30731

Scientific Journal of Impact Factor (SJIF): 4.14

e-ISSN (O): 2348-4470 p-ISSN (P): 2348-6406

International Journal of Advance Engineering and Research Development Volume 3, Issue 2, February -2016

DETERMINATION OF OPTIMAL VALUE OF MILLING PARAMETERS FOR AISI H13 BY TAGUCHI BASED GREY RELATIONAL ANALYSIS *Rahul Kumar, **Sanjeev Verma, ***Ranjeet kumar

**

*Assistant Professor, Department of Mechanical Engineering, SIET Aliyaspur.

Assistant Professor/H.O.D, Department of Mechanical Engineering, PCET Lalrumandi. *** Assistant Professor, Department of Mechanical Engineering, SIET Aliyaspur.

ABSTRACT : Machining conditions play a significant role in governing the performance of machining operations.

It has long been recognized that the machining conditions, such as cutting speed, feed and depth of cut affect the performance of the operation to a greater extent. These parameters should be selected to optimize the economics and quality of machining operations. This can be achieved using design of experiments (DOE).

AISI H13 steel is used in the manufacturing industries for making die casting moulds, extrusion dies, moulds for glass industry, punches, piercing tools, mandrels etc. In the present research paper the machining experiments were conducted on CNC vertical milling machine whose maximum speed was 6000 RPM. Design of experiments based on Taguchi grey relational analysis with three independent factors (cutting speed, feed rate and depth of cut), three levels L27 orthogonal array has been used to develop relationships for predicting surface roughness and metal removal rate. The surface roughness was measured using surface roughness tester (Mitutoyo surftest-4) and the averages were calculated to obtain the surface roughness of the samples. Metal removal rate was calculated using the formula in terms of width of cut, depth of cut and table feed rate. Model significance tests were conducted using ANOVA techniques and effects of various parameters were investigated. Optimization was carried out considering all the input parameters within range so as to get optimum combination of good surface roughness and high metal removal rate. The optimal values obtained are - speed = 3500 RPM, feed = 0.03 mm/tooth and depth of cut = 0.35 mm for general machining conditions.

Key word: CNC Milling, AISI H13, Speed, feed, Depth of Cut , Surface roughness And MRR. 1. INTRODUCTION End milling is one of the important machining operations, widely used in most of the manufacturing industries because complex geometric surfaces with good accuracy and surface finish can be produced by this operation.

However, with the invention of CNC milling machine, the flexibility has been adopted along with versatility in end milling process. In order to build up a bridge between quality and productivity and to achieve the same in an economic way, the present study highlights optimization of CNC end milling process parameters to provide good surface finish and high material removal rate (MRR).

The surface finish of the machined surface refers to quality attribute whereas MRR has been treated as performance index directly related to productivity. Attempt has been made to optimize quality and productivity in a manner that these multi-criterions could be fulfilled simultaneously up to the expected level. Multi-objectives related to quality and productivity has been accumulated to evaluate an equivalent single quality index (called grey relational grade); which has been optimized finally by Taguchi based Grey relational method (Moshat et al. 2010).

2.

EXPERIMENTAL SETUP

CNC End Milling End milling operation was carried out on a BFW SURYA VF 30 CNC VS in dry conditions.

The CNC milling machine equipped with AC variable speed spindle motor up to 6000 rpm and 3.7 KW motor power was used for the present experimental work. The cutter used in this work was end mill with mechanically attached carbide insert having 16 mm diameter.

Cutting Tool Commercially accessible carbide end mill cutter with two cutting flutes is used in this research for end milling.

The cutting inserts used in the machining test was WIDAX PA120 5575 (0.8 mm nose radius) and the cutter used was WIDAX XPHT 09 end milling cutter of diameter 16 mm.

