1. International Journal of Advanced JOURNAL OF ADVANCED RESEARCH IN0976 –
INTERNATIONAL Research in Engineering and Technology (IJARET), ISSN
6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December(IJARET)
ENGINEERING AND TECHNOLOGY (2012), © IAEME
ISSN 0976 - 6480 (Print) IJARET
ISSN 0976 - 6499 (Online)
Volume 3, Issue 2, July-December (2012), pp. 104-110
© IAEME: www.iaeme.com/ijaret.html
©IAEME
Journal Impact Factor (2012): 2.7078 (Calculated by GISI)
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OPTIMIZATION OF CUTTING PARAMETERS IN DRY TURNING
OPERATION OF MILD STEEL
RAHUL DAVIS 1*
1*
Assistant Professor, Department of Mechanical Engineering and Applied
Mechanics, SSET, SHIATS, Allahabad -211007, Uttar Pradesh, India
E-mail: rahuldavis2012@gmail.com
MOHAMED ALAZHARI 2
2
Assistant Professor, Department of Mechanical Engineering
Aljabal Algarby University
Hai Alandolas, Main Street,
Tripoli, Libya
E-mail: tobzal@yahoo.com
ABSTRACT
The quality of machined surface is characterized by the accuracy of its manufacture with
respect to the dimensions specified by the designer. Therefore it becomes necessary to get the
required surface quality in safe zone to have the choice of optimized cutting factors. In the
proposed research work the cutting parameters (depth of cut, feed rate, spindle speed) have
been optimized in dry turning of mild steel of (0.21% C) in turning operations on mild steel
by high speed steel cutting tool in dry condition and as a result of that the combination of the
optimal levels of the factors was obtained to get the lowest surface roughness. The Analysis
of Variance (ANOVA) and Signal-to-Noise ratio were used to study the performance
characteristics in turning operation. The results of the analysis show that depth of cut was the
only parameter found to be significant. Results obtained by Taguchi method match closely
with ANOVA and depth of cut is most influencing parameter. The analysis also shows that
the predicted values and calculated values are very close, that clearly indicates that the
developed model can be used to predict the surface roughness in the turning operation of mild
steel.
Keywords: Mild steel, Dry turning, Surface Roughness, Taguchi Method
1. INTRODUCTION
Product designers constantly strive to design machinery that can run faster, last longer, and
operate more precisely than ever. Modern development of high speed machines has resulted
in higher loading and increased speeds of moving parts. Bearings, seals, shafts, machine
ways, and gears, for example must be accurate - both dimensionally and geometrically.
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2. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME
Unfortunately, most manufacturing processes produce parts with surfaces that are either
unsatisfactory from the standpoint of geometrical perfection or quality of surface texture.
This primer begins by explaining how industry controls and measures the precise degree of
smoothness and roughness of a finished surface.1
Mild steel has a relatively low tensile strength, but it is cheap and malleable, surface
hardness can be increased through carburizing. Carbon content makes mild steel malleable
and ductile, but it cannot be hardened by heat treatment2. Since Turning is the primary
operation in most of the production process in the industry, surface finish of turned
components has greater influence on the quality of the product3. Surface finish in turning has
been found to be influenced in varying amounts by a number of factors such as feed rate,
work hardness, unstable built up edge, speed, depth of cut, cutting time, use of cutting fluids
etc4. There are three primary input control parameters in the basic turning operations. They
are feed, spindle speed and depth of cut. Feed is the rate at which the tool advances along its
cutting path. Speed always refers to the spindle and the work piece. Depth of cut is the
thickness of the material that is removed by one pass of the cutting tool over the workpiece5.
2. MATERIALS AND METHODS
The present research work reflects the usage of L27 Taguchi orthogonal design6 as the
study the effect of three different parameters (depth of cut, feed & spindle speed) on the
surface roughness of the specimens of mild steel was aimed after turning operations were
done 27 times in the Students Workshop in the Department of Mechanical Engineering,
Shepherd School of Engineering and Technology, SHIATS, Allahabad (U.P.), India,
followed by measurements of surface roughness around the part with the help of workpiece
fixture and the measurements of surface roughness were taken across the lay, while the setup
was a three-jaw chuck in Sparko Engineering Workshop, Allahabad (U.P.) India. The total
length of the workpiece (152.4 mm) was divided into 6 equal parts and the surface roughness
measurements were taken of each 25.4 mm around each workpiece.
