Analysis And Optimization Of Process Parameters During ...A.M.Badadhe etal [6] attempt is made to select the combination of optimum cutting parameters which will result in better surface

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Available online through - http://ijifr.com/searchjournal.aspx Published Online On: November 30, 2015

Copyright©IJIFR 2015

International Journal of Informative & Futuristic Research ISSN: 2347-1697

Volume 3 Issue 3 November 2015 Research Paper

Abstract

Machining industry is playing a crucial role in the present manufacturing scenario and boring is an operation which is quite highly used in these days to enlarge internal holes. As the work piece surrounds the tool, boring is inherently somewhat more challenging than turning in terms of tool holding rigidity and increased clearance angle requirements (limiting the amount of support that can be given to the cutting edge).MRR (Material Removal Rate) plays a significant role in achieving desired production rate and thus increases productivity. The work material used in this process is aluminium Al6061 and tool used is High Speed Steel (HSS). The work is carried out on a Turnmaster 40 conventional Lathe machine. In the present paper, Taguchi design of experiments is used. By Taguchi analysis, optimal setting of process parameters such as speed, feed, depth of cut for response characteristic such as MRR are identified. This Technique uses a special design of orthogonal arrays with only a small number of experiments which in turn reduces the cost and time of experiments. By using Analysis Of Variance (ANOVA), the percentage contribution of each process parameter on the response variable is evaluated.

Analysis And Optimization Of Process

Parameters During Boring Process

Paper ID IJIFR/ V3/ E3/ 065 Page No. 991-1001 Research Area Mechanical

Engineering

Keywords MRR, High speed steel, Turnmaster, Taguchi, ANOVA

1st S.Venkateswarlu

Professor , Department of Mechanical Engineering

G. Pullaiah College of Engineering and Technology Kurnool, Andhra Pradesh (India)

2nd

R.K. Suresh

Assistant Professor Department of Mechanical Engineering

Srikalahastheswara Institute of Technology Srikalahasti, Andhra Pradesh(India)

3rd

P.Dileep

Lecturer Department of Mechanical Engineering

Srikalahastheswara Institute of Technology Srikalahasti, Andhra Pradesh(India)

992

ISSN: 2347-1697 International Journal of Informative & Futuristic Research (IJIFR)

Volume - 3, Issue -3, November 2015 Continuous 27th Edition, Page No.: 991-1001

S.Venkateswarlu, R.K. Suresh, P.Dileep:: Analysis And Optim ization Of

Process Param eters During Boring Process

1. Introduction

The challenge of modern machining industries is mainly focused on the achievements of high

quality in terms of work piece dimensional accuracy, surface finish, high production rate, less wear

on the cutting tool, economy of machining in terms of cost saving and increase the performance of

the product with reduced environmental impact. Material Removal Rate plays an important role in

many areas and is factor of great importance in evaluation Production rate. Boring is the process

whereby a single point cutting tool removes unwanted material from the cylindrical work piece to

enlarge a hole that has been already drilled and the tool is fed parallel to the axis of rotation. It can

be done manually, in a traditional form of lathe, which frequently requires continuous supervision

by the operator or by using a computer controlled machines.

2. Literature Survey

Harsimran Singh Sodhi etal [1] applies Taguchi parameter optimization methodology is

applied to optimize cutting parameters in boring. The boring parameters evaluated are, cutting

Speed, feed rate, and depth of cut, of the material each at three levels. The results of analysis

show that feed rate and cutting speeds have present significant contribution on thesurface

roughness and depth of cut have less significant contribution on the surface roughness.

