4-Mech - IJMPERD - OPTIMIZATION OF SURFACE - Rahul Davis - Paid

International Journal of Mechanical and Production Engineering Research and Development (IJMPERD ) ISSN 2249-6890 Vol.2, Issue 3 Sep 2012 28-35 Š TJPRC Pvt. Ltd.,

OPTIMIZATION OF SURFACE ROUGHNESS IN WET TURNING OPERATION OF EN24 STEEL RAHUL DAVIS Assistant Professor, Department of Mechanical Engineering and Applied Mechanics, SSET, SHIATS, Allahabad -211007, Uttar Pradesh, India

ABSTRACT Generally in the machining operations, the trait of surface finish is an essential concern for most of the turned workpieces. Therefore it is very essential for controlling the required surface quality to have the choice of optimized cutting parameters. The present experimental study is concerned with the optimization of cutting parameters (depth of cut, feed rate, spindle speed) in wet turning EN24 steel (0.4% C) with hardness 40+2 HRC. In the present work, turning operations were carried out on EN24 steel by carbide P-30 cutting tool in wet condition and the combination of the optimal levels of the parameters was obtained. 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 none of the factors was found to be significant. Taguchi method showed that spindle speed followed by feed and depth of cut was the combination of the optimal levels of factors while turning EN24 steel by carbide cutting tool in dry cutting condition. The useful results have been obtained by this research for other similar type of studies and can be helpful for further research works on the vibration of tools etc.

KEYWORDS: EN24 Steel, Wet turning, Surface Roughness, Taguchi Method.

INTRODUCTION Surface finish is a measure to determine the machinability of different materials. Surface roughness greatly affects the performance of manufacturing cost and mechanical parts as well.

Optimization of machining parameters not only increases the utility for machining economics, but also the product quality increases to a great extent. EN24 is a high quality, high tensile, alloy steel and combines high tensile strength, shock resistance, good ductility and resistance to wear1. EN24 is most suitable for the manufacture of parts such as heavy-duty axles and shafts, gears, bolts and studs. EN24 is capable of retaining good impact values at low temperatures2. 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

29

Optimization of Surface Roughness in Wet Turning Operation of EN24 Steel

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.

METHODS The present research work consists of the use of L9 Taguchi orthogonal design6 in order to study the effect of three different parameters (depth of cut, feed & spindle speed) on the surface roughness of the specimens of EN-24 steel after turning operations were done 9 times followed by measurements of surface roughness around the part with the workpiece fixture and the measurements were taken across1the lay, while the setup was a three-jaw chuck in Sparko Engineering Workshop, Allahabad (U.P.). The total length of the workpiece (250 mm) was divided into 6 equal parts and the surface roughness measurements were taken of each 41.6 mm around each workpiece.

The turning operations were performed of by Carbide cutting tool in wet cutting condition. For this purpose a coolant was used.2 In proposed work, EN24 steel with carbon (0.4%), Nickel (1.5 %), Chromium (1 %) and Molybdenum (0.23 %) was selected as the specimen material.

The values of the input process 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

1.5

Feed (mm/rev)

1.21

1.81

3.63

Spindle (rpm)

780

1560

2340

Speed

In the present experimental work, the assignment of factors was carried out using MINITAB-15 Software. The trial runs specified in L9 orthogonal array were conducted on Lathe Machine for turning operations.

1

Department of Mechanical Engineering and Applied Mechanics, SSET, SHIATS, Allahabad -211007, Uttar Pradesh, India 2

Department of Mechanical Engineering and Applied Mechanics, SSET, SHIATS, Allahabad -211007, Uttar Pradesh, India

30

Rahul Davis

Table 2.2: Results of Experimental Trial Runs for Turning Operation Experiment No.

Depth of Cut (mm)

Feed Rate (mm/rev)

Spindle Speed

Surface Roughness (Âľm)

Signal-to-Noise (SN) ratio

(rpm) 1

0.5

1.21

780

20.8

-26.3613

2

0.5

1.81

1560

25

-27.9588

3

0.5

3.63

2340

4.16

-12.3819

4

1

1.21

1560

24.167

-27.6619

5

1

1.81

2340

20.83

-26.3738

6

1

3.63

780

24.16

-27.6619

7

1.5

1.21

2340

21.58

-26.681

8

1.5

1.81

780

6.7

-16.5215

9

1.5

3.63

1560

14.16

-23.0213

Table 2.3: ANOVA Table for Means Parameter

DF

SS

MS

F

P

Depth of Cut

2

126.5

63.3

0.59

0.629

Feed

2

97.4

48.7

0.45

0.688

Spindle Speed

2

49.2

24.6

0.23

0.813

Error

2

214.4

107.2

Total

8

487.6

Table 2.4: ANOVA Table for Signal-to-Noise Ratios for the Response Data Parameter

DF

SS

MS

F

P

Depth of Cut

2

51.63

25.82

0.43

0.698

Feed

2

52.11

26.05

0.44

0.696

Spindle Speed

2

29.57

14.79

0.25

0.801

Error

2

119.25

59.63

Total

8

252.57

31

Optimization of Surface Roughness in Wet Turning Operation of EN24 Steel

Table 2.5: Response Table for Signal-to-Noise ratio of Surface Roughness Depth of Cut

Feed Rate

Spindle Speed

(A)

(B)

(C)

1

-22.23

-26.90

-23.51

2

-27.23

-23.62

-26.21

3

-22.07

-21.02

-21.81

Delta (∆max-min)

5.16

5.88

4.40

Rank

2

1

3

Level

From Table 3.24, Optimal Parameters for Turning Operation were A3, B3 and C3.

