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

1. DEPARTMENT OF MECHANICAL ENGINEERING
INDIAN 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

2. Outline of Presentation
• Introduction
• Literature Review
• Objectives
• Experimentation
• Results & Discussion
• Conclusion & Future Scope
• Contribution

3. 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

4. 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

5. 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

6. 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

7. Experimentation
(a) Optimization WorkPiece (b) MADM WorkPiece CNC Lathe PUMA 400 MB
• Chemical Properties
• Mechanical Properties
Material
C
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
3
315-355 490-630 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

8. Pictorial View with WorkPiece Mounted Tool Cut & Retraction
Calibration Specimen Calibration Photographs
Hardness Measured

9. Results & Discussion
Experiment
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
Level
Speed
(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).

10. 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
Level
Speed
(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

11. 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 𝑥𝑖
0
𝑘 − 𝑥𝑖
0
𝑘
ma x 𝑥𝑖
0
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 𝛄𝐢
Order
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+ ςΔmax
Δ0i k + ςΔmax

12. 0.3333
0.4093
0.5045
0.9032
0.6761
0.4046
0.7944
0.597
0.67010.6878
0.47860.494
0.3861
0.5536
0.6249
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
AveragreGreyRelational
Grade
Experimental Runs
Graph For Grey Relational Grade
Level
Speed
(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).

13. 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

14. Weighted Product Method (WPM)
Matrix By Saaty’s Scale
0.5102
0.2639
0.1296
0.0636
0.0325
0.8702
0.6427
0.8666
4
2.696
2.957
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1 2 3
PerformanceScore
Tool Insert Combination
Comparison of Performance Scores
SAW WPM

15. 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

16. 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

17. 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

18. 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.