

Response Table of Signal to Noise Ratios for Surface Roughness.

Graph showing the Main Effects plot for S/N ratios of Ra.

Regression Model For Surface Roughness:
Regression Equation is the relationship between dependent variable and one or more independent variables. Dependent variable is the Surface Roughness and independent variables are Speed, Feed and Depth of Cut.
Using MINITAB Software the Regression Model has been developed for the above Experiment.
The regression equation is
Surface Roughness (Âµm) = 3.33 0.000217 Speed
(rpm) + 3.77 Feed (mm/rev) + 0.142 Depth
of Cut (mm)
If the value of Speed (rpm), Feed (mm/rev) and Depth of Cut (mm) are known, using the above equation we can predict the corresponding value of Surface Roughness (Âµm).

General Linear Model for Surface Roughness:
Table 7: General Linear Model for Surface Roughness
Factor
Type
Levels
Values
Speed(rpm)
Fixed
3
1000,2000,3000
Feed(mm/rev)
Fixed
3
0.1,0.2,0.3
Depth of Cut (mm)
Fixed
3
0.2,0.4,0.6
General Linear Model for Surface Roughness is shown in Table 7. Input Parameters for this experiment are Speed, Feed and Depth of Cut at 3 levels and the values are shown in the above Table 7.

Analysis of Variance for Surface Roughness:
Table 8: Analysis of Variance for Surface Roughness
Sou rce
D F
Se g SS
Ad j SS
Ad j M
S
F
P
A N K
Contr ibutio
n
Spe ed (rp
m)
2
1.7
72
7
1.7
72
7
0.8
86
4
1.
8
5
0.
35
1
2
19.64
%
Fee d (m m/r
ev)
2
6.2
22
8
6.2
22
8
3.1
11
4
6.
5
0
0.
13
3
1
69.00
%
Dep th of Cut (m
m)
2
1.0
28
0
1.0
28
0
0.5
14
0
1.
0
7
0.
48
2
3
11.36
%
Err or
2
0.9
57
7
0.9
57
7
0.4
78
8
Tot al
8
9.9
81
2
9.
4
2
100%
For this experiment, Analysis of Variance was performed is shown in Table 8. to identify the influence of Machining parameters on the output Responses using MINITAB software. Input parameters considered were Speed, Feed and Depth of Cut. Output parameter was Surface Roughness.
Ranking is given based on the value of P (Smaller the value of P, Greater the influence of that parameter on the Output). For this experiment, the input parameters that are influencing the Output parameter (Surface Roughness) in their decreasing order are Feed, Speed, and Depth of Cut. According to the Table 8, Feed has the highest contribution of 69% followed by Speed 19.64% and Depth of Cut 11.36%.

Response Table of Signal to Noise Ratios for Surface Roughness:
Table 9: Response Table for Signal to Noise Ratios
Levels 
Speed (rpm) 
Feed (mm/rev) 
Depth of Cut (mm) 
1 
11.72 
10.15 
11.06 
2 
11.53 
11.80 
11.79 
3 
10.70 
12.00 
11.10 
Delta 
1.02 
1.85 
0.74 
Rank 
2 
1 
3 
Above Table 9. shows the Response for Signal to Noise Ratios of the given parameters. For this experiment, the input parameters that are influencing the Output parameter (Surface Roughness) in their decreasing order are Feed, Speed, and Depth of Cut. Response Table is used to cross check the ranking obtained in the Analysis of Variance.
4.0
3.8
3.6
3.4
3.2
Main Effects Plot (data means) for Means
Speed (rpm) Feed (mm/rev)
0.2
0.4
0.6
4.0
3.8
3.6
3.4
3.2
0.3
0.2
0.1
3000
2000
DOC (mm)
1000
Mean of Means
Figure 6: Mean values for Surface Roughness
Highest S/N ratio gives optimum machining parameter. Hence from Figure 5 and Figure 6 it can be observed that optimum values of machining parameters to get minimum Surface Roughness are Speed (3000 rpm), Feed (0.1mm/rev) and Depth of Cut (0.2mm).
Confirmation Test: Turning was conducted at optimum cutting parameters i.e., Speed 3000 rpm, feed 0.1mm/rev and Depth of Cut 0.2mm and found that Surface Roughness as 1.94 Âµm.
4.6 To Study the Comparison of Actual And Theoretical Values of MRR:
MRR (mm3/min)
40000
4.5 Graph showing the Main Effects Plot for S/N ratios of Ra:
Main Effects Plot (data means) for SN ratios
Speed (rpm) Feed (mm/rev)
20000
0
1 2 3 4 5 6 7 8 9
Mean of SN ratios
Trial No
MRR (a)
(mm3/min)
MRR (t)
10.0
10.5
11.0
11.5
12.0
(mm3/min)
10.0
10.5
11.0
11.5
12.0
1000
2000
DOC (mm)
3000
0.1
0.2
0.3
0.2
0.4
0.6
Signaltonoise: Smaller is better
Figure 5: S/N ratio values for Surface Roughness
Figure 7: shows the comparison of Actual and Theoretical values of MRR
From Figure 7 it can be seen that for all the trials of this experiment, Theoretical value of Material Removal Rate is more compared to Actual values of Material Removal Rate. Further it can be observed that Material Removal Rate is Maximum when the values of Speed, Feed and Depth of Cut are at maximum levels i.e., 3000 rpm, 0.3 mm/rev and 0.4 mm respectively. Also it can be observed that Material Removal Rate is Minimum when the values of Speed, Feed and Depth of Cut are at minimum levels i.e., 1000 rpm, 0.1 mm/rev and 0.2 mm respectively.
5. CONCLUSION

Regression Model has been developed for Surface Roughness without coolant relating Speed, Feed and Depth of Cut to predict the value of the surface roughness.