@IJAERD-2016, All rights Reserved

130

International Journal of Advance Engineering and Research Development (IJAERD) Volume 3, Issue 2, February -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406

Figure 1. CNC Milling Machine R & D Centre Ludhuana

Work piece The milling experiments were performed on AISI H13 steel alloy plates. All the plates used in the experimentation were 117 mm in length with 80 mm width and 20 mm in thickness. The chemical composition of AISI H13 was obtained by spectro test and summarized in Table 1.

Table 1, chemical composition of AISI H 13, % weight Element

C

Mn

Si

Cr

Mo

V

P

S

Percentage

0.04

0.36

0.95

5.28

1.25

0.92

0.011

0.101

3.

METHODOLOGY

Taguchi Grey Relational Analysis Experiments are designed using Taguchi method so that effect of all the parameters could be studied with minimum possible number of experiments. Appropriate Orthogonal Array has been chosen and experiments have been performed as per the set of experiments designed in the orthogonal array. Signal to Noise ratios are also calculated for analyzing the effect of machining parameters more accurately. There are 2 Signal-to-Noise ratios of common interest for optimization of static problems used in present study as are: (I) SMALLER-THE BETTER : 1

Ρ = − 10 log n

n 2 i=1 đ?‘Śđ?‘–

(1)

(II) LARGER-THE-BETTER: Ρ = − 10 log

1 n i=1 đ?‘Śđ?‘– 2

(2)

Where, Ρ- Signal to Noise (S/N) Ratio, yi – ith observed value of the response, n - Number of observations in a trial, y - Average of observed values (responses)

@IJAERD-2016, All rights Reserved

131

International Journal of Advance Engineering and Research Development (IJAERD) Volume 3, Issue 2, February -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 Optimization with Taguchi method can be represented by the following flow chart: Normalization of the experimental results of each performance characteristics

Determination of the deviation sequence ∆đ?‘œđ?‘– đ?‘˜ = â•‘đ?‘Ľo (k) Ë— đ?‘Ľi (k) â•‘ ∆đ?‘šđ?‘–đ?‘› and ∆đ?‘šđ?‘Žđ?‘Ľ Calculation of the grey relational coefficient (GRC)

Calculation of the grey relational grade by mean value of GRCs

Analysis of mean (ANOM) response table and response graph for each level of machining

Analysis of variance (ANOVA) to obtain significant parameters and their interactions

Prediction of grey relational grade for optimal levels of machining parameters Figure 2 Flow chart of GRA optimization (Pawade & Joshi, 2011) Design Of Experiments The process control parameters and their levels are given in Table 2. Complete design matrix for performing experimentation is given in Table 2 and corresponding experimental results are summarized in Table 4. This demonstrates a total number of 27 experiments for the complete experimentation.

Table 2 Process control parameters and their levels according to TGRA Parameter Units Symbols Level 1 Level 2 Level3 Speed (rpm) A 3500 4000 4500 Feed (mm/tooth) B 0.01 0.03 0.05 Depth of cut (mm) C 0.20 0.35 0.50 Measurement of Surface Roughness Surface roughness can be defined as the finer irregularities on the surface texture that usually result from the inherent action of the material condition or the machining process. I n the present study, surface roughness of the work pieces after milling was measured by using surface roughness tester (Mitutoyo surftest - 4 Calculation of Metal Removal Rate If W = Width of cut; T = Depth of cutter; V = Cutting speed; N = RPM of Cutter n = Number of Teeth on Cutter; L = Length of pass or cut; đ?‘“đ?‘š =Table(machine) Feed đ??ż đ?‘“đ?‘Ą = Feed/tooth of cutter; D = Cutter Diameter ; đ?‘“đ?‘š = đ?‘“đ?‘Ą Ă— đ?‘ Ă— đ?‘›; CT = đ?‘“ đ?‘š

Metal Removal Rate for end milling is calculated as follows: MRR =

Volume removed Cutting time

=

đ??ż Ă—đ?‘Š Ă— đ?‘Ą đ??śđ?‘‡

= w Ă— t Ă— đ?‘“đ?‘š 4.