Pardeep kumar[2] studied the effects of various operational parameters like cutting speed,

feed rate and cutting allowance on bore diameter of engine crankcase tappet bore. It is found

that with the increase of cutting speed and feed, the bore deviation (BD) decreases. The result

of the experiment then was analyzed using DESIGN EXPERT (DOE) 9.0 software. This was

done by using the full factorial technique with optimal (custom) design and ANOVA analysis,

Full factorial with optimal design of three factors with two factor have three levels and one

factor has two level was conducted which consist of 18 runs. The machining responses that

were analyzed is Bore deviation (BD). As speed and feed increased a decrement of Bore

deviation approximately 40 % was observed. By applying RSM analysis, the predictive

mathematical model of the bore deviation average was developed in terms of the cutting

speed, feed rate, and cutting allowance. The error analysis and experimental results indicate

that the proposed predictive mathematical models could adequately describe the performance

indicators within the limits of the factors that are being investigated. In addition, the analysis

of variance (ANOVA) was implemented to identify the significant factors and the response

surface contours were constructed for determining the optimum conditions of finish boring

processes using CNC machine operations.

Show-Shyan Lin[3] investigates the optimization of computer numerical control (CNC)

boring operation parameters for aluminum alloy 6061T6 using the grey relational analysis

(GRA) method. Nine experimental runs based on an orthogonal array of Taguchi method were

performed. The surface properties of roughness average and roughness maximum as well as

the roundness were selected as the quality targets. An optimal parameter combination of the

CNC boring operation was obtained via GRA. By analyzing the grey relational grade matrix,

the degree of influenced for each controllable process factor onto individual quality targets can

be found. The feed rate is identified to be the most influence on the roughness average and

roughness maximum, and the cutting speed is the most influential factor to the roundness.

Additionally, the analysis of variance (ANOVA) was also applied to identify the most

significant factor; the feed rate is the most significant controlled factor for the CNC boring

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operations according to the weighted sum grade of the roughness average, roughness

maximum and roundness.

Gaurav Vohra [4] presents optimization of the boring parameters for a CNC turning centre

such as speed, feed rate and depth of cut is done for aluminium to achieve the highest possible

Material removal rate and at the same time minimum surface roughness by using the taguchi

method. Further the results are signified by using the analysis of variances and optimized

solution is suggested for the process.

Harsimran Singh Sodhi etal[5] The present research work has been done for optimization of

boring parameters i.e. feed rate, speed and depth of cut for steel pipes on a CNC lathe by using

Taguchi method. Carbide tool is used for boring operation. Based on Taguchi Orthogonal

Array L9, a series of experiments were designed and performed on steel pipes. Analysis of

variance, ANOVA, was employed to identify the significant factors affecting the surface

roughness and S/N ratio was used to find the optimal cutting combination of the parameters.

The main response parameters are material removal rate (MRR) and surface roughness. These

parameters depend upon the value of cutting speed, feed rate and depth of cut. All these

control parameters are directly or indirectly co-related with each other. If the depth of cut is

increased then MRR increases, but poor surface finishing is achieved. On the other hand by

increasing the cutting speed, material removal rate and surface finishing improves

simultaneously. It employs that all the parameters are conflicting so we have to select the

optimized parameters for the enhancement of the performance. The optimized results are

found by using ANOVA technique.

A.M.Badadhe etal [6] attempt is made to select the combination of optimum cutting

parameters which will result in better surface finish. Machining with optimum cutting

parameters will result in minimum machining time and hence increasing the productivity. Four

parameters viz. spindle speed, feed, depth of cut and length to diameter (L/D) ratio of boring

bar has been taken as control factors. The cutting trials were performed as per Taguchi 34 (L9)

orthogonal array method to deal with the response from multi-variables. AISI 1041 (EN9)

carbon steel was used as a job material which was cut by using standard boring bars of various

sizes each having a tungsten carbide inserts of same insert radius. The Analysis of Variance

(ANOVA) was carried out to find the significant factors and their individual contribution in

the response function i.e. surface roughness.

