![International Journal of Engineering Science Invention
ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726
www.ijesi.org Volume 3 Issue 8 ǁ August 2014 ǁ PP.54-58
www.ijesi.org 54 | Page
“The Effect of Process Parameters on Surface Roughness in Face Milling” 1,Sarang S Kulkarni , 2,Prof.M.G.Rathi 1,(Research Scholar Post graduate Student, Mechanical Engineering Department, Government College of Engineering,Aurangabad,Maharashtra,431005) 2,(Asst. Professor, Mechanical Engineering Department ,Government College of Engineering,Aurangabad,Maharashtra,431005) Dr.Babasaheb Ambedkar Marathwada University Aurangabad, India ABSTRACT : The purpose of this study is to investigate effect of process parameters on surface roughness on SAE 1541 material by carbide tools in face milling. In this study, parameters like cutting speed, feed, depth of cut and coolant flow rate considered. Series of experiments were studied by Design of Experiments (DOE). L9 Orthogonal array is selected and experiments were taken by the use of Taguchi method. Experimental Analysis is done by ANOVA and results were drawn out on the basis of analysis. It is investigated that cutting speed, feed and depth of cut are the influencing factors. KEYWORDS: Face Milling, Surface Roughness, Taguchi Method, L9 Orthogonal Array, ANOVA
I. INTRODUCTION
Surface roughness is an important measure of product quality since it greatly influences the performance of mechanical parts as well as production cost. There have been many research developments in modelling surface roughness and optimization of the controlling parameters to obtain a surface finish of desired level since only proper selection of cutting parameters can produce a better surface finish. In the manufacturing industries, various machining processes are adapted for removing the material from the work-piece for a better product. Out of these, end milling process is one of the most vital and common metal cutting operations used for machining parts because of its ability to remove materials faster with a reasonably good surface quality. In recent times, computer numerically controlled machine tools have been implemented to utilize full automation in milling since they provide greater improvements in productivity, increase the quality of the machined parts and require less operator input.[1] Face milling is an operation for producing plane or flat surfaces using face milling cutters. Surface roughness is one of the most important parameter to determine the quality of product. Several factors like cutting speed, feed, depth of cut, type of coolant, coolant pressure, tool geometry, work piece material influence surface roughness[2]. Surface Roughness : The surface parameter used to evaluate surface roughness in this study is the roughness average (Ra). The roughness average is the area between the roughness profile and its central line, or the integral of the absolute value of the roughness profile height over the evaluation length [3]. Ra is defined as the arithmetic value of the departure of the profile from the centerline along sampling length. It can be expressed by the following mathematical relationships. Ra = …(i) where Ra=the arithmetic average deviation from the mean line, and Y =the ordinate of the profile curve.
II. EXPERIMENTAL SET-UP
In this work, Taguchi method used for Design of experiments (DOE).Analysis of Variance is adapted for performance analysis. Finally, with the use of Regression Analysis, mathematical model for Ra is generated and it is validated.](https://image.slidesharecdn.com/j0383054058-140906053002-phpapp02/85/The-Effect-of-Process-Parameters-on-Surface-Roughness-in-Face-Milling-1-320.jpg)