RESULTS AND DISCUSSION

This portion presents the analysis of experimental results using Taguchi grey relational analysis (TGRA), analysis of mean (ANOM), analysis of variance (ANOVA) and optimization of milling parameters for optimal combination of better surface roughness (Ra) and good metal removal rate (MRR) under different treatments. The complete results of the 27 experiments performed as per the design matrix are given in Table 3.

@IJAERD-2016, All rights Reserved

132

International Journal of Advance Engineering and Research Development (IJAERD) Volume 3, Issue 2, February -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 Table 3 Experimental design and collected response data Parametric combination (Design of experiment) Expt. No.

Response features

Speed (A) (rpm)

Feed (B) (mm/tooth)

Depth of cut ( C) (mm)

Ra (μm)

MRR (mm3/sec)

1

3500

0.01

0.20

0.446

3.73333

2

3500

0.01

0.35

0.475

6.53333

3

3500

0.01

0.50

0.588

9.33333

4

3500

0.03

0.20

0.531

11.2000

5

3500

0.03

0.35

0.578

19.6000

6

3500

0.03

0.50

0.632

28.0000

7

3500

0.05

0.20

0.688

18.6667

8

3500

0.05

0.35

0.611

32.6667

9

3500

0.05

0.50

0.511

46.6667

10

4000

0.01

0.20

0.298

4.26667

11

4000

0.01

0.35

0.334

7.46667

12

4000

0.01

0.50

0.287

10.6667

13

4000

0.03

0.20

0.321

12.8000

14

4000

0.03

0.35

0.367

22.4000

15

4000

0.03

0.50

0.397

32.0000

16

4000

0.05

0.20

0.417

21.3333

17

4000

0.05

0.35

0.447

37.3333

18

4000

0.05

0.50

0.443

53.3333

19

4500

0.01

0.20

0.178

4.80000

20

4500

0.01

0.35

0.177

8.40000

21

4500

0.01

0.50

0.194

12.0000

22

4500

0.03

0.20

0.243

14.4000

23

4500

0.03

0.35

0.275

25.2000

24

4500

0.03

0.50

0.314

36.0000

25

4500

0.05

0.20

0.187

24.0000

26

4500

0.05

0.35

0.198

42.0000

27

4500

0.05

0.50

0.201

60.0000

Bi-objective optimization (surface roughness and material removal rate taken together) Table 4 S/N ratio calculations for Ra and MRR

Expt. No.

Ra

(μm)

S/N Ratio

MRR

(mm3/Sec)

S/N Ratio

1

0.446

7.013302826

3.73333

11.4419276

2

0.475

6.466127808

6.53333

16.3026919

3

0.588

4.612453478

9.33333

19.40073243

@IJAERD-2016, All rights Reserved

133

International Journal of Advance Engineering and Research Development (IJAERD) Volume 3, Issue 2, February -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 4