Gaurav Vohra etal [7] the present research work has been done for optimization of boring

parameters i.e. feed rate, speed and depth of cut for steel pipes on a CNC lathe by using

Taguchi method. Carbide tool is used for boring operation. Based on Taguchi Orthogonal

Array L9, a series of experiments were designed and performed on steel pipes. Analysis of

variance, ANOVA, was employed to identify the significant factors affecting the surface

roughness and S/N ratio was used to find the optimal cutting combination of the parameters.

The main response parameters are material removal rate (MRR) and surface roughness. These

parameters depend upon the value of cutting speed, feed rate and depth of cut. All these

control parameters are directly or indirectly co-related with each other. If the depth of cut is

increased then MRR increases, but poor surface finishing is achieved. On the other hand by

increasing the cutting speed, material removal rate and surface finishing improves

simultaneously. It employs that all the parameters are conflicting so we have to select the

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optimized parameters for the enhancement of the performance. The optimized results are

found by using ANOVA technique.

2.1 Objectives of the Present Work

The objectives of present work are:

Conducting experimentation by application of orthogonal array for design of experiments and

implementing Taguchi method for finding the effect of cutting parameters.

• Study the effects of material removal rate on the aluminium 6061T6 by considering speed,

feed and depth of cut in different combinations on boring operation.

• To develop a Regression model using MINITAB 16 software and compare with the

experimental material removal rate values.

• To perform a Statistical technique (ANOVA) for finding out the Percentage contribution

of various process parameters on Response variable i.e. material removal rate.

3. Materials and Methods

3.1. Specification of the work piece material

The work material used for the present study is AISI 8620 alloy steel. The chemical composition

of the work material is shown in Table 3.1.

Table3.1: Chemical composition of Al 6061.

Element Si Fe Cu Mn Cr Mg Zn Ti Al

%

composition

0.40-0.80

0.00-0.70

0.15-0.40

0.00-0.15

0.04-0.35

0.80-1.20

0.00-0.25

0.00-0.15

Balance

3.2. Specification of the tool

Company name : ADDISSION S-400

Cutting Material : High Speed Steel (HSS)

Dimensions : 6 X 6 mm2

Length of Cutting Tool : 2 cm

Hardness : 65-67 HRC

3.3. Material removal rate measurement

The MRR can be calculated by considering diameter of the work piece before and after turning of

the work piece.

(mm3/min) Eq. (1)

Where,

L = Boring Length (mm)

N = machine speed in revolutions/minute (RPM)

D1 = finished diameter (mm)

D2 = Initial /smaller diameter (mm)

Fr= machine feed rate (units/revolution)

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4. Experimental Exploration & Interpretations

4.1 Experimentation

The experimentation carried out on a Turnmaster 40 conventional Lathe machine and the details of

measurement of MRR are illustrated in the table 4.1.

Table 4.1: Measurement of Material Removal Rate (mm3/sec)

S. No. Speed

(rpm)

Feed

(mm/rev)

Depth of cut

(mm)

Initial diameter

(mm)

Final diameter

(mm)

MRR

(mm3/sec)

