OPTIMIZATION OF MACHINING PARAMETERS WITH TOOL INSERT SELECTION FOR S355J2G3 MATERIAL USING TAGUCHI AND MADM METHODS

DEPARTMENT OF MECHANICAL ENGINEERINGINDIAN SCHOOL OF MINES DHANBAD

OPTIMIZATION OF MACHINING PARAMETERS WITH TOOL INSERT SELECTION FOR S355J2G3 MATERIAL

USING TAGUCHI AND MADM METHODS

M.Tech Thesis Presentation

&

Presented By

Mr. AVINASH JURIANI

M.tech-Manufacturing

14MT000354

Date:02/05/2016

Dr. Somnath Chhattopadhyay

Associate Professor

Department of Mechanical Engineering

Indian School of Mines, Dhanbad

Mr. Shyam Sundar Mishra

Assistant Manager

Operations Department

JSPL-Machinery Division Raipur

Outline of Presentation

• Introduction

• Literature Review

• Objectives

• Experimentation

• Results & Discussion

• Conclusion & Future Scope

• Contribution

Introduction

• The key goal of modern manufacturing industries is increased productivity & high

quality

• Surface Roughness is major concern for quality aspects affecting performance.

• Speed, Feed & Depth of cut mainly influences SR & MRR in Turning

• Taguchi & Grey Relational Technique is used for optimization followed by ANOVA

for contribution

• MADM is the need for better Tool Insert Selection to get requisite surface finish

Literature Review

S.No. Authors Year Topic Conclusion

1 Vivek Soni et al. 2014 Mathematical Model

prediction for Surface

Roughness &

Material Removal

Rate in Aluminum

Turning in CNC Lathe

Genetic Algorithm

used Showed Speed,

feed rate & Depth of

cut were the best

process parameters for

SR & MRR

2 Vikas et al. 2013 Parameter

Optimization for EN8

Steel Turning in Lathe

Taguchi & ANOVA

were employed to get

the best Parameters &

their Significant effect

on SR & MRR

3 N. V. Patel et al. 2012 Insert Selection for

turning of AISI4340

using MADM

methods

Different inserts were

evaluated using

performance scores &

best insert was selected

4 Navneet Gupta et al. 2011 MADM

implementation

selecting absorbent

layer material for

thin-film solar cells

Many Parameters were

selected as diffusion

length etc.& combined

as such to get Copper

Indium Gallium

Diselinide

Objectives

• Machining of S355J2G3 material

• Studying the effect of turning process parameter on responses

• Identifying the significant factors affecting the performance measures

• Designing the experiment using statistical techniques & analyzing result

• Optimizing the process parameter with respect to responses for turning process

• Implementation of MADM methods and selecting the best possible tool insert

Experimentation

(a) Optimization WorkPiece (b) MADM WorkPiece CNC Lathe PUMA 400 MB

• Chemical Properties

• Mechanical Properties

MaterialC

max

Si

max

Mn

max

P

max

S

max

Cu

max

S355J2G3 0.22 0.55 1.6 0.035 0.035 0.55

Material

Yield

Strength

(N/mm2)

Tensile

Strength

(N/mm2)

Elongation

(%)

Impact Values

Charpy V-Notch

Longitudinal

Hardness

BHN

S355J2G

3315-355 490-630 20 min 27 Joules at -20°C 135 min

WIDAX- PDJNL 2525 M15 -

DNMG 15 06 04 PF (Sandvik)

WIDAX-STFCL 2020 K16 -

TCMT 16 T302 PF (Stellram)

WIDAX- SVJBL 2525 M15 -

VBMT 16 04 04 PF (Widia

Pictorial View with WorkPiece Mounted Tool Cut & Retraction

Calibration Specimen Calibration Photographs

Hardness Measured

Results & DiscussionExperiment

No.