0.531

5.498109578

11.2000

20.98436045

5

0.578

4.761443232

19.6000

25.84512143

6

0.632

3.985658434

28.0000

28.94316063

7

0.644

3.822282653

18.6667

25.42135096

8

0.611

4.279175795

32.6667

30.28210528

9

0.511

5.831581997

46.6667

33.38014182

10

0.298

10.51567472

4.26667

12.60178108

11

0.334

9.525070664

7.46667

17.46253915

12

0.287

10.84236207

10.6667

20.56060162

13

0.321

9.869899352

12.8000

22.14419939

14

0.367

8.706678715

22.4000

27.00496037

15

0.397

8.024189865

32.0000

30.10299957

16

0.417

7.597278901

21.3333

26.58116081

17

0.447

6.993849537

37.3333

31.4419276

18

0.443

7.071925476

53.3333

34.53996913

19

0.178

14.99159995

4.80000

13.62482475

20

0.177

15.04053467

8.40000

18.48558572

21

0.194

14.2439654

12.0000

21.58362492

22

0.243

12.28787453

14.4000

23.16724984

23

0.275

11.21334612

25.2000

28.02801082

24

0.314

10.06140704

36.0000

31.12605002

25

0.187

14.56316787

24.0000

27.60422483

26

0.198

14.06669619

42.0000

32.46498581

27

0.201

13.93607885

60.0000

35.56302501

In Taguchi grey relational analysis S/N ratio is use rather than mean value of data. The term signal represents the mean value and the term noise represents the undesirable value for the response variable. There are several S/N calculation methods available depending on type of characteristic; lower is better (LB), nominal is best (NB) and higher is better (HB). For surface roughness (LB) and for MRR (HB) criterion has been used (Equation 1 and 2 respectively). The computed S/N ratios for each quality characteristic are presented in Table 4.

Table 5, Data pre-processing results Response values (normalized) Expt. No. Ra

MRR

1

0.715551035

0.000000000

2

0.764326461

0.201515056

3

0.929563819

0.329952021

4

0.850616039

0.395605253

5

0.916282806

0.597120172

@IJAERD-2016, All rights Reserved

134

International Journal of Advance Engineering and Research Development (IJAERD) Volume 3, Issue 2, February -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 6

0.98543661

0.725557081

7

1.000000000

0.579551714

8

0.959272341

0.781066357

9

0.820890158

0.909503156

10

0.403348039

0.048084607

11

0.491650927

0.249599404

12

0.374226983

0.378037278

13

0.460912744

0.443689257

14

0.564602752

0.645204176

15

0.625440113

0.773641085

16

0.663495147

0.627634512

17

0.71728511

0.829149672

18

0.710325386

0.957586678

19

0.004362063

0.090497423

20

0.000000000

0.292012341

21

0.071006541

0.42044925

22

0.245373356

0.486102354

23

0.341157298

0.687617273

24

0.443841663

0.816054182

25

0.04255269

0.670048172

26

0.086808397

0.871563091

27

0.098451686

1.000000000

It is necessary to normalize the original data before analyzing it with the Grey relation theory or any other methodology. An appropriate value is deducted from the values in the same array to make the value of this array approximately equal to 1. Normalized the S/N ratio values for surface roughness and material removal rate are given in Table 5.

@IJAERD-2016, All rights Reserved

135

International Journal of Advance Engineering and Research Development (IJAERD) Volume 3, Issue 2, February -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 Table 6, Deviation sequence Deviation sequence Expt.

No. 1 3 5 7

2 4 6 8

9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27

Ra 1.0000 0.259201900 0.376990083 0.257692728 0.138351766 0.242770234 0.033592004 0.005314550 0.000000000 0.149965027 0.803761545 0.913592399 0.947713735 1.000000000 0.826414325 0.708319976 0.716755729 0.568788387 0.566482904 0.781790890 0.781790890 0.791126000 0.745600770 0.748548994 0.833017850 0.631440040 0.826414325 0.806954918

MRR 1.0000 1.000000000 0.790854472 0.668512182 0.845866942 0.636721414 0.514379124 0.723524651 0.514379124 0.392036833 0.744342092 0.535196564 0.412854274 0.590291939 0.381146412 0.258721216 0.467866743 0.258721216 0.136378925 0.607963167 0.398817640 0.276475349 0.453935629 0.244710958 0.122342291 0.331487818 0.122342291 0.000000000

The deviation sequence is the absolute difference between the reference sequence and the comparability sequence after normalization.

Calculated Grey relational coefficients and Grey relational grades Case A: General machining W1 = W2 = 0.5 The Grey relational analysis is performed from the data in Table 7 and Table 8, by calculating the Grey relational co-efficient for the normalized S/N ratio values. The value for 両 is taken as 0.5. The results are given in Table 7.

Table 7, Calculated Grey relational coefficients and Grey relational grades for W1= W2 = 0.5

@IJAERD-2016, All rights Reserved

136

International Journal of Advance Engineering and Research Development (IJAERD) Volume 3, Issue 2, February -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 Orthogonal Array

Grey Relational Coefficients

Expt.