1 180 0.2 0.3 25.62 25.9 6.7979

2 180 0.2 0.4 25.9 26.45 13.5681

3 180 0.2 0.5 27.13 27.63 12.9025

4 180 0.25 0.3 27.63 28.04 13.4449

5 180 0.25 0.4 28.04 28.45 13.6429

6 180 0.25 0.5 28.45 28.96 17.2468

7 180 0.355 0.3 28.96 29.22 12.6528

8 180 0.355 0.4 29.22 29.56 16.7166

9 180 0.355 0.5 29.56 29.91 17.4103

10 280 0.2 0.3 29.91 30.4 21.6627

11 280 0.2 0.4 30.4 30.79 17.4933

12 280 0.2 0.5 31.4 31.9 23.2007

13 280 0.25 0.3 31.9 32.28 22.3470

14 280 0.25 0.4 32.28 32.7 25.0072

15 280 0.25 0.5 32.7 33.12 25.3305

16 280 0.355 0.3 33.12 33.39 23.3655

17 280 0.355 0.4 33.39 33.74 30.5710

18 280 0.355 0.5 33.74 34.1 31.7770

19 400 0.2 0.3 34.1 34.48 27.2904

20 400 0.2 0.4 34.48 34.9 30.5149

21 400 0.2 0.5 34.9 35.4 36.8090

22 400 0.25 0.3 35.4 35.71 28.8557

23 400 0.25 0.4 35.71 36.01 28.1644

24 400 0.25 0.5 36.01 36.52 48.4202

25 400 0.355 0.3 36.52 36.8 38.1599

26 400 0.355 0.4 36.8 37.22 57.7864

27 400 0.355 0.5 37.22 37.6 52.8480

4.2 Results And Analysis

For the corresponding response Material Removal Rate, the signal to noise ratio and means are

calculated by using the formula which is the larger the better value in Taguchi analysis. The results

obtained for response for signal to noise ratio and means is tabulated in table 4.3 and 4.2

respectively are shown below.

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Table 4.2: Response table for means for HSS tool [MRR]

Level Speed(s) Feed(f) Depth of cut(d)

1 13.82 21.14 21.62

2 24.53 24.72 25.94

3 38.76 31.25 29.55

Delta 24.94 10.12 7.93

Rank 1 2 3

Table4.3: Response table for signal to noise ratio larger is better for HSS tool [MRR]

Level Speed(s) Feed(f) Depth of cut(d)

1 22.54 25.57 25.77

2 27.67 27.21 27.36

3 31.44 28.86 28.52

Delta 8.90 3.29 2.75

Rank 1 2 3

Plots for mean effects, S/N ratios, and interaction plots for means and S/N ratios are shown in

figure 4.1, 4.2, 4.3 and 4.4 respectively.

400280180

40

30

20

0.3550.2500.200

0.50.40.3

40

30

20

speed

Mea

n of

Mea

ns

feed

doc

Main Effects Plot for MeansData Means

Figure 4.1: Main effect plot for Means for MRR

400280180

30

28

26

24

22

0.3550.2500.200

0.50.40.3

30

28

26

24

22

speed

Mea

n of

SN

ratio

s

feed

doc

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Larger is better

Figure 4.2: Main effect plot for SN ratios for MRR

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50

30

10

0.50.40.3

0.3550.2500.200

50

30

10

400280180

50

30

10

speed

feed

doc

180

280

400

speed

0.200

0.250

0.355

feed

0.3

0.4

0.5

doc

Interaction Plot for MeansData Means

Fig 4.3: Interaction plot for Means

30

25

20

0.50.40.3

0.3550.2500.200

30

25

20

400280180

30

25

20

speed

feed

doc

180

280

400

speed

0.200

0.250

0.355

feed

0.3

0.4

0.5

doc

Interaction Plot for SN ratiosData Means

Signal-to-noise: Larger is better

Fig 4.4 Interaction plot for SN ratios

4.2 ANOVA for Al 6061 on HSS Tool for the Response MRR

The experimental results analyzed with the Analysis of Variance (ANOVA), which is used to

investigate design parameters significantly affect the quality characteristic. The results of ANOVA

on the Material Removal Rate (MRR) are shown in following Table 4.4. The figure 4.5 illustrates

percentage contribution on speed, feed and depth of cut in MRR.

Table 4.4: ANOVA for the response Metal removal rate [MRR]

SOURCE DOF SUM OF

SQUARES

MEAN SUM OF

SQUARES

F-RATIO %

CONTRIBUTION

SPEED 2 2817.799 1408.9 31.3408 74.8161

FEED 2 473.6516 236.8258 0.1681 12.5760

DOC 2 283.728 141.864 0.5990 7.5333

S*F 4 103.2008 25.8002 0.1819 2.7401

S*D 4 66.3654 16.5914 0.6431 1.7621

D*F 4 21.55708 5.3893 0.3248 0.5724

RESIDUAL 8 359.6329 44.9541 - -

TOTAL 26 100%

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Figure 4.5: % contribution of speed, feed and DOC in MRR

4.3 Analysis of regression for prediction of Material removal rate (MRR)

Regression equation is the best fit equation between the input factors output response.