![The Effect of Process Parameters on…
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a) Workpiece Material : In this study,workpiece material is SAE 1541,a type of low alloy steel. It is also called as medium carbon steels is type of Mn alloy [8]. Percentage of each element is given below: Table 1: Chemical composition of work-piece component C Mn Si Cr Al Cu HRC
0.399
1.52
0.205
0.103
0.25
0.15
52-59
b) Working Machine : For the experiments, Hyundai WIA F500DI model Vertical Machining Centre (VMC) is used .For this experiments, IPOL cut 140AS this metal cutting fluid is used whose density is 0.88 (g/cm3) at 15°C,viscosity is 25 (mm^2/sec) at 40°C.Mititoyo surface tester of model SJ-400 is used to measure surface roughness in experimental work. The roughness tester having measuring force 075mN-4mN and Diamond tip 5μm stylus having accuracy ±0.03μm.The probe comes in and out holes while traveling on the surface. The probe is made up of diamond nip which very high in cost
III. EXPERIMENTAL DETAILS
A series of experiments were carried out on Hyundai WIA F500DI (VMC) (Sanjeev Auto). From OVAT analysis four input controlling parameters selected having three levels. Table 2:Summary details of parameters and their levels Sr.No Process Parameters Level –I Level-II Level-III
1
Cutting Speed (rpm)
1200
1400
1600
2
Feed (mm/min)
150
180
210
3
Depth of cut (mm)
0.2
0.3
0.4
4
Coolant Flow Rate (lit/min)
20
40
60
IV. RESULTS AND ANALYSIS
Table 3: Summary Report for different trial conducted during Experimentation Exp No Speed (rpm) Feed (mm/min) DOC (mm) CF (lit/min) Ra (μm) Ra mean S/N Ratio
1
1200
150
0.2
20
0.69
0.785
3.223
2
1200
180
0.3
40
1.07
1.065
-0.5876
3
1200
210
0.4
60
1.48
1.475
-3.4052
4
1400
150
0.3
60
0.75
0.795
2.4987
5
1400
180
0.4
20
1.01
0.935
-0.0864
6
1400
210
0.2
40
1.14
1.075
-1.138
7
1600
150
0.4
40
0.61
0.600
4.2934
8
1600
180
0.2
60
0.63
0.620
4.0131
9
1600
210
0.3
20
0.88
0.895
1.1103
10
1200
150
0.2
20
0.88
0.785
1.1103
11
1200
180
0.3
40
1.06
1.065
-0.5061
12
1200
210
0.4
60
1.47
1.475
-3.3463
13
1400
150
0.3
60
0.84
0.795
1.5144
14
1400
180
0.4
20
0.86
0.935
1.31
15
1400
210
0.2
40
1.01
1.075
-0.0864
16
1600
150
0.4
40
0.59
0.600
4.5829
17
1600
180
0.2
60
0.61
0.620
4.2934
18
1600
210
0.3
20
0.91
0.895
0.8191](https://image.slidesharecdn.com/j0383054058-140906053002-phpapp02/85/The-Effect-of-Process-Parameters-on-Surface-Roughness-in-Face-Milling-2-320.jpg)
![The Effect of Process Parameters on…
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a) S/N Ratio Analysis-
Table 4: Response Table for S/N Ratios
Smaller is better (Ra)
Level Speed Feed DOC CF
1 -0.6278 2.8180 1.8488 1.1859
2 0.6302 1.3866 0.7980 1.0816
3 3.1830 -1.0192 0.5386 0.9180
Delta 3.8109 3.8372 1.3102 0.2679
Rank 2 1 3 4
Table 5: Response Tables for Means
The level of a factor with the highest S/N ratio was the optimum level for responses measured. From
the Table 7 and Figure 4 it is clear that, the better surface finish (Ra) can be obtained at cutting speed (1600
rpm), feed (150 mm/min),depth of cut (0.2mm) and coolant flow rate (20 lit/min). The response table includes
ranks based on Delta statistics, which compare the relative magnitude of effects. The Delta statistic is the
highest minus the lowest average for each factor. Minitab assigns ranks based on Delta values[9]; rank one to
the highest Delta value, rank two to the Second highest, and so on[10].
Fig 1. Effects of process parameters
Mean of Means
1200 1400 1600
1.1
1.0
0.9
0.8
0.7
150 180 210
0.2 0.3 0.4
1.1
1.0
0.9
0.8
0.7
20 40 60
Speed Feed
DOC CF
Main Effects Plot (data means) for Means