Speed

(m/min)

Feed Rate

(mm/rev)

Depth of Cut

(mm)

SR

(µm)

S/N

(SR)

1 77 0.05 0.5 6.95 -16.84

2 77 0.1 1 5.08 -14.117

3 77 0.15 1.5 4.35 -12.77

4 77 0.2 2 2.07 -6.3194

5 85 0.05 1 1.43 -3.1067

6 85 0.1 0.5 4.73 -13.497

7 85 0.15 2 2.05 -6.2351

8 85 0.2 1.5 4.66 -13.368

9 94 0.05 1.5 1.49 -3.4637

10 94 0.1 2 2 -6.0206

11 94 0.15 0.5 3.39 -10.604

12 94 0.2 1 4.75 -13.534

13 102 0.05 2 6.49 -16.245

14 102 0.1 1.5 3.1 -9.8272

15 102 0.15 1 2.21 -6.8878

16 102 0.2 0.5 2.81 -8.9741

LevelSpeed

(m/min)

Feed Rate

(mm/rev)

Depth of

Cut

(mm)

1 -12.512 -9.914 -12.479

2 -9.052 -10.866 -9.411

3 -8.406 -9.124 -9.857

4 -10.484 -10.549 -8.705

Delta 4.106 1.741 3.774

Rank 1 3 2

For Surface Roughness

• Smaller the better characteristics

From the graph it is concluded that the

optimum combination of each process

parameter for lower surface roughness is

meeting at speed (A3), feed rate (B3) and

depth of cut (C2).

Experiment

No.

Speed

(m/min

)

Feed Rate

(mm/rev)

Depth of

Cut

(mm)

MRR

(mm3/min)

S/N

(SR)

1 77 0.05 0.5 1917.33 65.6539

2 77 0.1 1 7563.3 77.5742

3 77 0.15 1.5 16619.83 84.4125

4 77 0.2 2 28556.78 89.1142

5 85 0.05 1 4241.1 72.5496

6 85 0.1 0.5 4182.2 72.4281

7 85 0.15 2 24504.16 87.7848

8 85 0.2 1.5 23679.5 87.4875

9 94 0.05 1.5 6976.65 76.8729

10 94 0.1 2 17278.58 84.7502

11 94 0.15 0.5 6576.65 76.3601

12 94 0.2 1 17278.58 84.7502

13 102 0.05 2 10084.4 80.073

14 102 0.1 1.5 14631.81 83.306

15 102 0.15 1 14278.34 83.0936

16 102 0.2 0.5 9377.55 79.4418

LevelSpeed

(m/min)

Feed Rate

(mm/rev)

Depth of Cut

(mm)

1 79.19 73.79 73.47

2 80.06 79.51 79.49

3 80.68 82.91 83.02

4 81.48 85.2 85.43

Delta 2.29 11.41 11.96

Rank 3 2 1

From the graph it is concluded that the

optimum combination of each process

parameter for higher material removal

rate is meeting at speed (A4), feed rate

(B4) and depth of cut (C4).

For Material Removal Rate

•Larger the better characteristics

Multi Objective Optimization

Experiment

No.

Data Normalization

Ideal

Sequence

SR MRR

1 0 0

2 0.34 0.21

3 0.47 0.55

4 0.88 1

5 1 0.08

6 0.4 0.09

7 0.89 0.85

8 0.41 0.82

9 0.98 0.18

10 0.89 0.6

11 0.64 0.17

12 0.39 0.57

13 0.08 0.31

14 0.69 0.48

15 0.85 0.46

16 0.75 0.28

𝑥𝑖∗ 𝑘 =

max 𝑥𝑖0 𝑘 − 𝑥𝑖

0 𝑘

ma x 𝑥𝑖0 𝑘 − 𝑚𝑖𝑛 𝑥𝑖

0(𝑘

=6.95 − 5.08

6.95 − 1.43= 0.3387 = 0.34

Experiment

No.

Grey Relation Coefficient

SR MRR

1 0.3333 0.3333

2 0.431 0.3875

3 0.4854 0.5263

4 0.8064 1

5 1 0.3521

6 0.4545 0.3546

7 0.8196 0.7962

8 0.4587 0.7352

9 0.9615 0.3787

10 0.8196 0.556

11 0.5813 0.3759

12 0.4504 0.5376

13 0.3521 0.4201

14 0.6172 0.4901

15 0.7692 0.4807

16 0.67 0.4098

Experiment

No.

Grey Relation

Grade 𝛄𝐢Order

1 0.3333 16

2 0.4093 13

3 0.5045 10

4 0.9032 1

5 0.6761 4

6 0.4046 14

7 0.7944 2

8 0.597 7

9 0.6701 6

10 0.6878 3

11 0.4786 12

12 0.494 11

13 0.3861 15

14 0.5536 8

15 0.6249 5

16 0.5399 9

GRC, ζij k =Δmin+ ςΔmaxΔ0i k + ςΔmax

0.33330.4093

0.5045

0.9032

0.6761

0.4046

0.7944

0.5970.67010.6878

0.47860.4940.3861

0.55360.6249

0.5399

0

0.2

0.4

0.6

0.8

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Av

era

gre

Gre

y R

ela

tio

na

l

Gra

de

Experimental Runs

Graph For Grey Relational Grade

LevelSpeed

(m/min)

