8/4/2019 Optimization of Influence of Process Parameter on CNC Turning for 21-4N Material

1/4

First National Conference on PRODUCT ENGINEERING (NCPE 2010), 3rd 4th December 2010,

S. G. G. S. Institute of Engineering & Technology, Nanded (MS), INDIA

Optimization of Influence of Process Parameter on CNC Turning for 21-4N

Material

Pramod R.Wadate1, Sidharth P.Chandurkar2, Ravi M.Warkhedkar31,2,3 Department of Mechanical Engineering,

Government College of Engineering, Aurangabad, Maharashtra-431005, India.

(pramodwadate@yahoo.com)

Abstract - This paper describes the multiresponse

optimization technique to predict and select the best

cutting parameters of Computer Numerical Control

(CNC) process. To predict the performance

characteristics namely material removal rate and

surface roughness, Tauguchi method is implemented

to predict most influential factor. 21-4N was selected

as work material to conduct experiments. A Tungsten

carbide tool bit of 25mm diameter was applied as tool

insert of TNMA K10 to cut the specimen.

Experiments were planned as per Taguchis L9

orthogonal array. Experiments were performed under

different cutting conditions of material removal rate,

depth of cut, feed, and speed. The responses were

optimized concurrently using multiresponse signal-

to-noise (MRSN) ratio in addition to Taguchis

parametric design approach. Analysis of variance

(ANOVA) was employed to identify the level of

importance of the machining parameters on the

multiple performance characteristics. Finally,

experimental confirmations were carried out to

identify the effectiveness of this proposed method. Agood improvement was obtained.

Keywords 21-4N, Taguchi, Anova, Matlab

1. INTRODUCTION

Computer Numerical Control (CNC) process

plays a major role in some manufacturing

sectors recently. This process has the capacity to

cut complex and intricate shapes of components

in all conductive materials with better precision

and accuracy. In this operation, a liquid coolantmedium is continuously passed in the gap

provided between the tool bit and work piece.

Objective of this paper is to optimize the processparameter of CNC Turning process for 21-4N

materials which is one of the high hardened

alloys steel used basically for engine valve,

crank shaft and knuckle joint. Fatigue behavior

of turned surfaces always depends on correct

combination of speed, depth of cut and feed

which are the parameters to be optimized.

2. EXPERIMENTAL PROCEDURE

Table No.1 shows the chemical composition of

21-4N material. From this we can understand

that, this particular material is one of the high

hardened materials.

ACE CNC is one of the automatic and computer

numerical control machine. The following are

the process parameter is used in turning process

for 21-4N material which is to be optimized,

1. Speed limit between 1600rpm to 2000 rpm

2. Feed limit between 0.06 mm/rev to

0.1mm/rev

3. Depth of cut limit between 0.02mm to .

06mm

Here the output response is surface finish up to

0.2 Ra. Experiments were planned as per

Taguchis L9 orthogonal array which shown in

Table 2,

2.1 Finding out signal to noise ratio

The formula under this criterion is given by

S/NHB = -10 log (1/r 1 / Yi2)

In the above equations

Vm = SSm

SST= Yi2

Table1. CHEMICAL COMPOSITION OF 21-4N

8/4/2019 Optimization of Influence of Process Parameter on CNC Turning for 21-4N Material

2/4

First National Conference on PRODUCT ENGINEERING (NCPE 2010), 3rd 4th December 2010,

S. G. G. S. Institute of Engineering & Technology, Nanded (MS), INDIA

Element C Si Mn S P Ni Cr Mo Fe

Specification %0.35

0.45

1.80

2.50

0.60

Max

0.030

Max

0.030

Max

0.60

Max

10.00

12.00

0.70

1.30

Bal

ance

Observation %

Valve No. 01 0.41 0.21 0.018 0.006 0.31 10.41 0.74 85.91

Observation %

Valve No. 020.40 1.92 0.23 0.023 0.009 0.29 10.34 0.73 85.86

.

