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
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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.
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2.
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Rahul Davis
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Optimization of Surface Roughness in Wet Turning Operation of EN24 Steel
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