The turning operations were performed by high speed steel cutting tool in dry cutting
condition.
Mild steel with carbon (0.21%), manganese (0.64 %) was selected as the specimen material.
The values of the three input control parameters for the Turning Operation are as under:
Table: 2.1 Details of the Turning Operation
Factors Level 1 Level 2 Level 3
Depth of cut (mm) 0.5 1.0 1.5
Feed Rate (mm/rev) 0.002 0.011 0.020
Spindle Speed (rpm) 14.91 25.12 40.03
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3. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME
Table 2.2: Results of Experimental Trial Runs for Turning Operation
Experiment Depth Feed Spindle Speed Surface SN Ratio
No. of Cut Rate (rpm) Roughness
(mm) (mm/rev) (µm)
1 0.5 0.002 14.91 10.040 -20.0347
2 0.5 0.002 25.12 3.700 -11.3640
3 0.5 0.002 40.03 16.930 -24.5731
4 0.5 0.011 14.91 9.330 -19.3976
5 0.5 0.011 25.12 1.910 -5.6207
6 0.5 0.011 40.03 11.010 -20.8357
7 0.5 0.020 14.91 14.590 -23.2811
8 0.5 0.020 25.12 4.020 -12.0845
9 0.5 0.020 40.03 1.880 -5.4832
10 1.0 0.002 14.91 31.250 -29.8970
11 1.0 0.002 25.12 26.750 -28.5465
12 1.0 0.002 40.03 43.370 -32.7438
13 1.0 0.011 14.91 30.710 -29.7456
14 1.0 0.011 25.12 15.610 -23.8681
15 1.0 0.011 40.03 29.620 -29.4317
16 1.0 0.020 14.91 35.620 -31.0339
17 1.0 0.020 25.12 45.331 -33.1279
18 1.0 0.020 40.03 27.040 -28.6401
19 1.5 0.002 14.91 21.250 -26.5472
20 1.5 0.002 25.12 63.040 -35.9923
21 1.5 0.002 40.03 78.120 -37.8552
22 1.5 0.011 14.91 71.480 -37.0837
23 1.5 0.011 25.12 54.780 -34.7724
24 1.5 0.011 40.03 79.180 -37.9723
25 1.5 0.020 14.91 49.570 -33.9044
26 1.5 0.020 25.12 45.950 -33.2457
27 1.5 0.020 40.03 64.250 -36.1575
In the present experimental work, the assignment of factors was carried out using
MINITAB-15 Software. The trial runs specified in L27 orthogonal array were conducted on
Lathe Machine for turning operations.
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4. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME
Table 2.3: ANOVA Table for Means
Parameter DF SS MS F P
Depth of Cut 2 11478.63 5739.32 37.96 0.000
Feed 2 13.30 6.7 0.04 0.957
Spindle Speed 2 530.9 265.4 1.76 0.198
Error 20 3023.9 151.2
Total 26 15046.7
Table 2.4: ANOVA Table for Signal-to-Noise Ratios for the Response Data
Parameter DF SS MS F P
Depth of Cut 2 1734.04 867.02 34.71 0.000
Feed 2 7.16 3.58 0.14 0.867
Spindle Speed 2 84.48 42.24 1.69 0.210
Error 20 499.6 24.98
Total 26 2325.28
Table 2.5: Response Table for Average Surface Roughness
Depth of Cut Feed Rate
Level Spindle Speed (C)
(A) (B)
1 8.157 32.717 30.427
2 31.700 33.737 29.010
3 58.624 32.028 39.044
Delta (∆max-min) 50.468 1.709 10.034
Rank 1 3 2
From Table 2.5, Optimal Parameters for Turning Operation were A1, B3 and C2.
Table 2.5 shows the SN Ratio (SNR) of the surface roughness for each level of the factors.
The difference of SNR between level 1 and 3 indicates that Depth of Cut contributes the
highest effect (∆max-min = 50.468) on the surface roughness followed by Feed Rate (∆max-min =
1.709) and Spindle Speed (∆max-min = 10.034).