Table2. L9 Orthogonal array

Levels P1 P2 P3 Trail 1 Trail 2 Trail 3 Mean

S/N

Ratio

1 1600 0.06 0.02 50 51.3 51.1 50.8 37.21

2 1600 0.08 0.04 55 55.5 56 55.5 40.9

3 1600 0.1 0.06 58.6 59 58.7 58.77 40.26

4 1800 0.06 0.04 54 55.2 54.9 54.7 38.8

5 1800 0.08 0.06 55.9 56.1 57 56.33 39.64

6 1800 0.1 0.02 56 57.1 57.5 56.87 37.28

7 2000 0.06 0.06 56.1 58 57 57.03 35.56

8 2000 0.08 0.04 58.8 57.7 59 58.5 38.44

9 2000 0.1 0.02 56.5 56.1 57 56.53 41.92

Figure 1: Signal to Noise Ratio Curve for Higher is Better

8/4/2019 Optimization of Influence of Process Parameter on CNC Turning for 21-4N Material

3/4

First National Conference on PRODUCT ENGINEERING (NCPE 2010), 3rd 4th December 2010,

S. G. G. S. Institute of Engineering & Technology, Nanded (MS), INDIA

3. PERFORMANCE ANALYSIS

Since there are three process parameters and three

levels, we have chosen L9 orthogonal array in

Taguchi. Experimental frame work done in

ANOVA from the pie chart. Most influential

process parameter is feed rate and depth of cut

which plays the minor effect in turning process

while machining the 21-4N material. Following are

the mathematical modeling used during execution,

Y xn

Taking log on both sides,

log y=nlogx1

Since we have three process parameters, therefore,

logy=nlogx3

By adding the above equations

3logy=n1logx1+n2logx2+n3logx3

By applying logy=Yand log x=Xwe will get,

3Y=n1X1+n2X2+n3X3

This is objective function. As a part of constraint

equation surface finish must lie between 0.1 to 0.8

Ra so,

n1X1+n2X2+n3X3 0.1 Ra,

n1X1+n2X2+n3X3 0.8Ra

Figure 2: Effect of Depth of Cut on Roughness

Figure 3: Effect of Speed on Roughness

Figure 4: Effect of feed on Roughness

4. RESULT AND DISCUSSION

Taguchi method is proved as effective method

for finding out the most influential factor. This

particular problem is solved in MATLAB

nonlinear program with inequality constraint,

and the average error is 5.5%. Reason for error

is influence of uncontrollable factor in

8/4/2019 Optimization of Influence of Process Parameter on CNC Turning for 21-4N Material

4/4

First National Conference on PRODUCT ENGINEERING (NCPE 2010), 3rd 4th December 2010,

S. G. G. S. Institute of Engineering & Technology, Nanded (MS), INDIA

machining process and human error while

unloading the product in NC.

Figure 5: Pie chart: Result of Anova

From ANOVA most influencial factor is

feed and less influence factor is depth of cut

which is proved both Taguchi as well asANOVA.From the above description it can be

justified that experimental analysis includes

process elements and computational analysis

by matlab, the difference between matlab value

and experimentation value is due to

uncontrollable factor and also the effects of

interaction of parameters was neglected in thedesign of experimentations. We can modify the

mathematical model by incorporating the

suitable constant, which can be obtained from

the experimentations.

REFERENCES

1. R. Ramakrishnan, L. Karunamoorthy, 2008,

Modeling and multi-response optimization

of Inconel 718 on machining of CNC

WEDM process. InternationalJournal of

Materials Processing Technology 2 0 7,

343349.

http://elsevier.com/locate/jmatprotec

2. Engineering manual Vol.2, Valve Industry34-36.

3. R.Palanivasan and R.M.Warkhedkar ,2010,

Optimizing Influence of Process Parameter

of Induction Hardening on IC Engine Valve.

http://www.indjst.org

4. Ramakrishnan, R., Karunamoorthy, L.,2006.

Multi-response optimization of wire EDM

operations using robust design of

experiments. International Journal of

AdvancedManufacturing Technology 29,

105112.

5. Chorng-Jyh Tzeng, Yu-Hsin Lin, Yung-

Kuang Yang A., Ming-Chang Jeng.Optimization of turning operation with

multiple performance characterstics usingthe Taguchi method and Grey relational

analysis.International Journal of Advanced

Manufacturing Technology, 2753-2759.

6. Asan E.,Camuscu N., Birgoren B.,2007.Design optimization of cutting parameters

when turning hardened AISI 4140 steel (63

HRC) With Al2O3TiCN mixed ceramic

tool. Des.28, 1618-1622.

http://elsevier.com/locate/jmatprotechttp://www.indjst.org/http://www.indjst.org/http://elsevier.com/locate/jmatprotechttp://www.indjst.org/