Application of ANN in FSP

Application of Artificial Neural Network (ANN) in Friction Stir Processing (FSP)
Presented by
Under the guidance of
Dr. Arun Kumar Shettigar
Department of Mechatronics Engineering
Manipal Institute of Technology, Manipal
Yyyy
Department of Mechatronics Engineering
Manipal Institute of Technology, Manipal
March, 2017
Alok Tiwari 130929190
Nihal Shetty 120929138
Vansh Vardhan Jha 130929238

Contents
6/19/2017 Department of Mechatronics Engineering, MIT Manipal 2
 Introduction
 Objectives
 Methodology
 Result and discussion
 Conclusion
 References

Introduction

Introduction
 Aluminium
 Friction Stir Processing (FSP)
 FSP process parameters
 Response Surface Methodology (RSM)
 Artificial Neural Network (ANN)

Introduction
Aluminium
1. 3rd most abundant element on earth (after oxygen and silicon) with 8% by weight.
2. Aluminum has relatively low density (2.7 g/cc as compared to 7.9 g/cc for steel)
3. It has FCC structure.
4. High electrical & thermal conductivity.
5. nonmagnetic and non sparking.
6. Resistance to corrosion
7. It is easy to cast (low melting point).

Introduction
Limitations of Aluminium
1. Chief limitation is its low melting temperature (660 °C).
2. It is very soft, which restricts its application.

Introduction
Aluminium Alloys
Aluminium
(molten)
Fe, Zn, Si, Cu, Mg, Mn
( up to 15% of weight)
Aluminium Alloy
( strength & durability )

Introduction
Aluminium Alloys
Designation
Main alloying
element
Application
1xxx Pure 1100 for Food packaging trays.
1350 for power grids, electrical appliances.
2xxx Cu 2024 for aircraft alloy
3xxx Mn 3004 for Al beverage cans
3003 for heat exchangers
4xxx Si 4043 for welding wire and brazing alloys
5xxx Mg 5005 for architecture
5083 for tanks
6xxx Si, Mg 6061, 6063 for Automobile production, architectural and structural
7xxx Zn 7050, 7075 for Airlines industry

Introduction
FSW FSP
Material 1 Material 2 Material 1
• FSW is a Solid State welding process.
• When we have to join 2 components we use FSW. It’s
joint efficiency is higher than most other Welding
techniques.
• For e.g. we want to make our material corrosion
resistant, 2 options 
1. Coat the material.
2. Make the material hard using FSP.
• We use FSP to modify the microstructure and harden
the upper surface till a specific depth by using the
same FSW principle.
In FSP, a rotating tool with shoulder and pin is plunged into the surface
of material till the shoulder touches the surface of the base material.
This creates frictional heat and dynamic mixing of material area
underneath the tool.
• Material is mixed without changing the phase (by melting or
otherwise) and
• a microstructure with fine equiaxed grains is created.

Introduction
FSP Process parameters

Introduction
FSP parameters
 How to optimize FSP process?
1. By having knowledge of effect of all process variables of FSP.
2. Here we will be looking at the effect of 3 process variables
SR Process parameter Effect on FSP process
1 Tool rotational speed
(ω)
 Determine the amount of heat input in the Stir Zone
 Amount of heat affects the microstructure and resultant properties.
 Tool rotation speed generates the heat.
 Tool traverse speed supplies the amount of heat generated.
2 Tool traverse speed (𝜈
)
3 Tool probe geometry Circular pin Round edges
 Lower tool wear rate than others.
Triangular
pin
It has sharp edges.
 This results in better stirring of materials.
Square pin Produces more pulse/sec. than other pins.
 Produce smaller grains with uniformly distributed fine precipitates
 This in turn yield higher strength and hardness

Introduction
Artificial Neural Network
1. Artificial neural network is a sub category of AI system.
2. ANN technique learns and understands the relationship between the input and output patterns by training
the network.
3. It consists of number of interconnected neurons, where it stores the knowledge in the memory based on the
given set of input and output patterns.