Fig2.Effects of process parameters on means
on S/N Ratios
Level Speed Feed DOC CF
1 1.1083 0.7267 0.8267 0.8717
2 0.9350 0.8733 0.9183 0.9133
3 0.7050 1.1483 1.0033 0.9633
Delta 0.4033 0.4217 0.1767 0.0917
Rank 2 1 3 4
Mean of SN ratios
1200 1400 1600
3
2
1
0
-1
150 180 210
0.2 0.3 0.4
3
2
1
0
-1
20 40 60
Speed Feed
DOC CF
Main Effects Plot (data means) for SN ratios
Signal-to-noise: Smaller is better](https://image.slidesharecdn.com/j0383054058-140906053002-phpapp02/85/The-Effect-of-Process-Parameters-on-Surface-Roughness-in-Face-Milling-3-320.jpg)
![The Effect of Process Parameters on…
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Table 8: Comparison of Experimental Ra mean values and Predicted Ra mean values Trial No Predicated Ra mean value (μm) Experimental Ra mean values (μm) % Error
1
0.7709
0.785
1.79%
2
1.1159
1.065
-4.77%
3
1.4609
1.475
0.95%
4
0.7688
0.795
3.29%
5
0.9564
0.935
-2.28%
6
1.0365
1.075
3.58%
7
0.5893
0.600
1.78%
8
0.6394
0.620
-3.12%
9
0.877
0.895
2.01%
V. CONCLUSIONS
This study discussed the effects of face milling parameters on surface roughness of SAE 1541 by Taguchi method. From this research, following conclusions could be reached with a fair amount of confidence. Regardless of the category of the quality characteristics, the lower the better for surface roughness the lowest feed rate (B=150 mm/min),the highest cutting speed ( A= 1600 rpm ),lowest depth of cut ( C=0.2 mm) and the lowest coolant flow ( D=20 lit/min) lead to the lower surface roughness value In this study, feed and cutting speed are the most influencing factor compared to depth of cut. Coolant flow rate is the least significant factor for surface roughness. REFERENCES
[1] By Surasit Rawangwong , Jaknarin Chatthong, Rommdon Burapa and Worapong Boonchouytan An Investigation of Optimum Cutting Conditions for Quality of Surface Roughness in Face Milling Mold Steel AISI P20 Using Carbide Tool in Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference 2012
[2] By Parveen Saini & Suraj Choudhary “Analysis Of Machining Parameters For The Optimization Of Surface Roughness Of Stainless Steel Aisi 202 In Cnc Face Milling Process” in Vol. 2, Issue 3, July 2013, 27-34.
[3] By D. Baji, B. Lela, D. Ivkovi ” Modeling Of Machined Surface Roughness And Optimization Of Cutting Parameters In Face Milling” in 2012.
[4] Taguchi G, Konishi S. Taguchi methods, orthogonal arrays and linear , tools for quality American supplier institute. American Supplier Institute; 1987 [p. 8–35]
[5] . Taguchi G. Introduction to quality engineering. New York: Mc Graw-Hill; 1990.
[6] By Ali RizaMotorcu “The Optimization of Machining Parameters Using the Taguchi Method for Surface Roughness of AISI 8660 Hardened Alloy Steel ” in 29.04.2010.
[7] By B.T.H.T Baharudin, M.R. Ibrahim, N. Ismail, Z. Leman, M.K.A. Ariffin and D.L. Majid
[8] “Experimental Investigation of HSS Face Milling to AL6061 using Taguchi Method” in 2012.
[9] By Séblin. B , Jahazeeah. Y , Sujeebun. S ,Manohar ,Wong Ky. B Material Science 2014.
[10] By Ali RizaMotorcu “The Optimization of Machining Parameters Using the Taguchi Method for Surface Roughness of AISI 8660 Hardened Alloy Steel ” in 29.04.2010.
[11] By Anjan Kumar Kakati, M. Chandrasekaran, AmitavaMandal, and Amit Kumar Singh: ”Prediction of Optimum Cutting Parameters to obtain Desired Surface in Finish Pass end Milling of Aluminium Alloy with Carbide Tool using Artificial Neural Network” in Vol:5 2011-09-25.
[12] By R. Arokiadass1, K. Palaniradja, N. Alagumoorthi “Surface roughness prediction model in end milling of Al/SiCp MMC by carbide tools ” in Vol. 3, No. 6, 2011, pp. 78-87, 2011.