No.

MRR

Grade For W1=W2=0.5

Grade Order

A

B

C

Ra

1

1

1

1

2

1

1

2

0.637390095

0.333333333

0.970723428

19

0.679649292

0.385064149

1.064713441

3

1

1

3

18

0.876522242

0.427332903

1.303855145

4

1

2

1

9

0.769960501

0.45273667

1.222697171

11

5

1

2

2

0.856579188

0.553783554

1.410362742

6

6

1

2

3

0.971697578

0.645625375

1.617322953

2

7

1

3

1

1.00000000

0.543213571

1.543213571

5

8

1

3

2

0.924679904

0.695474478

1.620154382

1

9

1

3

3

0.736257921

0.846744577

1.583002497

3

10

2

1

1

0.455933166

0.344372683

0.800305849

25

11

2

1

2

0.495860029

0.39987185

0.895731879

23

12

2

1

3

0.444139265

0.445647605

0.88978687

24

13

2

2

1

0.481191543

0.47334556

0.954537103

20

14

2

2

2

0.534532254

0.584935005

1.11946726

16

15

2

2

3

0.571716137

0.688364925

1.260081061

10

16

2

3

1

0.597725163

0.573154265

1.170879429

13

17

2

3

2

0.638802208

0.745322733

1.384124941

7

18

2

3

3

0.633172184

0.921806267

1.554978451

4

19

3

1

1

0.334305508

0.354735073

0.689040581

27

20

3

1

2

0.333333333

0.413911513

0.747244847

26

21

3

1

3

0.349896633

0.463155623

0.813052256

22

22

3

2

1

0.398524934

0.493146426

0.891671359

21

23

3

2

2

0.431464943

0.61547345

1.046938393

17

24

3

2

3

0.473413865

0.731052061

1.204465926

12

25

3

3

1

0.343065575

0.602444604

0.945510179

15

26

3

3

2

0.353809065

0.795624815

1.149433881

14

27

3

3

3

0.356748315

1.00000000

1.356748315

8

Overall Grey relational grade (GRG) is calculated by averaging the Grey relational coefficient corresponding to selected responses as shown in Table 7. The overall performance characteristic of the multiple response process depends on the calculated GRG. Thus Grey relational approach converts a multiple response optimization problem into a single response optimization problem.

The optimal parametric combination is carried out to obtain the highest Grey relational grade.

Mean effects on overall grey relational grades To calculate mean effects on overall grades average value of GRGs at the desired level is determined. For example the mean effect of A at level 1 is obtained from averaging all 1-9 test runs. The mean effect of other variables at each level can be computed in the same manner.

Table 8, Mean effects on overall grey relational grades for W1= W2 = 0.5 Level 1 2 3

Speed (rpm) 1.370671703 1.114432538 0.982678415

@IJAERD-2016, All rights Reserved

Feed (mm/tooth) 0.9082727 1.19194933 1.367560627

Depth of cut (mm) 1.02095319 1.15979686 1.28703261

137

International Journal of Advance Engineering and Research Development (IJAERD) Volume 3, Issue 2, February -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 Average

1.155927552

1.155927552

1.15592755

Max. -Min.

0.387993288

0.459287928

0.26607942

Percent of Deviation

34.84884184

41.25239494

23.8987632

Rank

2

1

3

Mean effect of parameters are computed and listed in Table 8. As large value of mean GRG is favorable, the optimum set of parameters is 窶連 at first level, B at third level and C at third level respectively (A1 B3 C3).