Objectives

Objectives
There are 3 objectives of this project
 Investigation - Investigate the effects of FSP process parameters on the mechanical and metallurgical
properties of AA5052-O
 Prediction - Prediction of surface roughness & mechanical properties (Ultimate tensile strength, Percentage
elongation, Vickers hardness) of FSPed alloys using
 Response surface methodology (RSM)
 Artificial neural network (ANN)
 Optimization – Use Desirability approach to find the optimum range of process parameters

Methodology

Methodology
Methodology can be organised into 3 parts
1. Initialization
2. Experimentation
3. RSM modelling
4. Desirability approach
5. ANN modelling

Methodology

Methodology - initialization
Step 1.1 Selection of base material AA5052 -O
 -O = annealed
 Chemical composition
 Properties
• Good workability,
• medium static strength,
• high fatigue strength, good weldability,
• very good corrosion resistance, especially in marine atmospheres.
• low density and excellent thermal conductivity
Physical properties
Property At Value unit
Density 20oC 2,680 kg/m3
Melting Range 607 –
650
oC
Mean
Coefficient
of Expansion
20oC 23.75 x 10-6 / oC
Thermal
Conductivity
25oC 138 W / m. oC
Electrical
resistivity
20oC 0.050 Micro-
ohm .m
Alloy Si Fe Cu Mn Mg Cr Zn
5052 0.25 0.40 0.10 0.10 2.2 – 2.8 0.15 – 0.35 0.10

Kitchen cabinets
Fig 5 Application of AA5052 -O

Need for FSP on AA5052-O
1. -O means soft material.
2. In some cases we might need
• Upper surface to have more hardness. In these cases
• Lower surface to have lower hardness. We go for FSP.
3. Based on application.
New hardened
upper surface
Lower surface

Step 1.2 Selection of tool material
1. There are some criteria for selection of tool material.
a) It should be stronger than selected base material.
b) It should have a low wear rate.
c) It should be easy to manufacture.
d) No Manufacturing constraint.
2. Hence we select – molybdenum tungsten alloy
a) Molybdenum and Tungsten both are Refractory Metals – [ extraordinary resistance to heat and wear ]
b) Moly alloys - excellent strength, mechanical stability at high temperatures (up to 1900°C).
c) Their high ductility and toughness provide a greater tolerance for imperfections.
d) It has a Vickers hardness of 62 HRC.

Step 1.3 Selection of tool profile
We have selected square pin profile as it
Produces more pulse/sec. than other pins.
 Produce smaller grains with uniformly distributed fine
precipitates
 This in turn yield higher strength and hardness
 Figure 4.1 shows square pin profile with shoulder diameter of
33 mm, pin diameter of 10 mm and 5.7 mm height.
Figure 4.1 Square pin profile

Methodology - experimentation
Step 2.1 identify working range
• Trial experiments have been conducted to determine the working range of the parameters.
• The FSP area should be free from any visible external defects.
Table 4.2 Different combinations tried

Step 2.2 Perform FSP
• Total 9 experiments with 3 tool rotation speeds (700, 1000, 1300) RPM and 3 tool traverse speed (50, 65, 80) mm/min as
shown in Table 4.2, were selected as they had the least visible defects.
Figure 4.1 Friction stir processing setup

Methodology – experimentation
Step 2.3 Specimen cutting
Fig 9 Specimen before
Specimen cutting
Fig 10 Specimen after

Step 2.4 Perform tests
1. Surface roughness
2. Tensile strength
3. Vickers hardness
4. Microstructure

Step 2.4 Preform tests
 Surface roughness
• It is a measure of the finely spaced surface irregularities.
 Significance of roughness
• Rough surfaces usually wear more quickly
• Rough surfaces have higher friction coefficients than smooth surfaces.
• It is a predictor of the performance of a mechanical component, since irregularities in the surface may
form nucleation sites for cracks or corrosion.
• But, roughness may promote adhesion too.
• Measuring roughness – Roughness average Ra
• Ra is the most widely used parameter is the is the arithmetic average of the absolute values.

Surface roughness
Specimen cutting
TEST Specimen used Preparation
Surface
Roughness • The specimen can be used directly.
• Surface measurements was carried
out by using Surtronics 3+.
• In each sample 3 reading were taken
at points A, B and C. The mean was
then used for analysis.