[13] By Eyup Bagci · SerefAykut “A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (stellite 6)” in 21 December 2005.](https://image.slidesharecdn.com/j0383054058-140906053002-phpapp02/85/The-Effect-of-Process-Parameters-on-Surface-Roughness-in-Face-Milling-5-320.jpg)
The Effect of Process Parameters on Surface Roughness in Face Milling
- 1. International Journal of Engineering Science Invention ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726 www.ijesi.org Volume 3 Issue 8 ǁ August 2014 ǁ PP.54-58 www.ijesi.org 54 | Page “The Effect of Process Parameters on Surface Roughness in Face Milling” 1,Sarang S Kulkarni , 2,Prof.M.G.Rathi 1,(Research Scholar Post graduate Student, Mechanical Engineering Department, Government College of Engineering,Aurangabad,Maharashtra,431005) 2,(Asst. Professor, Mechanical Engineering Department ,Government College of Engineering,Aurangabad,Maharashtra,431005) Dr.Babasaheb Ambedkar Marathwada University Aurangabad, India ABSTRACT : The purpose of this study is to investigate effect of process parameters on surface roughness on SAE 1541 material by carbide tools in face milling. In this study, parameters like cutting speed, feed, depth of cut and coolant flow rate considered. Series of experiments were studied by Design of Experiments (DOE). L9 Orthogonal array is selected and experiments were taken by the use of Taguchi method. Experimental Analysis is done by ANOVA and results were drawn out on the basis of analysis. It is investigated that cutting speed, feed and depth of cut are the influencing factors. KEYWORDS: Face Milling, Surface Roughness, Taguchi Method, L9 Orthogonal Array, ANOVA I. INTRODUCTION Surface roughness is an important measure of product quality since it greatly influences the performance of mechanical parts as well as production cost. There have been many research developments in modelling surface roughness and optimization of the controlling parameters to obtain a surface finish of desired level since only proper selection of cutting parameters can produce a better surface finish. In the manufacturing industries, various machining processes are adapted for removing the material from the work-piece for a better product. Out of these, end milling process is one of the most vital and common metal cutting operations used for machining parts because of its ability to remove materials faster with a reasonably good surface quality. In recent times, computer numerically controlled machine tools have been implemented to utilize full automation in milling since they provide greater improvements in productivity, increase the quality of the machined parts and require less operator input.[1] Face milling is an operation for producing plane or flat surfaces using face milling cutters. Surface roughness is one of the most important parameter to determine the quality of product. Several factors like cutting speed, feed, depth of cut, type of coolant, coolant pressure, tool geometry, work piece material influence surface roughness[2]. Surface Roughness : The surface parameter used to evaluate surface roughness in this study is the roughness average (Ra). The roughness average is the area between the roughness profile and its central line, or the integral of the absolute value of the roughness profile height over the evaluation length [3]. Ra is defined as the arithmetic value of the departure of the profile from the centerline along sampling length. It can be expressed by the following mathematical relationships. Ra = …(i) where Ra=the arithmetic average deviation from the mean line, and Y =the ordinate of the profile curve. II. EXPERIMENTAL SET-UP In this work, Taguchi method used for Design of experiments (DOE).Analysis of Variance is adapted for performance analysis. Finally, with the use of Regression Analysis, mathematical model for Ra is generated and it is validated.
