Optimization Of Machining Parameters For Surface Finish Of EN 42 Alloy Steel For End Milling Operation Using Taguchi Method

 

Pragati D. Tayade1, Dr. S.P. Trikal2

1ME (Mechanical), S S G M College of Engineering, Shegaon

2Professor and Head, Department of Mechanical Engineering, S S G M College of Engineering, Shegaon

*Corresponding Author E-mail: pragatitayade777@gmail.com, sptrikal1971@yahoo.com

 

ABSTRACT:

Present work includes the optimization of machining parameters to obtain surface finish of EN42 alloy steel using end milling operation. Milling operation performed on EN42 alloy steel using TiAlNi coated solid carbide end milling cutter of 12mm diameter. For this experimental work end milling machining parameters considered are spindle speed, feed and depth of cut. Taguchi method is implemented to find out the optimum cutting parameters for surface roughness. The process parameters are obtained for surface finish using Taguchi technique in minitab software.

 

KEYWORDS: Milling Operation, Taguchi Method, Machining Parameters, Surface Roughness.

 


INTRODUCTION:

The machining industries are facing a great challenge to achieve high quality, good surface finish and high material removal rate with a view to economize in machining. End Milling is widely used in a variety of manufacturing industries including the aerospace and automotive sectors, where quality is an important factor in the production of slots, pockets, precision molds, and dies because good-quality milled surface significantly improves fatigue strength, corrosion resistance, and creep life. Surface roughness is an important measure of the quality of a product and a factor that greatly influences manufacturing cost. In milling to achieve high cutting performance, selection of optimum parameter selection is determined by the operator’s experience knowledge or the design data book. [5]

 

LITERATURE SURVEY

L B Abhang and M Hameedullah [1] (2012), In this work machining parameters optimized in steel turning operation by Taguchi method for EN-31 steel alloy by using tungsten carbide inserts. Cutting parameters was feed rate, depth of cut, lubricant temperature with response parameter surface roughness. Better surface finish is obtained by applying cooled lubricant.

 

Mrs. Prachi Londhe/Chilwant [2] (2016),In this experimental work cutting parameters Optimized in milling operation to improve surface finish by using taguchi method. The experiments were conducted on EN31 material by using carbide inserts. Control factors were selected for machining operation cutting speed, feed rate, depth of cut, coolant rate. Cutting speed is more influencing factor on surface roughness out of machining parameters.

 

Shadab Anwar, Saleem UZ Zaman Khan [3] (2016), This work based on Optimize end milling parameters for improving surface roughness of EN-31 using taguchi method. The effect of spindle speed, feed rate and depth of cut on surface roughness is investigated by using end milling cutter is investigated.

 

Joao Eduardo Ribeiro , Manuel Braz  C’esar, Hernani Lopes [4] (2017) , This paper involved  improve the surface finish of steel for moulds (GMTC 1.2738) of hardness 45 HRC with the help of end mill cutter and analyse the influence of cutting parameters namely cutting speed, feed rate, axial depth and radial depth on surface roughness (ANOVA).

 

Vishal parashar, Shailandra S. Bhadauria, Yogesh sahu [5] (2015), In this paper optimum surface roughness for EN-19 tool is obtained in end milling operation by using taguchi method .speed is more influencing factor on surface roughness out of speed, feed rate and depth of cut.

 

G. Tamil Kumaran, R. John Stephen [6], In this work CVD and PVD coated face milling cutters use for Optimization of Machining Parameters using ANOVA. Experiment is carried on VMC for EN19 die steel as a work piece material. PVD insetrs are responsible for better surface finish when machining with input machining parameters namely cutting speed, feed rate and depth of cut.

 

S.T. Warghat, Dr. T.R. Deshmukh [7] (2015),This paper discussed the literature review of optimization of machining parameters for end milling operation. In this paper, in order to achieve optimal machining parameters for purpose of end milling operation a particle swarm optimization algorithm implemented.

 

Omprakash Waikar, Swapnil Nikam, Rahul More, Shivam Shinde, Rahul Chakle [8], In this paper EN-31 work piece material is machine by grinding operation with grinding wheels. In this study effect of cutting parameters on surface roughness is studied by using minitab software.

 

Ali Abdallah, Bhuvenesh Rajamoy, Adulnasser Embark [9] (2014),In this paper optimal value of cutting parameters namely cutting speed, feed rate and depth of cut obtained by turning operation with help of taguchi L9 orthogonal array. This study determines the most effective cutting parameter on output that is surface roughness and material removal rate for aluminium alloy 6061 material.

 

S.Sathiyaraj, A.Elanthiraiyan, G.Haripriya, V.Srikanth pari [10] (2015), This experimental work is carried out to optimization of machining parameters for en8 steel by using carbide tipped tool. Experiment was plane by using taguchi L9 orthogonal array. Input variables for this work are spindle speed, feed rate and depth of cut and output parameters surface roughness.