Microstructure
Specimen cutting
TEST Specimen used and Preparation
Microstructure The specimen should be polished so that there are no scratches before we observe it in Scanning Electron
Microscope.
1. Polish
I. Polish the specimen using progressively finer emery paper (grades 100, 600, 1000, 2000) to
get near mirror finish
II. Diamond Polish
2. Etching using Keller's Etchant

Specimen cutting
Step 1 Step 2 Step 3 Step 4 Step 5 Step 6
Use Polishing Machine
RPM – 150-200 RPM
Use velvet cloth
as cover for disc
Drop few
drops of 3μ
diamond
paste
Polish for 5
min in 1
position
Rinse and
dry
Polish for 5 min in a position 90° to the original
Diamond polishing

Specimen cutting
Step 1 Step 2 Step 3 Step 4
Put few drops of Keller’s
etch.
(let bubbles appear)
Rinse it using
running water
Dry it using dryer See the sample in Optical Microscope.
If less scratches and grain boundaries
and orientations visible then OK.
Etching

Vickers Hardness test
Specimen cutting
Vickers
Hardness
• The microstructure specimen was
used for Vickers hardness too.
• Tests were carried out at the cross
section of stir zone on surface
normal to the FSP direction.
• Samples were tested with a load of
100 g and duration of 15s using a
Vickers hardness tester.

Step 2.4 Perform tests
Specimen cutting
Tensile
strength
• The tensile specimens were prepared
as per ASTM: E8/E8M-11 standard,
which was cut along the direction of
FSP.
• The tensile test is carried out on a
computer controlled horizontal
tensometer.

Department of Mechatronics Engineering, MIT Manipal
Tensile test

Methodology – RSM modelling

Step 3.2 Checking for adequacy of model using ANOVA
• The adequacy of the developed RSM model is tested using the analysis of variance (ANOVA) technique.
• Analysis of Variance (ANOVA) is a statistical method used to test differences between two or more means.
• In order to analyse the
• effect of design factors (namely tool rotational speed and traverse speed) and the
• interactions on the experimental data,
analysis of variance is performed at 95% level of confidence.
• Main effect plot was also plotted.

Step 3.3 Confirmation tests
• Confirmation tests are used to check the robustness of the model created.
• The process parameters for Experiments which are already done are fed into the RSM model and the response is
predicted and error calculated.

Step 3.4 Optimizing the process parameters
In order to determine the optimum process parameter values, contour plots and surface plots were used.
1. Contour plot = A contour plot is produced to visually display the region of optimal factor settings. They play very important
role in the study of the response surface.
• Minitab 16 software is used to optimize the process parameters for obtaining the maximum responses (mechanical
properties).
2. Surface plot = 3D wireframe plots are graphs that are used to explore the potential relationship between three variables.
• The predictor variables are displayed on the x- and y-scales, and the response (z) variable is represented by a smooth
surface (3D surface plot) or a grid (3D wireframe plot).

Methodology
Step 4 Desirability approach
The desirability method is a method used for determining the best conditions for process adjustment, making possible
simultaneous optimization of multiple responses.
According to Derringer and Suich (1980), the algorithm will depend on the optimization type desired for response
(maximization, minimization or normalization) of desired limits within the specification and the amounts (weights) of each
one response, which identifies the main characteristics of different optimization types

Methodology - ANN
Input Layer Hidden Layer 1 Output Layer
Tool rotational speed
Tool traverse speed
Tensile strength
Vickers Hardness
Figure 7 Skeleton of proposed ANN
Ultimate Tensile Strength

Result and discussions

Results and discussions
 Macro structure analysis
 Micro structure analysis
 Mechanical properties
 RSM Modelling
 Desirability approach
 ANN modelling

46
1. Macro structure analysis
Top view of FSPed sample
• It can be seen that the surface is shiny, reflective
and is free of visible defects.
• No cracks are presents.
• But, in some samples small “ribbon flashes”
are visible. Defects like “surface lack of fill” and
“surface galling” are also not present.
.

47
1. Macro structure analysis
Side view of FSPed sample
• The macro images of the AA5052-O FS processed with different combinations of traverse speeds of 50, 65, 80 mm/min
and rotational speeds of 700, 1000 and 1300 rpm, using Square Pin Profile have been presented in Table 5.1.
• The nugget region exhibited basin-shape for the all the cases of FSP using Square pin profile.
.
Table 5.1 Macro structural images of AA5052-O FS processed at different rotational speed and
traverse speeds, using square pin profile