- 2. The Effect of Process Parameters on… www.ijesi.org 55 | Page a) Workpiece Material : In this study,workpiece material is SAE 1541,a type of low alloy steel. It is also called as medium carbon steels is type of Mn alloy [8]. Percentage of each element is given below: Table 1: Chemical composition of work-piece component C Mn Si Cr Al Cu HRC 0.399 1.52 0.205 0.103 0.25 0.15 52-59 b) Working Machine : For the experiments, Hyundai WIA F500DI model Vertical Machining Centre (VMC) is used .For this experiments, IPOL cut 140AS this metal cutting fluid is used whose density is 0.88 (g/cm3) at 15°C,viscosity is 25 (mm^2/sec) at 40°C.Mititoyo surface tester of model SJ-400 is used to measure surface roughness in experimental work. The roughness tester having measuring force 075mN-4mN and Diamond tip 5μm stylus having accuracy ±0.03μm.The probe comes in and out holes while traveling on the surface. The probe is made up of diamond nip which very high in cost III. EXPERIMENTAL DETAILS A series of experiments were carried out on Hyundai WIA F500DI (VMC) (Sanjeev Auto). From OVAT analysis four input controlling parameters selected having three levels. Table 2:Summary details of parameters and their levels Sr.No Process Parameters Level –I Level-II Level-III 1 Cutting Speed (rpm) 1200 1400 1600 2 Feed (mm/min) 150 180 210 3 Depth of cut (mm) 0.2 0.3 0.4 4 Coolant Flow Rate (lit/min) 20 40 60 IV. RESULTS AND ANALYSIS Table 3: Summary Report for different trial conducted during Experimentation Exp No Speed (rpm) Feed (mm/min) DOC (mm) CF (lit/min) Ra (μm) Ra mean S/N Ratio 1 1200 150 0.2 20 0.69 0.785 3.223 2 1200 180 0.3 40 1.07 1.065 -0.5876 3 1200 210 0.4 60 1.48 1.475 -3.4052 4 1400 150 0.3 60 0.75 0.795 2.4987 5 1400 180 0.4 20 1.01 0.935 -0.0864 6 1400 210 0.2 40 1.14 1.075 -1.138 7 1600 150 0.4 40 0.61 0.600 4.2934 8 1600 180 0.2 60 0.63 0.620 4.0131 9 1600 210 0.3 20 0.88 0.895 1.1103 10 1200 150 0.2 20 0.88 0.785 1.1103 11 1200 180 0.3 40 1.06 1.065 -0.5061 12 1200 210 0.4 60 1.47 1.475 -3.3463 13 1400 150 0.3 60 0.84 0.795 1.5144 14 1400 180 0.4 20 0.86 0.935 1.31 15 1400 210 0.2 40 1.01 1.075 -0.0864 16 1600 150 0.4 40 0.59 0.600 4.5829 17 1600 180 0.2 60 0.61 0.620 4.2934 18 1600 210 0.3 20 0.91 0.895 0.8191
- 3. The Effect of Process Parameters on… www.ijesi.org 56 | Page a) S/N Ratio Analysis- Table 4: Response Table for S/N Ratios Smaller is better (Ra) Level Speed Feed DOC CF 1 -0.6278 2.8180 1.8488 1.1859 2 0.6302 1.3866 0.7980 1.0816 3 3.1830 -1.0192 0.5386 0.9180 Delta 3.8109 3.8372 1.3102 0.2679 Rank 2 1 3 4 Table 5: Response Tables for Means The level of a factor with the highest S/N ratio was the optimum level for responses measured. From the Table 7 and Figure 4 it is clear that, the better surface finish (Ra) can be obtained at cutting speed (1600 rpm), feed (150 mm/min),depth of cut (0.2mm) and coolant flow rate (20 lit/min). The response table includes ranks based on Delta statistics, which compare the relative magnitude of effects. The Delta statistic is the highest minus the lowest average for each factor. Minitab assigns ranks based on Delta values[9]; rank one to the highest Delta value, rank two to the Second highest, and so on[10]. Fig 1. Effects of process parameters Mean of Means 1200 1400 1600 1.1 1.0 0.9 0.8 0.7 150 180 210 0.2 0.3 0.4 1.1 1.0 0.9 0.8 0.7 20 40 60 Speed Feed DOC CF Main Effects Plot (data means) for Means Fig2.Effects of process parameters on means on S/N Ratios Level Speed Feed DOC CF 1 1.1083 0.7267 0.8267 0.8717 2 0.9350 0.8733 0.9183 0.9133 3 0.7050 1.1483 1.0033 0.9633 Delta 0.4033 0.4217 0.1767 0.0917 Rank 2 1 3 4 Mean of SN ratios 1200 1400 1600 3 2 1 0 -1 150 180 210 0.2 0.3 0.4 3 2 1 0 -1 20 40 60 Speed Feed DOC CF Main Effects Plot (data means) for SN ratios Signal-to-noise: Smaller is better
