Multi-Objective optimization and Modeling of AISI D2 Steel using Grey Relational Analysis and RSM approaches under Nano based MQL Hard turning

1. 1
The 2nd International Conference on
Future Learning Aspects of Mechanical Engineering
FLAME 2020 - Online
5th – 7th August 2020
Department of Mechanical Engineering
Amity School of Engineering and Technology, Amity University Uttar Pradesh,
Sector – 125, Noida, India
Title of Paper: Multi-Objective optimization and Modeling of AISI D2
Steel using Grey Relational Analysis and RSM
approaches under Nano based MQL Hard turning
Name of Author(s): Vaibhav Chandra, Dr Andriya Narshimhulu,
Prof Sudarsan Ghosh, Prof . P. V Rao.
Paper Id:674
Name of Presenter(s): Vaibhav Chandra
Affiliation of Presenter: (Delhi Institute of Tool
Engineering, Okhla.)

2. Content
2
1. Introduction and objective of the study
2. Process parameters and technical specification.
3. Flowchart of the optimization.
4. Overall experimental results
5. Conclusions
6. References

3. Introduction and Objective
of study
3
• Hard Turning is now days are very viable method to machine the
automotive components made up of ferrous alloy with hardness
range of 40- 60 HRC.
• It reduces the cost of turning operation as well as setup time of
machining.
• AISI D2 is a high-carbon, high chromium tool steel
• To find out the optimum parameters of machining which is a need of
today manufacturing industries.
• To study the cutting forces and surface roughness as an output
response under the machining of AISI D2 with TiALN PVD coated
carbide insert under nano MQL condition .
• To developed the regression based models which can be used for
predicting the values using RSM approach

4. Process parameters and technical
specification
Fig. Experimental setup
Machine Tool CNC T-6(Leadwell, Taiwan) Turning
centre.
Cutting Insert TiAlN PVD coated inserts, Kennametal
make.
VNMG 160408(SM1105), CNMG
120408(MM1115, KC5025, KC5510,
KC5010)
Tool holder
specification
PCLNL2020K12, MVTNL 12 3B
Cutting Condition MQL: Mist Flow rate 200ml/hr,
Compressed Air Pressure 4 Bar, Twin
Fluid Siphon Nozzle, Distance between
Nozzle and rake face is 50 mm, Nozzle
Angle 45°, Solution in the ratio of 1:10
cutting oil to water added with Nano
Particales (Al2O3)
Having fluid concentration of 0.3 % by
Volume
Cutting
parameters.
Cutting Speed(m/min): 65, 85, 105, 125,
145
Feed Rate(mm/rev/): 0.04, 0.08, 0.12,
0.16, 0.20
Depth of Cut(mm): 0.4, 0.6, 0.8, 1.0, 1.2
Rake Angle(Degree): -10, -6, 1, 7, 14

5. Select machine tool, machining parameters and
performance characteristics
Experimental design using CCD and
conduct the experiments
Measure / calculate the
responses
Normalize the data set
Calculate grey relational
coefficients
Calculate the deviation
sequence
Calculate overall grey
relational grade
Flow chart for implementing the optimization techniques (GRA)
Calculate mean grey
relational grade
Finding of Optimum
parameters 5
min max
ij max
+
+


 
 
ij
1
GC
i
G
m
 
Some standard formulae
used:
1. Normalizing.
2. Grey relational coefficient
3. Grey relational grade.

6. Overall experimental results and optimization data.
6
S.No
Cutting
Speed(V)
Feed
Rate(F)
Depth
of
Cut(t)
Rake
Angle
Cutting
Force(N)
Surface
Roughness(Ra)
Normalize
Data(CF)
Normalize
Data(Ra)
Quality
Loss(CF)
Quality
Loss(Ra)
Grey
Coefficient(CF)
Grey
Coefficient(Ra)
GR
Grade.
1 105 0.12 0.8 1 210 0.26 0.54 0.84 0.46 0.16 0.52 0.76 0.64
2 85 0.16 1 7 280 0.58 0.21 0.27 0.79 0.73 0.39 0.41 0.40
3 125 0.08 1 7 180 0.57 0.68 0.29 0.32 0.71 0.61 0.41 0.51
4 125 0.16 0.6 -6 225 0.25 0.47 0.86 0.53 0.14 0.49 0.78 0.63
5 105 0.12 0.8 1 220 0.25 0.50 0.86 0.50 0.14 0.50 0.78 0.64
6 125 0.16 0.6 7 207 0.22 0.56 0.91 0.44 0.09 0.53 0.85 0.69
7 85 0.16 0.6 -6 258 0.4 0.32 0.59 0.68 0.41 0.42 0.55 0.49
8 85 0.16 1 -6 314 0.66 0.06 0.13 0.94 0.88 0.35 0.36 0.35
9 105 0.12 0.8 1 219 0.28 0.50 0.80 0.50 0.20 0.50 0.72 0.61
10 125 0.16 1 7 287 0.61 0.18 0.21 0.82 0.79 0.38 0.39 0.38
11 85 0.08 1 7 182 0.42 0.67 0.55 0.33 0.45 0.60 0.53 0.57
12 125 0.08 1 -6 187 0.28 0.65 0.80 0.35 0.20 0.59 0.72 0.65
13 125 0.16 1 -6 319 0.65 0.03 0.14 0.97 0.86 0.34 0.37 0.35
14 105 0.12 1.2 1 318 0.73 0.04 0.00 0.96 1.00 0.34 0.33 0.34
15 105 0.12 0.8 1 210 0.25 0.54 0.86 0.46 0.14 0.52 0.78 0.65
16 105 0.12 0.8 1 231 0.27 0.44 0.82 0.56 0.18 0.47 0.74 0.61
17 145 0.12 0.8 1 254 0.34 0.34 0.70 0.66 0.30 0.43 0.62 0.53
18 65 0.12 0.8 1 222 0.43 0.49 0.54 0.51 0.46 0.49 0.52 0.51
19 85 0.16 0.6 7 187 0.33 0.65 0.71 0.35 0.29 0.59 0.64 0.61
20 105 0.12 0.8 1 224 0.28 0.48 0.80 0.52 0.20 0.49 0.72 0.60
21 105 0.2 0.8 1 326 0.32 0.00 0.73 1.00 0.27 0.33 0.65 0.49
22 125 0.08 0.6 -6 172 0.17 0.72 1.00 0.28 0.00 0.64 1.00 0.82
23 105 0.04 0.8 1 112 0.18 1.00 0.98 0.00 0.02 1.00 0.97 0.98
24 85 0.08 0.6 -6 162 0.42 0.77 0.55 0.23 0.45 0.68 0.53 0.60
25 105 0.12 0.8 14 197 0.6 0.60 0.23 0.40 0.77 0.56 0.39 0.48
26 105 0.12 0.8 -10 234 0.28 0.43 0.80 0.57 0.20 0.47 0.72 0.59
27 85 0.08 0.6 7 120 0.5 0.96 0.41 0.04 0.59 03 0.46 0.69
28 105 0.12 0.8 1 230 0.25 0.45 0.86 0.55 0.14 0.48 0.78 0.63
29 105 0.12 0.4 1 157 0.34 0.79 0.70 0.21 0.30 0.70 0.62 0.66
30 125 0.08 0.6 7 118 0.5 0.97 0.41 0.03 0.59 0.95 0.46 0.70
31 85 0.08 1 -6 207 0.35 0.56 0.68 0.44 0.32 0.53 0.61 0.57