Grade

Mean effect on grey grades

Speed (rpm)

1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0

Feed (mm/tooth) Depth of cut (mm) level 1

level 2

level 3

Figure 3, Grey relational grades with varying input parameters for W1 = W2 = 0.5 Analysis of variance (ANOVA) Table 9, Results of ANOVA for general machining W1= W2 = 0.5 FACTOR D.F

SUM OF SQUARE

MEAN SQUARES

F-RATIO

PERCENT CONTRIBUTION

F>F TABLE

SPEED (s)

2

0.700669351

0.350334676

31.72718514

33.44436746

Significant

FEED (F)

2

0.966771477

0.483385739

43.77662244

46.14596098

Significant

DEPTH OF CUT (D)

2

0.318794283

0.159397141

14.43540411

15.2166969

Significant

SXF

4

0.006051165

0.001512791

0.13700216

0.288834359

FXD

4

0.014284738

0.003571184

0.323415401

0.681839469

SXD

4

0.000121665

3.041646505

0.002754581

0.005807337

ERROR

8

0.088336781

0.011042098

TOTAL

26

2.095029461

0.080578056

F0.05 (2,8)

=4.459

F0.05 (4,8)

=3.8378

Table 9 shows the summarized results of ANOVA for general machining. It shows that all the three input factors i.e speed, feed and depth of cut are significant factors. The percentage contribution of speed, feed and depth of cut are 33.44 %, 46.14 % and 15.21 % respectively.

5. CONCLUSION The important conclusions drawn from the present work are summarized as follows: 1. Cutting speed is the only significant milling parameter for surface roughness.

2. With the increase in cutting speed surface finish increases (i.e., surface roughness reduces). The surface roughness decreases from level one to level two and subsequently increases to level three with depth of cut, whereas with increase in feed rate the surface roughness increases throughout.

@IJAERD-2016, All rights Reserved

138

International Journal of Advance Engineering and Research Development (IJAERD) Volume 3, Issue 2, February -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 3. In rough machining conditions the most effective parameters in decreasing order are feed rate, depth of cut and cutting speed.

4. Out of three input parameters considered feed rate is identified as the most significant and influential machining parameter followed by cutting speed. Whereas depth of cut has the least effect on surface roughness and MRR for general machining conditions.

5. For finish machining conditions the significant parameters are cutting speed and feed rate.

6. The optimal combination of the milling parameters obtained for maximizing MRR and minimizing surface roughness is the set with A1, B3 and C. REFERENCES

1. Alauddin, M., Baradie M.A.El. & Hashmi, M.S.J. (1996). Optimization of surface finish in end milling INCONEL 718, Journal of Materials Processing Technology, Vol.56, pp.54-65.

2. Brezocnik, M., Kovacic, M. & Ficko. M. (2004). Prediction of surface roughness with genetic programming, Journal of Materials Processing Technology Vol.157–158, pp.28–36.

3. Choudhury, S.K. & Mangrulkar K.S. (2000). Investigation of orthogonal turn-milling for the machining of rotationally symmetrical work pieces, Journal of Materials Processing Technology, Vol. 99, pp.120-128.

4. Fratila, Domnita. & Caizar, Cristian. (2011). Application of Taguchi method to selection of optimal lubrication and cutting conditions in face milling of AlMg3, Journal of Cleaner Production, Vol.19, pp.640-645.

5. Ghani, J.A., Choudhury, I.A. & Hassan, H.H. (2004). Application of Taguchi method in the optimization of end milling parameters, Journal of Materials Processing Technology, Vol.145, pp.84–92.

6. Gopalsamy, Bala Murugan., Mondal, Biswanath. & Ghosh, Sukamal. (2009). Taguchi method andANOVA: An approach for process parameters optimization of hard machining while machine hardened steel, Journal of Scientific & Industrial Research, vol. 68, pp.685–695.

7. Hossain, Shahriar Jahan. & Ahmad, Nafis. (2012). Surface roughness prediction model for ball end milling operation using artificial intelligence, Management Science and Engineering Vol. 6, No. 2, pp.41-54.

8. Kakati, Anjan Kumar., Chandrasekaran, M., Mandal, Amitava. & Singh, Amit Kumar. (2011). Prediction of optimum cutting parameters to obtain desired surface in finish pass end milling of aluminium alloy with carbide tool using artificial neural network, World Academy of Science, Engineering and Technology Vol.57,pp.751-757.