- 4. The Effect of Process Parameters on… www.ijesi.org 57 | Page From main effect plot in figure 1.It has been shown that the value of a S/N ratio is maximum at speed of 1600 rpm and minimum at 1200 rpm, further it has been shown that the value of S/N ratio is increasing initially but it decreases further with the increasing in feed rate, and also as depth of cut increases, surface roughness also increases from 0.2 mm to 0.4 mm. Surface roughness value increases from 20 lit/min to 40 lit/min of coolant flow rate. Table 6: Analysis of Variance for S/N Ratio S = 0.697035 R-Sq = 95.67% R-Sq(adj) = 91.83% Table 7: Analysis of Variance for Ra S = 0.0689202 R-Sq= 96.45% R-Sq(adj) = 93.29 Fig 3.Residual plots for S/N ratio of Ra Source DF Seq SS Adj SS F P Speed 2 44.248 44.248 45.54 0.000 Feed 2 46.023 46.023 47.36 0.000 DOC 2 6.136 6.136 6.31 0.019 CF 2 0.307 0.307 0.32 0.737 Error 9 4.373 4.373 Total 17 101.087 Source DF Seq SS Adj SS F P Speed 2 0.49124 0.49124 51.71 0.000 Feed 2 0.54988 0.54988 57.88 0.000 DOC 2 0.09368 0.09368 9.86 0.005 CF 2 0.02528 0.02528 2.66 0.124 Error 9 0.04275 0.04275 Total 17 1.20283 Residual Percent 1.00.50.0-0.5-1.0999050101Fitted Value Residual 420-2-41.00.50.0-0.5-1.0Residual Frequency 1.00.50.0-0.5-1.043210Observation Order Residual 181614121086421.00.50.0-0.5-1.0Normal Probability Plot of the ResidualsResiduals Versus the Fitted ValuesHistogram of the ResidualsResiduals Versus the Order of the DataResidual Plots for SNRA1
- 5. The Effect of Process Parameters on… www.ijesi.org 58 | Page Table 8: Comparison of Experimental Ra mean values and Predicted Ra mean values Trial No Predicated Ra mean value (μm) Experimental Ra mean values (μm) % Error 1 0.7709 0.785 1.79% 2 1.1159 1.065 -4.77% 3 1.4609 1.475 0.95% 4 0.7688 0.795 3.29% 5 0.9564 0.935 -2.28% 6 1.0365 1.075 3.58% 7 0.5893 0.600 1.78% 8 0.6394 0.620 -3.12% 9 0.877 0.895 2.01% V. CONCLUSIONS This study discussed the effects of face milling parameters on surface roughness of SAE 1541 by Taguchi method. From this research, following conclusions could be reached with a fair amount of confidence. Regardless of the category of the quality characteristics, the lower the better for surface roughness the lowest feed rate (B=150 mm/min),the highest cutting speed ( A= 1600 rpm ),lowest depth of cut ( C=0.2 mm) and the lowest coolant flow ( D=20 lit/min) lead to the lower surface roughness value In this study, feed and cutting speed are the most influencing factor compared to depth of cut. Coolant flow rate is the least significant factor for surface roughness. REFERENCES [1] By Surasit Rawangwong , Jaknarin Chatthong, Rommdon Burapa and Worapong Boonchouytan An Investigation of Optimum Cutting Conditions for Quality of Surface Roughness in Face Milling Mold Steel AISI P20 Using Carbide Tool in Proceedings of the Asia Pacific Industrial Engineering & Management Systems Conference 2012 [2] By Parveen Saini & Suraj Choudhary “Analysis Of Machining Parameters For The Optimization Of Surface Roughness Of Stainless Steel Aisi 202 In Cnc Face Milling Process” in Vol. 2, Issue 3, July 2013, 27-34. [3] By D. Baji, B. Lela, D. Ivkovi ” Modeling Of Machined Surface Roughness And Optimization Of Cutting Parameters In Face Milling” in 2012. [4] Taguchi G, Konishi S. Taguchi methods, orthogonal arrays and linear , tools for quality American supplier institute. American Supplier Institute; 1987 [p. 8–35] [5] . Taguchi G. Introduction to quality engineering. New York: Mc Graw-Hill; 1990. [6] By Ali RizaMotorcu “The Optimization of Machining Parameters Using the Taguchi Method for Surface Roughness of AISI 8660 Hardened Alloy Steel ” in 29.04.2010. [7] By B.T.H.T Baharudin, M.R. Ibrahim, N. Ismail, Z. Leman, M.K.A. Ariffin and D.L. Majid [8] “Experimental Investigation of HSS Face Milling to AL6061 using Taguchi Method” in 2012. [9] By Séblin. B , Jahazeeah. Y , Sujeebun. S ,Manohar ,Wong Ky. B Material Science 2014. [10] By Ali RizaMotorcu “The Optimization of Machining Parameters Using the Taguchi Method for Surface Roughness of AISI 8660 Hardened Alloy Steel ” in 29.04.2010. [11] By Anjan Kumar Kakati, M. Chandrasekaran, AmitavaMandal, and Amit Kumar Singh: ”Prediction of Optimum Cutting Parameters to obtain Desired Surface in Finish Pass end Milling of Aluminium Alloy with Carbide Tool using Artificial Neural Network” in Vol:5 2011-09-25. [12] By R. Arokiadass1, K. Palaniradja, N. Alagumoorthi “Surface roughness prediction model in end milling of Al/SiCp MMC by carbide tools ” in Vol. 3, No. 6, 2011, pp. 78-87, 2011. [13] By Eyup Bagci · SerefAykut “A study of Taguchi optimization method for identifying optimum surface roughness in CNC face milling of cobalt-based alloy (stellite 6)” in 21 December 2005.