7. Mean table for the Grey Relational Grade.
7
Process
Parameters
Mean Grey Relational Grade Rank
Level -2 Level -1 Level 0 Level 1 Level 2 Max-Min
Cutting Speed 0.5058 0.5357 0.6086 0.5934 0.526 0.1028 4
Feed 0.9828 0.5647 0.5747 0.4407 0.4922 0.5421 1
Depth of cut 0.6652 0.6631 0.5722 0.47391 0.3376 0.3255 2
Rake Angle 0.5926 0.5593 0.606 0.5696 0.4758 0.1302 3
Process parameters Optimal prediction Confirmatory
Cutting Forces Fc 120 107
Surface Roughness, Ra 0.20 0.16
GR Grade. 0.920 1.043
Results of confirmation experiments for
multi-response

8. 2D Graph between GR grade and process parameters
8
A:Cutting Speed 0
B:Feed Rate 0
C:Depth of cut 0
D:Effective Rake Angle 0
Hold Values
B:Feed Rate*A:Cutting Speed
130
105
80
0.20
0.15
0.10
0.05
C:Depth of cut*A:Cutting Speed
130
105
80
1.2
1.0
0.8
0.6
0.4
D:Effective Rake Angle*A:Cutting Speed
130
105
80
10
5
0
-5
-10
C:Depth of cut*B:Feed Rate
0.18
0.12
0.06
1.2
1.0
0.8
0.6
0.4
D:Effective Rake Angle*B:Feed Rate
0.18
0.12
0.06
10
5
0
-5
-10
D:Effective Rake Angle*C:Depth of cut
1.00
0.75
0.50
10
5
0
-5
-10
>
–
–
–
–
< -1 .0
-1 .0 -0.5
-0.5 0.0
0.0 0.5
0.5 1 .0
1 .0
GRG
Contour Plots of GRG

9. 9
Regression model developed using RSM for predicting
Cutting Force under Nano MQL Condition
Cutting Force ( Fy) = 219.03 + 2.13 x A + 48.32 x B + 35.34 x C -15.09 x D +
0.81 x AB -0.31 x AC + 3.57 x AD + 8.68 x BC -1.80 x BD
+ 5.15 x CD + 2.00 x A2 -3.74 x B2 + 2.87 x C2 -2.45 x D2
Where A= Cutting speed, B= Feed Rate, C= depth of Cut, D= Effective Rake angle
All the points are close to Regression line All point are within a control limits.

10. 10
Regression model developed using RSM for predicting
Surface roughness under Nano MQL Condition
Surface Roughness ( Ra ) =0.262 - 0.023 x A + 0.031 x B + 0.087 x C + 0.042 x
D + -0.00 x AB + 0.038 x AC + 0.034 x AD + 0.079 x
BC -0.061 x BD -0.0047 x CD + 0.034 x A2 -0.0002 x
B2 + 0.072 x C2 + 0.055 x D2
Where A= Cutting speed, B= Feed Rate, C= depth of Cut, D= Effective Rake angle
All the points are close to Regression line All point are within a control limits.

11. Conclusion.
11
 GRA has turned out to be a good promising tool for the conversion of
multiple objective problems into a single objective problem. So both
optimization techniques are suitable to optimize the multi-objective
function.
The optimal parametric combination are found to be cutting
speed(105m/min), Feed rate(0.04mm/rev), depth of cut(0.4mm) and
effective rake angle(1Degree) by using Grey relational optimization
technique.
Feed and depth of cut found to be most dominating input parameters.
 Further RSM technique also confirms the optimum levels of
parameters through 2D graphs developed with respect to GRG and
input process parameters.
Regression-based models for cutting forces and surface roughness
under Nano based MQL conditions are found to significant in nature.

12. REFRENCES
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13. Thank You
Q&A
13
Contact Details of Presenter:
E-mail, Phone Number etc..