Therefore the Predicted value of SN Ratio for Turning Operation can be obtained using the predictive equation ηp (Surface Roughness) = -23.87+ [-18.95-(-23.87)] + [-21.81-(-23.87)] + [-22.07-(-23.87)] =-15.09

RESULTS Comparing the F values of ANOVA Table 2.4 of Surface Roughness with the suitable F values of the Factors (F0.05;2;2 = 19.0) for 95% confidence level respectively shows that the all these factors are insignificant.

Main Effects Plot for Means Data Means Depth of Cut (mm)

Feed Rate (mm/rev)

22 20

Mean of Means

18 16 14 0.5

1.0 Spindle Speed (rpm)

1.5

780

1560

2340

1.21

1.81

22 20 18 16 14

Figure 4.1: Main Effects Plot for Means

3.63

32

Rahul Davis

Main Effects Plot for Means

Fig 4.1 shows the effect of the each level of the three factors on surface roughness for the mean values of measured surface roughness at each level for all the 9 trial runs.

Main Effects Plot for SN ratios Data Means Depth of Cut (mm)

Feed Rate (mm/rev)

-22

Mean of SN ratios

-24 -26 -28 0.5

1.0 Spindle Speed (rpm)

1.5

780

1560

2340

1.21

1.81

3.63

-22 -24 -26 -28

Signal-to-noise: Smaller is better

Figure 4.2: Main Effects Plot for S/N ratio Main Effects Plot for SN Ratio Fig 4.2 shows the effect of the each level of the three factors on surface roughness for the mean values of obtained SN ratio at each level for all the 9 trial runs Table No. 2.5 shows the results of Signal-to-Noise ratio for Surface Roughness. Comparing the F values of ANOVA Table No. 2.4 of Surface Roughness with the suitable F values of the Factors (F0.05;2;2 = 19.0) for 95 % confidence level, shows that all these factors are insignificant factor.

DISCUSSIONS From Table 2.6, Fig 4.1 and Fig 4.2, From Table 3.24, optimal parameters for Surface Roughness are third level of Depth of Cut (A3) i.e 1.5 mm, first level of Feed (B3) i.e 3.63 and third level of Spindle Speed i.e 2340 rpm (C3). So the combination of the factors is not found in any given trial in table No.3.13 gives the optimum result.

3

Few results of the confirmation test of the same of the combination of the optimal levels of the factors

are as follows:

3

Department of Mechanical Engineering and Applied Mechanics, SSET, SHIATS, Allahabad -211007, Uttar Pradesh, India

33

Optimization of Surface Roughness in Wet Turning Operation of EN24 Steel

Table 2.6: Results of the Confirmation Tests of the optimal levels of the factors Specimen

Depth of Cut (mm)

Feed Rate (mm-rev)

Spindle Speed (rpm)

Surface Roughness (Âľm)

1

1.5

3.63

2340

20.11

1

1.5

3.63

2340

20.06

1

1.5

3.63

2340

20.01

CONCLUSIONS 1.

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.

2.

The obtained results can be recommended to get the lowest surface roughness for further research works.In this research work, the material used is EN24 with 0.4% carbon content. The experimentation can also be done for other materials having more hardness to see the effect of parameters on Surface Roughness.

3.

Interactions of the different levels of the factors can also be included to extend the study.

REFERENCES 1.

Website: http://www.westyorkssteel.com/en24.html

2.

Website: www.kvsteel.co.uk/steel/EN24T.html

3.

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

4.

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

5.

Raghuwanshi, B. S.

(2009). A course in Workshop Technology Vol.II (Machine Tools),

Dhanpat Rai & Company Pvt. Ltd. 6.

Ross, Philip J.

(2005). Taguchi Techniques for Quality Engineering, Tata McGraw-Hill

Publishing Company Ltd.

Rahul Davis

7.

34

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.

8.

Van Luttervelt, C. A. et al (1998). Present situation and future trends in modelling of machining operations, CIRP Ann.

9.

Kirby, Daniel (2010). Optimizing the Turning Process toward an Ideal Surface Roughness Target.

10. 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.

11. Kirby, E. Daniel (2006). Optimizing surface finish in a turning operation using the Taguchi parameter design method Int J Adv Manuf Technol: 1021–1029.

12. 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.).

13. 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.

14. Hasegawa. M, et al (1976). Surface roughness model for turning, Tribology International December 285-289.

15. 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. 16. Phadke, Madhav. S. (1989). Quality Engineering using Robust Design. Prentice Hall, Eaglewood Cliffs, New Jersey.

17. 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.

18. 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 390396 19. Palanikumar, K. et al (2006). Assessment of factors influencing surface roughness on the machining of glass –reinforced polymer composites, Journal of Materials and Design.

20. 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.

21. 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.

22. 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.

35

Optimization of Surface Roughness in Wet Turning Operation of EN24 Steel

23. 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.