# Optimization of Boring Process Parameters By Using Taguchi Method

DOI : 10.17577/IJERTV3IS080601

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#### Optimization of Boring Process Parameters By Using Taguchi Method

Mayuresh P Vaishnav*,

*(Research Scholar Post graduate Student, Mechanical Engineering Department, Government College of Engineering, Aurangabad

,Maharashtra,431005)

S A Sonawane**

**(Asst. Professor, Mechanical Engineering, Department, Government College of Engineering, Aurangabad,Maharashtra,431005)

Abstract : – In order to produce any product with desired quality by machining , proper selection of parameter is essential surface an indicator of surface quality is one of the prime customer requirement for machined parts. In this paper Taguchi parameter optimization methodology is applied to optimize cutting parameter as speed, feed and coolant flow for mild steel SAE1541 using regression analysis method. The surface roughness were selected as the quality targets. The result analysis show that feed rate and cutting speed have present significant contribution on the surface roughness and coolant flow rate have less significant contribution on surface roughness.

INTRODUCTION :

Metal based industry are focused to increase productivity and quality of the machined parts. For these

SURFACE ROUGHNESS AND MEASUREMENT

Surface roughness of a machined product could affect several of the products functional attributes, such as contact causing surface friction, wearing, light reection, heat transmission, ability of distributing and holding a lubricant, coating, and resisting fatigue. There are several ways to describe surface rough- ness. One of them is average roughness which is often quoted as Ra symbol. Ra is dened as the arithmetic value of the departure of the prole from the centerline along sampling length as shown in Fig. 1. It can be expressed by the following mathematical relationships.

Ra = 1 ()

purpose all aspects of every process need to be monitored. Certain desired parameter of a machined parts are chosen and checked against desired degree of a quality.

where

0

Surface finish is one of these important parameter in a manufacturing. It directly affects performing efficiency of a mechanical parts as well as their production cost. The ratio between cost and quality of a products n each production stage has t be monitored and immediate corrective action have to be taken in case of a deviation from a desired trend.

BORING :For internal machining ,Boring is a precision operation. It increases whole diameter and also it gives desired degree of a surface roughness provided that parameters affecting are maintained under control conditions as observed in experimental analysis. This process used after drilling or cast.

Boring is a unit process in manufacturing as a mass reduction step, used for enlarging and accurately sized existing hole by means of a single point of a cutting tool with multiple cutting edges

Boring is used to achieve greater accuracy of the diameter of the hole and can be used to a cut tapered hole. Boring is done with the conjunction with turning, facing or other machined operation. Because of the limitation on tooling design imposed by the fact the work piece mostly surrounded the tool ,boring is inherently somewhat more challenging than turning. Boring Can be viewed as the internal diameter counterpart to turning ,which cuts external diameter.

Ra=the arithmetic average deviation from the mean line,

and

Y =the ordinate of the prole curve.

There are many methods of measuring surface roughness, such as using specimen blocks by eye or ngertip, microscopes, stylus type instruments, prole tracing instruments, etc. The tools measuring surface roughness with probes, measure, and control in appropriate length and circumferences. 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.

LITERATURE REVIEW

1. Show-shynlin (2009) investigated the optimization of 6061T6 CNC boring process using the Taguchi method and grey relation analysis. the surface properties of a roughness average roughness and maximum roughness as well as the roundness were selected as to quality targets. Analysis of variance (ANOVA) is also used to analyze the influence of the cutting parameter during machining. The result related that the feed rate is the most influence factor on the average roughness and maximum roughness and the cutting speed is the most influential factor to the roundness. [ 1]

2. Adel H.Suhail et.al [2010](2) conducted experimental study to optimize the cutting parameter using two

performance measure work piece, surface temperature and surface roughness. He has used carbon steel AISI 1020 and its dimension were 250mm long with 50 mm dia. Optimal cutting parameter for each performance were obtained using Taguchi techniques. The orthogonal array, signal to noise ratio and ANOVA were employed to study the performance characteristics in operation. The experimental result showed that the work piece surface temperature can be sensed and used effectively as an indicator to control the cutting performance and improves the optimization process.

3. Harriman Singh sodhi (2012) Investigated cutting parameters for surface roughness of a mild steel in boring process using Taguchi method. He investigated that the influence of the three most important machining parameter of depth of cut, feed rate and the cutting speed on surface roughness during turning of a mild steel by using carbide tool of 0.06 mm of a noise radius. ANOVA is used to analyze the influence of the machining parameter of the surface roughness. Results shows that the cutting speed and feed rate have present significant contribution on the surface roughness and depth of cut have less significant contribution on surface roughness.