Therefore the Predicted optimum value of Surface Roughness
βp (Surface Roughness)
= 32.82 + [8.157-32.82) ]+ [32.028-32.82)] + [29.010-32.82)] = 3.555
Table 2.6: Response Table for Signal-to-Noise ratio of Surface Roughness
Depth of Cut Feed
Level Spindle Speed (C)
(A) (B)
1 -15.85 -27.51 -27.88
2 -29.67 -26.53 -24.29
3 -34.84 -26.33 -28.19
Delta (∆max-min) 18.98 1.18 3.90
Rank 1 3 2
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5. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
6480(Print), ISSN 0976 – 6499(Online) Volume 3, Number 2, July-December (2012), © IAEME
From Table 2.6, Optimal Parameters for Turning Operation were A1, B3 and C2.
Table 2.6 shows the SNR of the surface roughness for each level of the factors. The
difference of SNR between level 1 and 3 indicates that Depth of Cut contributes the highest
effect (∆max-min = 18.98) on the surface roughness followed by Feed Rate (∆max-min = 1.18) and
Spindle Speed (∆max-min = 3.90).
Therefore the Predicted optimum value of SN Ratio for Turning Operation.
ηp (Surface Roughness)
= -26.78 + [-15.85-(-26.78)] + [-26.33-(-26.78)] + [-24.29-(-26.78)]
= -12.91
3. RESULTS AND DISCUSSION
Comparing the F values of ANOVA Table 2.3 and 2.4 of Surface Roughness with the
suitable F values of the Factors (F0.05;2;8 = 4.46) and their Interactions (F0.05;4;8 = 3.84)
respectively for 95% confidence level respectively show that the Depth of Cut (F = 37.96 and
F = 34.71) and was the only significant factor and other two factors Feed (F = 0.04 and F =
0.14) and Spindle Speed (F = 1.76 and F = 1.69) are the factors found to be insignificant.
Main Effects Plot for Means
Data Means
Depth of Cut (mm) Feed Rate (mm/rev)
60
40
Mean of Means
20
0.5 1.0 1.5 0.002 0.011 0.020
Spindle Speed (rpm)
60
40
20
14.91 25.12 40.03
Figure 3.1: Main Effects Plot for Means
Main Effects Plot for Means: Fig 3.1 and Fig 3.5 show the effect of the each level of the
three parameters on surface roughness for the mean values of measured surface roughness at
each level for all the 27 trial runs.
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6. International Journal of Advanced Research in Engineering and Technology (IJARET), ISSN 0976 –
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Main Effects Plot for SN ratios
Data Means
Depth of Cut (mm) Feed Rate (mm/rev)
-15
-20
-25
Mean of SN ratios
-30
-35
0.5 1.0 1.5 0.002 0.011 0.020
Spindle Speed (rpm)
-15
-20
-25
-30
-35
14.91 25.12 40.03
Signal-to-noise: Smaller is better
Figure 3.5: Main Effects Plot for SN ratio
From Table 2.5, Table 2.6 and Fig 3.1 and Fig 3.5 optimal levels of the parameters for
minimum Surface Roughness are first level of Depth of Cut (A1) i.e 0.5 mm, third level
of Feed (B3) i.e 0.020 and first level of Spindle Speed i.e 25.12 rpm (C2). So the
combination of the factors found in 8th trial in Table 2.2 gives the optimum result.
Table 3.1: Results of the Confirmation Tests of the optimal levels of the factors
Specimen Trial Depth of Feed Rate Spindle Speed Surface
Run Cut (mm) (mm-rev) (rpm) Roughness
(µm)
1 8 0.5 3 14.03 3.491
2 8 0.5 3 14.03 3.443
4. SUMMARY AND CONCLUSIONS
• Optimization of the surface roughness was done using taguchi method and
predictive equation was obtained. A confirmation test was then performed which
depicted that the selected parameters and predictive equation were accurate to within
the limits of the measurement instrument.
• The obtained results can be recommended to get the lowest surface roughness for
further research works.In this research work, the material used is mild steel with
0.21% carbon content. The experimentation can also be done for other materials
having more hardness to see the effect of parameters on Surface Roughness.
• Interactions of the different levels of the factors can be included to see the effect.