That is to say the relationship between material removal rate and machining independent

variables (speed, feed and depth of cut) is stated by the following way.

(2)

Where

MRR = Material removal rate in mm3/min

S, F, d = Speed (rpm), Feed (mm/rev) and doc (mm)

a, b, c = constants

In order to facilitate the determination of constants and parameters the mathematical models of

linearized by performing logarithmic transformations shown in equation 3 and 4. Regression

predicted for MRR is determined by performing Regression for the data shown in table 4.5. The

table 4.5 shows the values for experimental and Regression predicted for HSS tool on Aluminium

Al6061 material.

The plot for experimental and Predicted MRR is shown in figure 4.6.

(3)

(4)

75%

13%

7%

3% 2% 0%

CONTRIBURION

S

F

D

S*F

S*D

F*D

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Table 4.5: Response table for predicted and experimental MRR

S.NO. SPEED (rpm)

FEED (mm/rev)

DOC (mm)

EXP.MRR (mm

3/sec)

PREDICTED MRR(mm

3/sec)

S/N RATIO (dB)

1 180 0.2 0.3 6.7979 9.826792 52.21053

2 180 0.2 0.4 13.5681 11.74894 58.21344

3 180 0.2 0.5 12.9025 13.49522 57.77652

4 180 0.25 0.3 13.4449 11.36065 58.13415

5 180 0.25 0.4 13.6429 13.58283 58.26116

6 180 0.25 0.5 17.2468 15.60168 60.2972

7 180 0.355 0.3 12.6528 14.26891 57.60676

8 180 0.355 0.4 16.7166 17.05995 60.02598

9 180 0.355 0.5 17.4103 19.59561 60.37913

10 280 0.2 0.3 21.6627 17.37577 62.27727

11 280 0.2 0.4 17.4933 20.77452 60.42046

12 280 0.2 0.5 23.2007 23.86229 62.87303

13 280 0.25 0.3 22.3470 20.08795 62.54742

14 280 0.25 0.4 25.0072 24.01721 63.52434

15 280 0.25 0.5 25.3305 27.58695 63.6359

16 280 0.355 0.3 23.3655 25.23034 62.93454

17 280 0.355 0.4 30.5710 30.16546 65.26922

18 280 0.355 0.5 31.7770 34.64903 65.60529

19 400 0.2 0.3 27.2904 27.52756 64.28322

20 400 0.2 0.4 30.5149 32.91202 65.25327

21 400 0.2 0.5 36.8090 37.80382 66.8821

22 400 0.25 0.3 28.8557 31.82432 64.76765

23 400 0.25 0.4 28.1644 38.04925 64.55703

24 400 0.25 0.5 48.4202 43.70461 69.26355

25 400 0.355 0.3 38.1599 39.97114 67.19517

26 400 0.355 0.4 57.7864 47.78961 70.79953

27 400 0.355 0.5 52.8480 54.89271 70.02359

Fig 4.6: Plot for experimental and predicted MRR

0

10

20

30

40

50

60

70

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Experimental MRR Predicted MRR

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4.4 Signal factors influence on MRR for the HSS tool on Al 6061 aluminium

The plots consisting of mean effects for S/N Ratio, larger-is-better (S/N Ratio) is selected as an

objective of performance characteristics for minimizing the target MRR of signal factors speed,

feed and depth of cut. Among the machining parameters Spindle speed is the most influence

parameter. For material removal rate

Table 4.6: Optimized table obtained for Al 6061on HSS tool

Control factors Speed(s)rpm Feed (f) mm/rev Doc(d) mm

Material removal rate

(mm3/min)

400 0.355 0.5

5. Conclusions & Future Work

The analyzed results from boring Al 6061 aluminum with HSS tool bar revealed the following

conclusions.