2. Micro structure analysis
1. Analysis of base material
Figure 5.2 shows the SEM micrograph of as received AA5052 alloy.
• It shows uniform distribution of grains in all the direction.
• The approximate grain size is around 56±6 micrometer.
• The dark block spots represents the presences of intermetallic phases.
• These sports are Al-Mg phases, which were confirmed during spot
EDAX analysis.
Figure 5.2 SEM micrograph of Base material
Uniform grains
Al-Mg intermetallic phases

Figure 5.4 SEM micrographs of AA5052-O FS processed at(a) 700 RPM and 65 mm/min
(b)1000 RPM and 65 mm/min (c)1300 RPM and 80 mm/min
2. Analysis of FS Processed material

2. Analysis of FS Processed material
Figure shows the micro images of FS processed AA5052-O at different tool rotation speeds and tool traverse
speeds by using SEM.
• It is clear from the images that the grains of the alloys are further refined and uniformly distributed in all the
direction.
• The approximated average grain size is 2±0.5 microns.
• As the rotational speed increases, the grain size increases.
Rotational
speed
Effect Reason
700 RPM No ultrafine grain
Grain is refined
heat generated is not sufficient
Heat generated is sufficient for this.
1000 RPM ultrafine grains. Temperature at nugget zone sufficient.
1300 RPM Grain growth
Ultrafine grain
Temperature produced is more than required.

• Figure (a) = SEM image for analysis.
• Figure (b)-(e) = represent the distribution of
Mg, Al, Cu and Si element in the stir zone,
respectively.
• Figure (f) = peaks presents
in the stir zone.

Department of Mechatronics Engineering, MIT Manipal 52
• 3. Fracture analysis
The fracture surfaces exhibit dimple and tear ridges confirming the ductile fracture.
Figure 5.6 SEM micrograph showing the fracture surface of (a) FS processed at 1000 RPM, 80 mm/min (b) base material
Property Non FSP FSP
Dimples Shallow deeper
No. of dimples Less more
Inference % elongation is less % elongation is more

3. Mechanical properties
Vickers hardness (HV) UTS (MPa) % Elongation
68 HV 195 MPa 25
Table 5.2 Mechanical properties of base material
Property effect
Tool Rotational speed
(RPM)
• Tool rotation speed generates heat.
• Lower rotational speeds resulting in lesser heat generation causes improper mixing,
• High rotational speeds affect grain refinement due to heat input.
Tool traverse speed
(mm/min)
• Tool traverse speed supplies heat.
• Lower traverse speed results in higher quantity of heat supply.
Table 5.2 Mechanical properties of base material

0
1
2
3
4
5
6
7
700.0 1000.0 1300.0
SurfaceRoughness(Ra)
Rotational Speed (RPM)
Surface Roughness
50 65 80
RPM  Ra
mm/min  Ra
Further
mm/min  Ra
Observation
Reason
This is due to increase in the
quantity of heat generated
during process.
.This is due to
• decrease in the quantity of
heat supplied to the processed
region &
• tool advancement is greater
than the previous case.

0
10
20
30
40
50
60
70
80
90
100
700.0 1000.0 1300.0
Vickershardness(HV)
Vickers hardness
50 65 80
0
50
100
150
200
250
300
700.0 1000.0 1300.0
UTS(MPa)
Ultimate tensile strength (UTS)
50 65 80

0
10
20
30
40
50
60
70
80
90
100
700.0 1000.0 1300.0
Vickershardness(HV)
Vickers hardness
50 65 80
0
50
100
150
200
250
300
700.0 1000.0 1300.0
UTS(MPa)
Ultimate tensile strength
(UTS)
50 65 80
Reason
@700 RPM
This is due to decrease in the quantity of heat supplied to the processed
region which resulted in insufficient mixing of the material lead to
decrease in the hardness.
@1000 RPM
This is due to proper generation and supply of heat at pressed region.
Further, increase in the traverse speed resulted in decrease in the
hardness. The reason behind the decrease in the hardness is due to
improper mixing of the material.
@1300 RPM
This is due to increase in the heat generation which results in turbulence
in the material flow and grain growth.
Observation
@700 RPM mm/min HV

30.5
31
31.5
32
32.5
33
33.5
34
34.5
35
700.0 1000.0 1300.0
%EL
Percentage Elongation
50 65 80
Reason
As the rotation speed increases, quantity of heat
generated increases during process.
As the welding speed increases, quantity of heat supplied
decreases.
Observation
RPM  %EL
mm/min  %EL