4. GauravVhora (2013) investigated that the analysis and optimization of a boring process parameter by using Taguchi method. In investigation boring parameters for a CNC turning center such as speed, feed experiment ,dept of cut is done on a aluminum to achieve the highest possible material removal rate and at that same time minimum surface roughness by using the Taguchi method. The objective of the study was to identify the performance characteristics and select process parameter to be evaluate and to determine the no. of parameters level for the process and possible interaction between the process parameter. Experimental

Medium carbon low alloy steel ASI1541is nothing but the mild steel. It was used as work piece material (Hardness,51-59 HRC).Chromium (Cr) ,Manganese (Mn) and Silicon (Si) alloyed materials offers a very good polish ability and this material we are using to make a gear shifting fork. Material composition of work piece is as follows : C,0.41; Mn,1.52; Si,0.205; Cr,0.1; Al,0.25;

Cu,0.15% by wt.

 C Mn Si Cr Al Cu HRC 0.39 1.52 0.205 0.103 0.255 0.155 52-59

Table 1: Chemical composition of work-piece component

TAGUCHI METHOD

Taguchi method based design of experiment has been used to study effect of three machining parameters like speed, feed, coolant flow on one important parameter like surface rougness. for selecting appropriate orthogonal arrays, degree of freedom of array is calculated. There are eight degree of freedom owing to three machining input parameters, so Taguchi based L9 arrays is selected. Accordingly,9 experiment were carried out the study the effect of machining input.parameters. Each experimentwas repeated three times in order to reduce experimental errors. In all tests, roughness was measured using surface roughness tester made by MITITOYO model no.SJ400.The roughness tester having measuring force 075mN-4mN and Diamond tip 5Âµm stylus having accuracy Â±0.03Âµm.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

EXPERIMENTAL CONDITIONS

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. Details of parameters and their levels used shown in the table:

Table 2. Machining parameters and their levels

 Parameter Level 1 Level 2 Level 3 Speed (rpm) 1700 1900 2100 Feed (mm/min) 90 110 130 Coolant flow (lit/min) 20 40 60

EXPERIMENTAL PROCEDURE

High performance HYNDAI WIA f 500D CNC Milling machine (working space, X, Y and Z movements being 600Ã—460Ã—570 mm) variable spindle speeds, optimum 8000 rpm; main spindle power,14.7 kw.having table size 700Ã—500mm was employed to perform experiments for boring rough bar (Material, cemented carbide K20 grade; cylinder shank helix angle 0Â° and chafer 90Â° ).The tool bar having overall length (1) 140 mm; ,flute length (2) 85mm; cutting diameter (d1), 16.04mm and shank diameter (d2), 15.5mm. S/N ratio for Ra was calculated.

Table 3: Experimental design matrix of L9 Orthogonal Array by Minitab 14

 Exp No A Speed (rpm) B Feed (mm/min) C Coolant Flow Rate (Lit/min) 1 1700 90 20 2 1700 110 40 3 1700 130 60 4 1900 90 40 5 1900 110 60 6 1900 130 20 7 2100 90 60 8 2100 110 20 9 2100 130 40

RESULTS AND ANALYSIS

A table is got after actual experimentation, showing Ra values and S/N ratios for each trials. Table is given below:

Table 4 Response table for Ra

 SPEED FEED COOLANT FLOW RATE Ra S/N Ratio 1700 90 20 0.77 2.2710 1700 110 40 1.10 – 0.8278 1700 130 60 1.36 – 2.6707 1900 90 40 0.69 3.2230 1900 110 60 0.76 2.3837 1900 130 20 1.06 – 0.5061 2100 90 60 0.62 4.1521 2100 110 20 0.66 3.6091 2100 130 40 1.13 – 1.0615

Table 5 Analysis of Variance for Means

 Source DF Seq SS Adj SS Adj MS F P Speed 2 0.1 33 0.13 34 0.066 7 55.0 0.018 Feed 2 0.3 79 0.37 94 0.189 7 156. 0.006 Coolant Flow 2 0.0 31 0.01 08 0.015 5 12.8 0.047 Residual Error 2 0.0 02 0.00 24 0.001 2 Total 8 0.5 46

S = 0.0348010 R-Sq = 99.56%

In table 4 Analysis of Variances had been done ( ANOVA) and it reflects that the value P is less than 0.05 in all three parametric sources.Therefore it is clear that all three parameters have significant effect on the surface roughness while boring.

 Level Speed Feed Coolant Flow 1 1.0767 0.6933 0.8300 2 0.8367 0.8400 0.9733 3 0.8033 1.1833 0.9133 Delta 0.2733 0.4900 0.1433 Rank 2 1 3

Table 6 Response Table for Means (smaller is better) for Ra

1.2

1.0

0.8

#### Main Effects Plot (data means) for Means

SPEED FEED

20

40

60

1.2

1.0

0.8

130

110

90

2100

1900

CF

1700

Mean of Means

Fig1.main effect plot for Ra

It is clear form main effect plot as shown in figure

2 that surface roughness is increasing with increasing

inspeed from 1700 rpm to 2100 rpm but it decreases with the change in further increases in speed from 1900 to 2100 rpm. Similarly in the case of feed rate we will get the minimum surface roughness at 90mm/revolution. Minimum value of surface roughness lies at 20 liter/min.