Feed Rate

(mm/rev)

Depth of Cut

(mm)

1 -6.077 -6.216 -7.329

2 -4.485 -6.061 -5.361

3 -4.85 -4.649 -4.819

4 -5.775 -4.262 -3.679

Delta 1.593 1.954 -3.65

Rank 3 2 1

Source DF Adj SS Adj MS F-Value

Speed (A) 3 0.02167 0.00722 0.25

Feed Rate (B) 3 0.04317 0.01439 0.5

Depth of Cut

(C)3 0.12652 0.04217 1.48

Error 6 0.17119 0.02853

Total 15 0.36254

From the graph it is concluded

that the optimum combination of

each process parameter for higher

grey relational grade is meeting at

speed (A2), feed rate (B4) and

depth of cut (C4).

Implementation of MADM Methods SAW Method

Speed, 11%

Feed Rate, 22%

Depth of Cut 67%

Pie Chart For Percentage Contribution

Responses/

Levels

Orthogonal Array Grey Theory Design

A2B3C4 A2B4C4

Surface

Roughness 2.05 2

Material

Removal

Rate 24504.16 32672.22

Experi

ment

No.

Nose

Radius

(mm)

Approach

angle

(deg)

Clearance

Angle

(deg)

Rake

Angle

(deg)

Inclination

Angle

(deg)

1 0.4 93 0 -6 -6

2 0.4 93 5 0 0

3 0.2 91 7 0 0

Experi

ment

No.

Nose

Radius

(mm)

Approach

angle

(deg)

Clearance

Angle

(deg)

Rake

Angle

(deg)

Inclination

Angle

(deg)

1 1 1 0 1 1

2 0.5 0.978 1 0 0

3 1 1 0.714 0 0

Weighted Product Method (WPM)

Matrix By Saaty’s Scale

0.51020.26390.12960.06360.0325

0.87020.6427

0.8666

4

2.6962.957

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

1 2 3

Per

form

an

ce S

core

Tool Insert Combination

Comparison of Performance Scores

SAW WPM

Discussions

• Results of GRA are Discussed & Compared

• Optimal Turning Combination is Similar to GRA & ANOVA

• By GRA Exp. 4, 7, 10, 15 nearby SR & 4 & 7 , 10 & 15 nearby MRR

• By GRA Exp. 1 Has High MRR

• By ANOVA for low SR & High MRR DOC contributes more then feed rate & speed

• MADM methods suggests DNMG 15 06 04 PF insert usage As PER SAW & WPM

Conclusions & Future Scope

Conclusions

• Project Aimed at developing Quality Parameters for Heavy Industry Material's

• GRA adopted gives Speed at 85m/min, Feed at 0.2 mm/rev & DOC at 2.0mm

• Optimal SR Came to 97% of initial & MRR increased to 133.33%

• MADM suggested tool insert choice for quality finish reducing Tool wear analysis

Future Scope

• Techniques as Particle Swarm Optimization, Improved Genetic Algorithm can be used

• Many other material's & inserts geometries can also be investigated

Contribution

• This project aided in improvised increase in surface finish with improved productivity

• The material used was finally turned to bush after optimization

• Successful implementation of the material in dynamic condition's proved satisfactory

References

Vivek Soni, Sharif Uddin Mondal and Bhagat Singh, “Process parameters optimization in turning of

Aluminum using a new hybrid approach”, International journal of innovative science engineering &

technology, May (2014), Vol 1, Issue 3, pp. - 418-423.

Navneet gupta, Material selection for thin-film solar cells using multiple attribute decision making

approach, Materials and Design 32 (2011) 1667-167.

Vikas B. Magdum and Vinayak R. Naik, “Evaluation and optimization of machining parameter for

turning of EN 8 steel”, International journal of engineering trends and technology, May (2013),

Volume 4, Issue 5, pp.1564-1568.

N. V. Patel, R. K. Patel, U. J. Patel, B.P. Patel , A Novel Approach for Selection of Tool Insert in CNC

Turning Process Using MADM Methods, International Journal of Engineering and Advanced Technology ,

1(5)(2012) 385-388.

G. Jain, C. P. Patel, A review of effect of insert in hard turning of alloy steel, International Journal

For Technological Research In Engineering, 1(6) 2014.