 

Workpiece material

The workpiece material is used EN42 steel in form of bar of 70mm length and 40mm diameter.  EN42 special gear case hardening tensile tools and it’s hardness is 40-45 HRC. EN42 is basically alloys steel so it consist different components. Chemical composition of EN42 alloy steel.

 

Table1: Chemical composition of EN42 alloy steel

Grade

Element

EN42

C

Mn

Si

S

P

0.70-0.85%

0.55-0.75%

0.10-0.40%

0.04%

0.04%

 

 

 

 

Figure 1: EN42 alloy steel

 

 

Cutting tool

The cutting tool used for machining is TiAlNi coated solid carbide tool of 12mm diameter. . It consists of four teeth. It consists of very high hardness 60-62 HRC and good toughness and it is principally intended for roughing of super alloys and steel alloys.

 

 

Figure 2: End milling cutter

 

Cutting fluid

Coolant used for machining operation is water soluble oil. Ratio of coolant to water is 1:5 i.e. 1liter of oil mixed with 5 liters of water.

 

Surface roughness tester

Surface roughness is an essential requirement in determining the surface quality of a product. It is one of the crucial performance parameters that have to be controlled within suitable limit for a particular process. [7]

 

Surface roughness of machined components is measured with help of Mitutoyo SJ-410 surface roughness tester. It is portable type of surface roughness tester.

 

 

Figure 3: Surface roughness tester

 

Machining parameters

To improve surface finish of EN42 alloy steel using end milling operation experiments are carried out by considering three machining parameters namely spindle speed, feed rate and depth of cut. So here three machining parameters and their three levels are selected as input parameters for end milling operation.

 

Table 2:  Machining Parameters and Levels

Sr. No.

Machining parameter

Levels

1

2

3

1

Spindle speed(rpm)

2500

2700

2900

2

Feed (mm/min)

50

100

130

3

Depth of cut (mm)

0.1

0.2

0.3

 

 

Figure 4: End milling operation

Taguchi method

To optimize parameters of a process and improve the components quality, that are manufactured Taguchi Method is used which is actually statistical approach. Taguchi and Konishi have developed the Taguchi method. Primarily to improving the manufactured goods (development of manufacturing process) quality, Taguchi method was developed. At a later stage its application of this method was expanded to various other engineering fields, like Biotechnology etc. Taguchi’s efforts have been acknowledged by qualified statisticians especially in the development of designs for studying variation. Desired results are successfully achieved by careful selection of control factors and divide them into control and noise factors. Control factors must be selected in such a way that it eliminates the effect of noise factor. Proper control factors are recognised by Taguchi method and optimum results of the process are obtain by this method. To conduct a set of experiments orthogonal array (OA) are selected. To analyse the data and predict the quality of components produced, results of these experiments are used. [11]

 

Selection of orthogonal array

Selection of orthogonal array depends upon number of input parameters and their levels. For this study there are three input parameters and their three levels. The selection of orthogonal array for experiment was done by using Minitab statistical software. By putting parameter variation levels in Minitab statistical software, the Minitab suggests that L9 (3*3) fractional factorial orthogonal array is most compatible for this experiment. This design reduces the number of experiments to a designed set of 9 experiments without compromise quality of experiment. The experiment table suggested by Minitab software for L9 Orthogonal array is shown in Table 3 .

 

 

Table 3: Standard L9 Orthogonal Array Experiment

Experiment no.

Control factors

1

2

3

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

 

The design of experiment shown in table 4.

 

 

Table 4:  Experimental Design

Experiment no.

Control factors

1

2

3

1

2500

50

0.1

2

2500

100

0.2

3

2500

130

0.3

4

2700

50

0.2

5

2700

100

0.3

6

2700

130

0.1

7

2900

50

0.3

8

2900

100

0.1

9

2900

130

0.2

 

 

RESULT AND DISCUSSION:

From the experiment we have obtained the optimum combination of of machining parameter (spindle speed, feed, depth of cut) which leads to minimum surface roughness of EN42 alloy steel. In this experimental work, software used for obtained the signal to noise ratio. The effect of different process parameters on surface roughness changes from one level to another level are calculated and plotted as shown in fig. 5.

 

 

 

Table 5: The result of experiment with surface roughness (RA) values

Test number

N

F

D

Ra Values (µm)

1.

2500

50

0.1

0.1

2.

2500

100

0.2

0.13

3.

2500

130

0.3

0.16

4.

2700

50

0.2

0.12

5.

2700

100

0.3

0.16

6.

2700

130

0.1

0.18

7.

2900

50

0.3

0.10

8.

2900

100

0.1

0.15

9.