9. Kalpakjian, S. & Schmid, S.R. Manufacturing Engineering & Technology, 5th edition, Prentice- Hall International, 2006.

10. Kolahan, F., Gomezerji, R. & Moghaddam, M.A. (2011). Application of Taguchi method grey analysis and ANOVA in optimization of titanium alloys milling, 3rd International Conference on Manufacturing Engineering ICME2011, Tehran, Iran.

11. Krain, H.R., Sharman, A.R.C. & Ridgway, K. (2007). Optimisation of tool life and productivity when end milling Inconel 718TM, Journal of Materials Processing Technology, Vol.189, pp.153–161.

12. Kuram, Emel. & Ozcelik, Babur. (2013). Multi-objective optimization using Taguchi based grey relational analysis for micro-milling of Al 7075 material with ball nose end mill, Measurement, Vol.46, pp.1849–1864.

13. Kurt, Mustafa., Hartomaclioglu, Selim., Mutlu, Bilçen. & Koklu, Ugur. (2012). Minimization of the surface roughness and form error on the milling of free- form surfaces using a grey relational analysis, Materials and technology, Vol.46, pp.205– 213.

14. Liao, Y.S., Lin, H.M. & Wang, J.H. (2008). Behaviors of end milling Inconel 718 super alloy by cemented carbide tools, Journal of Materials Processing Technology, Vol.201, pp.460–465.

15. Luo, Tao., Lu, Wen., Krishnamurthy, K. & Mcmillin, Bruce. (1998). A neural network approach for force and contour error control in multi-dimensional end.

16. Lou, Mike S., Chen, Joseph C. & Li, Caleb M. (1998). Surface roughness prediction technique for CNC end milling, Journal of Industrial Technology, Vol. 15, No. 1, pp. 1-6.

17. Mahajan, M. Statistical quality control, DHANPAT RAI & CO (P) LTD, 2008.

18. Montgomery, D.C. Design and Analysis of Experiments, edition, John Wiley and Sons, New York 1997.

19. Moshat, Sanjit., Datta, Saurav., Bandyopadhyay Asish. & Pal, Pradip Kumar. (2010). Parametric optimization of CNC end milling using entropy measurement technique combined with grey-Taguchi method, International Journal of Engineering, Science and Technology, Vol. 2, No. 2, pp.1-12.

20. Oktem, Hasan., Erzurumlu, Tuncay. & Erzincanli, Fehmi. (2006). Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm, Materials and Design, Vol.27, pp.735–744.

21. Oktem, H., Erzurumlu, T. & Kurtaran, H. (2005). Application of response surfacemethodology in the optimization of cutting conditions for surface roughness, Journal of Materials Processing Technology, Vol.170, pp.11–16.

22. Pa, Nik Masmiati Nik., Sarhan, Ahmed Aly Diaa. & Shukor, Mohd Hamdi Abd. (2012). Optimizing the Cutting Parameters for Better Surface Quality in 2.5D Cutting Utilizing Titanium Coated Carbide Ball End Mill, International Journal of Precision Engineering and Manufacturing, Vol.13, No. 12, pp.2097-2102.

23. Pare, Vikas., Agnihotri, Geeta. & Krishna, C.M. (2011). Optimization of Cutting Conditions in End Milling Process with the Approach of Particle Swarm Optimization, International Journal of Mechanical and @IJAERD-2016, All rights Reserved

139

International Journal of Advance Engineering and Research Development (IJAERD) Volume 3, Issue 2, February -2016, e-ISSN: 2348 - 4470, print-ISSN: 2348-6406 Industrial Engineering, Vol.1, No. 2, pp.21-25. milling operations, International Journal of Machine Tools & Manufacture, Vol. 38, pp.1343-1359.