Taguchi is an efficient and systematical methodology for optimizing turning parameters

and can be utilized rather than engineering judgment.

Spindle speed cut is the most influential controlling factor on material removal rate.

The optimal combination of process parameters for maximum material removal rate is

obtained at 400 rpm, 0.355 mm/rev feed and 0.5mm depth of cut.

The ANOVA related that the percentage contribution of spindle speed (74.8161%) is the

dominant parameter followed by feed (12.5760%) then depth of cut(7.5333%) for MRR.

Using the experimental data, a linear regression model is developed and the values

obtained for the responses are compared with experimental values. A graph is plotted

between regression predicted values and an experimentally measured values.

It is observed that the predicted values and experimental values of both MRR are close to

each other.

Future direction of work

i.) While the results declared through this experimental work may be generalized to a

considerable extent while working on MMC (metal matrix composite), the study is limited

to the extreme range of values of the cutting parameters specified. Further work may be

directed towards applying the fine tune optimization of cutting parameters which was

beyond the scope of the present work.

ii.) The work can be expended for multi objective optimization like surface roughness and

tool life, and production cost, production time. The use of genetic algorithm which have

the ability to adapt to the problem being solved under suggested by the evolutionary

process of natural selection.

iii.) The effect of tool vibration, work piece hardness, cutting fluid, nose radius, tool material,

acoustic emission can be considered has they have greater influence on surface roughness

so, including all those along with speed, feed and depth of cut may be selected.

Following is a list of summarizing the future research opportunities in the area of

machining of metal matrix composite.

Efforts should be more to investigate effects of process parameters on various outputs

responses in boring.

Effect of different cooling environment on surface roughness and material removal rate in

boring.

Chip formation analysis in boring.

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6. References [1] Harsimran Singh Sodhi, Dhiraj Prakash Dhiman,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 ,Vol.7 No.11 (2012).

[2] Paradeep kumar, J.S Oberoi, Charanjeet singh, Hitesh Dhiman,”Analysis and Optimization of

ParametersAffecting Bore Deviation in Boring Process, International Journal of innovative

technology and research, Volume No.2, Issue No. 3, pp 967 – 972 (2014).

[3] Show-Shyan Lin, Ming-Tsan Chuang, Jeong-Lian Wen, and Yung-Kuang Yang,”Optimization of

6061T6 CNC Boring Process Using the Taguchi Method ,and Grey Relational Analysis, The Open

Industrial and Manufacturing Engineering Journal , Vol.2, pp 14-20 (2009).

[4] Gaurav Vohra, Palwinder Singh and Harsimran Singh Sodhi,”Analysis and Optimization of Boring

Process Parameters ByUsing Taguchi Method, International Journal of computer science and

communication Engineering , pp 232-237, (2013).

[5] Harsimran Singh Sodhi, Gaurav Vohra,Prediction of Optimized Parameters for CNC Boring Process

usingTaguchi Method for Steel, International Journal of Engineering Technology and Scientific

Research,Vol1,Issue 1, pp 16-22, (2013).

[6] A.M.Badadhe, S. Y. Bhave, L. G. Navale, ”Optimization of Cutting Parameters in Boring

Operation,”IOSR Journal of Mechanical and Civil Engineering , (IOSR-JMCE),pp 10-15.

[7] Gaurav Vohra1, Harsimran Singh Sodhi2, Suneev Anil BansalPrediction of Optimised Parameters for

CNC Boring ProcessUsing Taguchi Method for Steel, International Journal of Mechanical science

and Civil Engineering, pp 30-36.

Documents

Analysis And Optimization Of Process Parameters During ...A.M.Badadhe etal [6] attempt is made to select the combination of optimum cutting parameters which will result in better surface