2900

130

0.2

0.17

 

 

 

Figure 5:  Graphical representation of RA values with respect to test number

 

Taguchi Design

Taguchi Orthogonal Array Design

 

L9 (3^3)

Factors:  3

Runs:     9

 

Response Table for Signal to Noise Ratios

Smaller is better

 

Level                     N                            F                             D            

1                             17.88                      19.47                      17.12

2                             16.41                      16.71                      17.18

3                             17.29                      15.40                      17.28

Delta                      1.47                        4.07                        0.15

Rank                      2                             1                             3

 

 

Figure 6:  Effect of Process Parameters (Ra)

 

Analysis of Variance (ANOVA)

Analysis of variance is powerful statistic and core technique for testing causality in nonlinear models. ANOVA used to test the process variables for significant effect on objective function of the desired model. According to ANOVA if the Probability “P-value >F” less than 0.05 than said to be the model terms are significant.

 

From table 6 it observed that feed is most significant parameter for minimum surface roughness as compare to spindle speed and depth of cut.

 

Table 6:  Analysis of variance of S/N ratios for surface roughness

Source

DF

SeqSS

AdjSS

AdjMS

F-Value

P-Value

% Contribution

N

2

0.000822

0.000822

0.000411

0.39

0.691

11.60%

F

2

0.006156

0.006156

0.003078

19.79

0.002

86.83%

D

2

0.000022

0.000022

0.000011

0.01

0.991

0.31%

ERROR

 

1.26%

TOTAL

 

100%

 

 

CONCLUSION:

The following conclusions can be drawn from experimental result of this study.

·                Taguchi robust orthogonal array design method is suitable to analyse the surface roughness and allowed to determine the contribution of each machining parameter and their interaction.

·                The effect of machining parameters(spindle speed, feed, depth of cut)  of end milling by using end milling cutter on surface roughness investigated by using solid carbide tool.

·                In present experimentation work the optimum combination of machining parameters obtained for minimum surface roughness by using taguchi method is spindle speed 2500 rpm , feed 50 mm/m, depth of cut 0.1mm

·                ANOVA used for find out most affecting input parameter on surface roughness so for this experimental work according to ANOVA most significant factor on surface roughness is feed and least significant factor is depth of cut.

·                Feed (86.83%) contributes maximum followed by spindle speed (11.60%) and depth of cut (0.31%) to minimize the surface roughness.

 

 

REFERENCES:

1.          L B Abhang and M Hameedullah (2012), ‘‘Optimization of machining parameters in steel turning operation by Taguchi method”, SciVerse ScienceDirect

2.          Mrs. Prachi Londhe/Chilwant (2016), ‘‘Optimization of cutting parameters in milling operation to improve surface finish of EN 31”, International Journal OF Engineering Sciences and Management Research

3.          Shadab Anwar, Saleem UZ Zaman Khan (2016), ‘‘Optimization of end milling parameters for improving surface roughness using taguchi method”, ISSN: 2320-2092, Volume- 4, Issue-8, Aug.-2016

4.          Joao Eduardo Ribeiro, Manuel Braz C’esar , Hernani Lopes , ‘‘Optimization of machining parameters to improve the surface finish” , Science Direct ICSI   (2017) 355-362.

5.          Vishal Parashar, Shailandra S. Bhadauria, Yogesh Sahu (2015) , ‘‘Optimization of surface roughness using taguchi method in end milling of steel grade EN19 with tin coated carbide tool”, International Journal of Mechanical And Production Engineering, ISSN: 2320-2092,  Volume- 3, Issue-2, Feb.-2015

6.          G. Tamil Kumaran, R. John Stephen, ‘‘Optimization of Machining Parameters for Face Milling Operation using ANOVA”, e-ISSN: 2278-1684, p-ISSN: 2320-334X

7.          S.T. Warghat, Dr. T.R. Deshmukh (2015), ‘‘A review on Optimization of machining parameters for end milling operation”, International Journal OF Engineering research and applications (IJERA) ISSN: 2248-9622

8.          Omprakash Waikar, Swapnil Nikam, Rahul More, Shivam Shinde, Rahul Chakle, ‘‘Optimization of machining parameters for surface roughness”, International journal of mechanical and civil engineering (IOSR-JMCE)

9.          Ali Abdallah, Bhuvenesh Rajamoy, Adulnasser Embark (2014), ‘‘Optimization of cutting Parameters for Surface Roughness in CNC turning machining with aluminium alloy 6061 material”, (IOSRJEN) ISSN(e): 2250-3021

10.        S.  Sathiyaraj, A. Elanthiraiyan, G. Haripriya, V. Srikanth Pari (2015), ‘‘Optimization of machining parameters for EN8 steel through taguchi method”, Journal of Chemical and Pharmaceutical Sciences ISSN: 0974-2115

11.        Sanjay Kumar Mishra and Shabana Naz Siddique (2017), ‘‘Study Of Performance Of Milling Machine For Optimum Surface Roughness” , Research Journal Of Engineering Science  ISSN 2278-9472

 

 

 

 

Received on 16.08.2020            Accepted on 15.09.2020     

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Int. J. Tech. 2020; 10(2): 115-121.

DOI: 10.5958/2231-3915.2020.00022.X