Table 7 Analysis of variance for S/N ratio

 Source D F Seq SS Adj SS Adj MS F P Speed 2 11.71 11.718 5.8594 38.6 0.025 Feed 2 33.47 33.473 16.736 110 0.009 Coolant Flow 2 2.777 2.7779 1.3890 9.17 0.042 Residua l Error 2 0.303 0.3030 0.1515 Total 8 48.27

S = 0.389243 R-Sq = 99.37%

From main effect plot in figure 6.It has been shown that the value of a S/N ratio is maximum at speed of 2100 rpm and minimum at 1700 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.

Taguchi statics for Ra

Firstly data has checked for its normality by probability plot (see figure). As data points are distributed all along the normal line and having negligible outliers, so data can be concluded as normally distributed. The second plot doesn't show any trend while plotting residual verses fitted value of data which implies Taguchi model chosen is well fitted with given data set. Third plot is frequency histogram showing data distribution and at last residue verses order plot highlights the random data points which signifies non-significance of experimental order as far as first response (Ra) is concerned.

Residual Plots for RA MIN

Normal Probability Plot of the Residuals Residuals Versus the Fitted Values

99

Table 8 Response Table for S/N ratio

90

Percent

50

10

 Level Speed Feed Coolant Flow 1 -0.4095 3.2151 1.7911 2 1.7002 1.7217 0.4445 3 2.2332 -1.4128 1.2884 Delta 2.6427 4.6279 1.3465 Rank 2 1 3

1

-0.2

-0.1

0.0

0.1

0.2

0.1

Residual

0.0

-0.1

-0.2

0.6

0.8

1.0

#### Fitted Value

1.2

1.4

Histogram of the Residuals Residuals Versus the Order of the Data

4 0.1

Frequency

Residual

3

0.0

2

1 -0.1

0 -0.2

-0.20 -0.15 -0.10 -0.05 0.00 0.05 0.10

1 2 3 4 5 6

7 8 9

Main Effects Plot (data means) for SN ratios

SPEED FEED

Mean of SN ratios

#### Residual Observation Order

3

2

1

0

-1

1700

1900

CF

2100

90

110

130

20

40

60

Signal-to-noise: Smaller is better

Fig.2 Main effets plot for S/N ratios

Fig.3 Residual plot for Ra mean

CONFORMATION TEST

3

2

1

0

-1

Table 9 Conformation Test

 Sr no. Predicted Ra mean Exp. Trial 1 Exp. Trial 2 Exp. Ra mean % Error 1 0.51 0.478 0.521 0.499 -2.07

CONCLUSION

This study discussed an application of the Taguchi method of optimizing the cutting parameters of boring operation. from this research, following conclusion 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 (F=90 mm/min),the highest cutting speed ( S= 2100 rpm ) and the lowest coolant flow ( C=20 lit/min) lead to the optimal surface roughness value.

for solving machining optimization problems, various conventional techniques had been used so far, but they are not robust and have problems when applied to the boring process , which involves a numbers of variables and constrains. To overcome the above problems, Taguchi method is used in this work. since Taguchi method is experimental method it is realistic in nature. According to this study the prime factor affecting surface finish is feed and after that cutting speed and coolant flow.

REFERENCES

1. Show-Shyn Lin, Ming-TsanChaung, Jeoung-Lian Wen, and Yung- Kaung Yang ''Optimization of 6061T6 CNC Boring Process Using the Taguchi Method and Grey Relation Analysis' The Open Industrial and Manufacturing Engineering, Journal,2009, 2, 14-20

2. Adeel H. Suhail, N. Ismail, S.V.Wong and N.A. Abdul Jalil "Optimization of Cutting Parameters Based on Surface Roughness and Assistance ofWorkpiece Surface Temperature in turning process'' American Journal of Engineering and Applied Sciences 3 (1),pp 102-108,2010

3. Harimansinghsodhi, DhirajPrakashDhiman, Ramesh Kumar Gupta, Raminder Singh Bhatia " Investigation of Cutting Parameters For Surface Roughness of Mild Steel In Boring Process Using Taguchi Method" International Journal OF Applied Engineering Research, ISSN 0973-4562 Vol.7 No.119 (2012)

4. GauravVohra, Palwinder Singh, Harimansinghsodhi '' Analysis and Optimization of Boring Parameters By Using Taguchi Method'' International Journal of Computer Science and Communication Engineering, ISSN 2319-7080, NCRAET-2013

5. Parvesh Kumar Rajvanshi, Dr. R.M. Belokar ''Improving the Process Capability of a Boring Operation by Application of Statistical Techniques' International Journal of Scientific and Engineering Research Volume 3, ISSN 2223-5518, 5 May-2012.

6. M G Mehrabi, G O Neal, B-K Min, Z.Pasek and Y.Koren ''Improving Machining Accuracy in Precision Line Boring'' Journal of Intelligent Manufacturing,13, 379-389, 2002

7. Rang-Tai Yang, Hsin-Te-Ligo, Yung-Kaung Yang, Show-Shyn Lin '' Modeling and Optimization in Precise Boring Processes For Aluminum Alloy 6061T6 Component ''International Journal of Precision Engineering and Manufacturing, Vol 13, No.1, PP 11-16, Jan 2012