The Analysis of Variance was performed to identify the influence of Machining Input parameters considered were Speed, Feed and Depth of Cut on the output Responses Surface Roughness using MINITAB software. Based on the Analysis of Variance the input parameters that are influencing the Output parameter Surface Roughness in their decreasing order are Feed, Speed and Depth of Cut.

Feed has the highest contribution of 69% followed by Speed 19.64% and Depth of Cut 11.36%.

The optimum values of machining parameters to get Optimum Surface Roughness are Speed of 3000 rpm, Feed of 0.1mm/rev and Depth of Cut of 0.2mm. Surface Roughness is found that is
1.94 Âµm. And average Surface Roughness is found to be 3.70 Âµm.

The Material Removal Rate is Maximum i.e., 22992.85 mm3/min when the values of Speed, Feed and Depth of Cut are 3000 rpm, 0.3 mm/rev and 0.4mm respectively. And Machining Time is 11 sec i.e., Minimum at this level.

The Material Removal Rate is Minimum i.e., 2790.34 mm3/min when the values of Speed, Feed and Depth of Cut are 1000 rpm, 0.1 mm/rev and 0.2 mm respectively. And Machining Time is 70 sec i.e., 1.5 times the Average at this level.

The average Material Removal Rate is 12540.06 mm3/min.
6. REFERENCES

Gilbert W W (1950) Economics of machining. In Machining Theory and practice. Am. Soc. Met.476480

ArmaregoE J A, BrownR H (1969) The machining of metals (Englewood Cliffs, NJ: Prentice Hall) ASME 1952 Research committee on metal cutting data and bibliography. Manual on cutting of metals with single point tools 2nd edn.

Brewer R C, Rueda R (1963) A simplified approach to the optimum selection of machining parameters.Eng. Dig. 24(9): 133150

Brewer R C (1966) Parameter Selection Problem in Machining.
Ann. CIRP 14:11

Bhattacharya A, FariaGonzalez R, Inyong H
(1970) Regression analysis for predicting surface finish and its application in the determination of optimum machining conditions. Trans. Am. Soc. Mech. Eng. 92: 711

Walvekar A G, Lambert B K (1970) An application of geometric programming to machining variable selection. Int. J. Prod. Res. 8: 3

Sundaram R M (1978) An application of goal programming technique in metal cutting. Int.
J. Prod.Res. 16: 375382

Ermer D S, Kromordihardjo S (1981) Optimization of multipass turning with constraints. J. Eng. Ind.103: 462468

Hinduja S, Petty D J, Tester M, Barrow G (1985) Calculation of optimum cutting conitions for turning operations. Proc. Inst. Mech. Eng. 199(B2): 8192

Tsai P (1986) An optimization algorithm and economic analysis for a constrained machining model. PhD thesis, West Virginia University

Gopalakrishnan B, Khayyal F A (1991) Machine parameter selection for turning with constraints: An analytical approach based on geometric programming. Int. J. Prod. Res. 29: 18971908

Agapiou J S (1992) The optimization of machining operations based on a combined criterion, Part 1: The use of combined objectives in singlepass operations, Part 2: Multipass operations. J. Eng. Ind., Trans.
ASME 114: 500513

Prasad A V S R K, Rao P N, Rao U R K (1997) Optimal selection of machining parameters for turning operations in a CAPP system. Int. J. Prod. Res. 35: 14951522

Zhou Q., Hong G. S. and Rahman M., (1995), A New Tool Life Criterion For Tool Condition Monitoring Using a Neural Network, Engineering Application Artificial Intelligence, Volume 8, Number 5, pp. 579 588.

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. 348356.

Suresh P. V. S., Rao P. V. and Deshmukh S. G., (2002), A genetic algorithmic approach for optimization of surface roughness prediction
model, International Journal of Machine Tools and Manufacture, Volume 42, pp. 675680.

Lee S. S. and Chen J. C., (2003), Online surface roughness recognition system using artificial neural networks system in turning operations International Journal of Advanced Manufacturing Technology, Volume 22, pp. 498509.

Choudhury S. K. and Bartarya G., (2003), Role of temperature and surface finish in predicting tool wear using neural network and design of experiments, International Journal of Machine Tools and Manufacture, Volume 43, pp. 747753.

Chien W.T. and Tsai C.S., (2003), The investigation on the prediction of tool wear and the determination of optimum cutting conditions in machining 174PH stainless steel, Journal of Materials Processing Technology, Volume 140, pp.340345.

Bhattacharyya, A., Reprinted 2006, Metal Cutting: Theory and Practice, New Central Book Agency, Page No. 495 501. ISBN: 81 73812284.

DIN 4760: Form deviations; concepts; classification system. Deutsches Institute Fuer Normung, e.V., 1982.

Groover, Mikell P. (2007). Theory of Metal Machining, Fundamentals of Modern Manufacturing, 3rd ed, John Wiley & Sons, Inc.
ISBN 0471744859

Kaczmarek J., 1983 Principles of machining by cutting, abrasion and erosion. London: Peter Peregrinus.

Kalpakjian S. and Schmid Steven R. (2000), Manufacturing Engineering and Technology, 4th ed, Pearson Education Asia. ISBN 8178081571.

Manly, B. F. J., (1994), Multivariate Statistical Methods: A Primer, Chapman and Hall, London.

Peace, G., S., (1993), Taguchi Methods A HandsOn Approach, Addison Wesley Publishing Company. Massachusetts.

Rao, P. N. (2001). Manufacturing Technology Metal Cutting and Machine Tools, First reprint 2001, Tata McGrawHill. ISBN 007463843.