5. REFERENCES
1. http://www.mfg.mtu.edu/cyberman/quality/sfinish/index.html
2. http://en.wikipedia.org/wiki/Surface_finish
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3. http://en.wikipedia.org/wiki/Carbon_steel
4. Diwakar Reddy.V, Krishnaiah.G. et al (2011), ANN Based Prediction of Surface Roughness in
Turning, International Conference on Trends in Mechanical and Industrial Engineering
(ICTMIE'2011) Bangkok
5. Mahapatra, S.S. et al (2006). Parametric Analysis and Optimization of Cutting Parameters for
Turning Operations based on Taguchi Method, Proceedings of the International Conference on
Global Manufacturing and Innovation - July 27-29
6. Raghuwanshi, B. S. (2009). A course in Workshop Technology Vol.II (Machine Tools), Dhanpat
Rai & Company Pvt. Ltd.
7. Ross, Philip J. (2005). Taguchi Techniques for Quality Engineering, Tata McGraw-Hill Publishing
Company Ltd.
8. Suhail, Adeel H. et al (2010). Optimization of Cutting Parameters Based on Surface Roughness and
Assistance of Workpiece Surface Temperature in Turning Process, American J. of Engineering and
Applied Sciences 3 (1): 102-108.
9. Van Luttervelt, C. A. et al (1998). Present situation and future trends in modelling of machining
operations, CIRP Ann.
10. Kirby, Daniel (2010). Optimizing the Turning Process toward an Ideal Surface Roughness Target.
11. Selvaraj, D. Philip et al (2010). optimization of surface roughness of aisi 304 austenitic stainless
steel in dry turning operation using Taguchi design method Journal of Engineering Science and
Technology,Vol. 5, no. 3 293 – 301, © school of engineering, Taylor’s university college.
12. Kirby, E. Daniel (2006). Optimizing surface finish in a turning operation using the Taguchi
parameter design method Int J Adv Manuf Technol: 1021–1029.
13. Tzou, Guey-Jiuh and Chen Ding-Yeng (2006). Application of Taguchi method in the
optimization of cutting parameters for turning operations. Department of Mechanical Engineering,
Lunghwa University of Science and Technology, Taiwan, (R.O.C.).
14. Singh, Hari (2008). Optimizing Tool Life of Carbide Inserts for Turned Parts using Taguchi’s
design of Experiments Approach, Proceedings of the International MultiConference of Engineers
and Computer Scientists Vol II IMECS 2008, 19-21 March, Hong Kong.
15. Hasegawa. M, et al (1976). Surface roughness model for turning, Tribology International
December 285-289.
16. Kandananond, Karin (2009). Characterization of FDB Sleeve Surface Roughness Using the
Taguchi Approach, European Journal of Scientific Research ISSN 1450-216X Vol.33 No.2 ,
pp.330-337 © EuroJournals Publishing, Inc.
17. Phadke, Madhav. S. (1989). Quality Engineering using Robust Design. Prentice Hall, Eaglewood
Cliffs, New Jersey.
18. Aruna, M. (2010). Wear Analysis of Ceramic Cutting Tools in Finish Turning of Inconel 718.
International Journal of Engineering Science and Technology Vol. 2(9), 2010, 4253-4262.
19. Arbizu, Puertas. I. and Luis Prez, C.J. (2003). Surface roughness prediction by factorial design
of experiments in turning processes, Journal of Materials Processing Technology, 143- 144 390-396
20. Palanikumar, K. et al (2006). Assessment of factors influencing surface roughness on the
machining of glass –reinforced polymer composites, Journal of Materials and Design.
21. Sundaram, R.M., and Lambert, B.K. (1981). Mathematical models to predict surface finish in
fine turning of steel, Part II, International Journal of Production Research.
22. Thamizhmanii, S., et al (2006). Analyses of roughness, forces and wear in turning gray cast iron,
Journal of achievement in Materials and Manufacturing Engineering, 17.
23. Thamizhmanii, S., et al (2006). Analyses of surface roughness by turning process using Taguchi
method, journal of Achievements in Materials and Manufacturing Engineering. Received
03.11.2006; accepted in revised form 15.11.2006.
24. Yang, W.H. and Y.S. Tarng (1998), Design optimization of cutting parameters for turning
operations based on the Taguchi method. Journal of Materials Processing Technology.
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