24. Selvam, Milon D., Karuppusami, G. & Dawood, A.K.Shaik. (2012). Optimization of machining parameters for face milling operation in a vertical cnc milling machine using genetic algorithm, Engineering Science and Technology: An International Journal, Vol.2, No. 4, pp.544-548.

25. Sharma P.C. (1982). A textbook of production engineering, S. Chand & Co., New Delhi.

26. Shunmugam, M.S., Reddy, S.V. Bhaskara. & Narendran, T.T. (2000). Selection of optimal conditions in multi-pass face-milling using a genetic algorithm, International Journal of Machine Tools & Manufacture, Vol. 40, pp.401-414.

27. Sreenivasulu, Reddy. & Rao, Ch. Srinivasa. (2013). Design of Experiments based Grey Relational Analysis in Various Machining Processes - A Review, Research Journal of Engineering Sciences, Vol. 2, No. 1, pp.21-26.

28. Singh, Baljeet. (2012). Optimization of machining parameters for minimum surface roughness in end milling of EN 353 steel.

29. T, Sadasiva Rao., V, Rajesh. & A, Venu Gopal. (2012). Taguchi based grey relational analysis to optimize face milling process with multiple performance characteristics, International Conference on Trends in Industrial and Mechanical Engineering, March 24-25, 2012 Dubai, pp.166-170.

30. Tzeng, Yih-fong. (2007). A hybrid approach to optimise multiple performance characteristics of high-speed computerised numerical control milling tool steels, Materials and Design, Vol.28, pp.36–46.

31. Wang, Ming-Yung. & Chang, Hung-Yen. (2004). Experimental study of surface roughness in slot end milling AL2014-T6, International Journal of Machine Tools & Manufacture, Vol.44, pp.51–57.

32. Yalcin, Umit., Karaoglan, A.D. & Korkut, Ihsan. (2013). Optimization of cutting parameters in face milling with neural networks and taguchi based on cutting force, surface roughness and temperatures, International Journal of Production Research, Vol. 51, No. 11, pp.3404-3414.

33. Yan, Jihong. & Li, Lin. (2013). Multi-objective optimization of milling parameters e the trade-offs between energy, production rate and cutting quality, Journal of Cleaner Production, pp. 1-10.

34. Yang, Yung-Kuang., Chuang, Ming-Tsan. & Lin, Show-Shyan. (2009). Optimization of dry machining parameters for high-purity graphite in end milling process via design of experiments methods, Journal of Materials Processing Technology, Vol.209, pp.4395– 4400.

35. Yazdi, M.R. Soleymani. & Khorram, A. (2010). Modeling and optimization of milling process by using RSM and ANN methods, International Journal of Engineering and Technology, Vol.2, pp. 474-480.

36. Yih-fong, Tzeng. & Ming-der, Jean. (2005). Dimensional quality optimisation of high-speed CNC milling process with dynamic quality characteristic, Robotics and Computer-Integrated Manufacturing, Vol.21, pp.506–517.

37. Zain, A.M., Haron, H. & Sharif, S. (2010). Application of GA to optimize cutting conditions for minimizing surface roughness in end milling machining process, Expert Systems with Applications, Vol.37, pp.4650–4659.

38. Zain, Azlan Mohd., Haron, Habibollah. & Sharif, Safian. (2010). Prediction of surface roughness in the end milling machining using Artificial Neural Network, Expert Systems with Applications Vol.37, pp.1755–1768.

39. Zarei, O., Fesanghary, M., Farshi, B., Saffar, R. Jalili. & Razfar, M.R. (2009). Optimization of multi-pass face-milling via harmony search algorithm, Journal of Materials Processing Technology, Vol.209, pp.2386–2392.

40. Zhang, Julie Z., Chen, Joseph C. & Kirby, E. Daniel. (2007). Surface roughness optimization in an end-milling operation using the Taguchi design method, Journal of Materials Processing Technology Vol.184, pp.233–239.

@IJAERD-2016, All rights Reserved

140