4. RSM Modelling
RSM model was developed using the experimental data. The following is the analysis of the model.
4.1 Checking the adequacy of the model using ANOVA (analysis of variance)
• In order to analyse the effect of design factors namely rotational speed and welding speed and the interactions on the
experimental data, analysis of variance is performed at 95% level of confidence. The adequacy of the developed model is
tested using the analysis of variance (ANOVA)
• If the calculated value of Fisher ratio of the developed model is less than the standard Fisher ratio (from F-table) value at
a desired level of confidence 95%, then the model is said to be adequate within the confidence level.
• The value of P> F for three developed models is less than 0.05 (95% Confidence level), which indicates that the model is
significant and lack of fit is not significant, as desired. All the above considerations indicate an excellent adequacy of the
regression models.

4. RSM Modelling
4.2 Equations for prediction
All the regression coefficients were estimated and tested by applying “Response surface design” using Minitab 16 software
for their significance at a 95% confidence level. After determining the significant coefficients, the final model was developed
to predict the surface roughness, Vickers hardness, ultimate tensile strength and percentage elongation. The equations are as
follows.
x = rotational speed (RPM)
y = traverse speed (mm/min)
Surface roughness
Vickers hardness
Ultimate tensile strength
Percentage elongation

4. RSM Modelling
Source DF Seq SS Adj SS Adj MS F P
1. Regression 5 15.6456 15.6456 3.12912 151.30 0.000
Linear 2 14.7215 0.81 0.40498 19.58 0.001
x 1 14.6922 0.7027 0.70265 33.98 0.001
y 1 0.0293 0.0314 0.03141 1.52 0.258 insignificant
Square 2 0.8565 0.8565 0.42826 20.71 0.001
x2 1 0.7764 0.4996 0.49964 24.16 0.002
y2 1 0.0801 0.0801 0.08013 3.87 0.090 insignificant
Interaction 1 0.0676 0.0676 0.06760 3.27 0.114 insignificant
xy 1 0.0676 0.0676 0.06760 3.27 0.114 insignificant
2. Residual Error 7 0.1448 0.1448 0.02068
Lack-of-Fit 3 0.1424 0.1424 0.04748 81.87 0.000
Pure Error 4 0.0023 0.0023 0.00058
Total 12 15.7904
Table 5.6 ANOVA for Surface roughness (surface quadratic model)

4. RSM Modelling
Table 5.6 ANOVA for Vickers hardness (surface quadratic model)
1. Regression 5 950.11 950.109 190.022 23.35 0.000
Linear 2 68.33 144.798 72.399 8.90 0.012
x 1 48.17 3.790 3.790 0.47 0.517 insignificant
y 1 20.17 144.778 144.778 17.79 0.004
Square 2 575.53 575.525 287.763 35.56 0.000
x2 1 254.82 62.996 62.996 7.74 0.027
y2 1 320.71 320.710 320.710 39.41 0.000
Interaction 1 306.25 306.25 306.250 37.63 0.000
xy 1 306.25 306.25 306.250 37.63 0.000
2. Residual Error 7 56.97 56.968 8.138
Lack-of-Fit 3 54.97 54.968 18.323 0.002 0.002
Pure Error 4 2.00 2.00 0.500
Total 12 1007.08

4. RSM Modelling
Table 5.6 ANOVA for UTS (surface quadratic model)
1. Regression 5 6282.58 6282.58 1256.52 60.86 0.000
Linear 2 576.15 1058.95 529.48 25.65 0.001
x 1 445.48 2.09 2.09 0.10 0.759 insignificant
y 1 130.67 1009.07 1009.07 48.88 0.000
Square 2 4142.23 4142.23 2071.11 100.32 0.000
x2 1 2100.42 633.05 633.05 30.66 0.001
y2 1 2041.81 2041.81 2041.81 98.90 0.000
Interaction 1 1564.20 1564.20 1564.20 75.77 0.000
xy 1 1564.20 1564.20 1564.20 75.77 0.000
2. Residual Error 7 144.51 144.51 20.64
Lack-of-Fit 3 141.92 141.92 47.31 73.00 0.001
Pure Error 4 2.59 2.59 0.65
Total 12 6427.09

4. RSM Modelling
Table 5.6 ANOVA for % Elongation (surface quadratic model)
1. Regression 5 37.0593 37.0593 7.4119 118.52 0.000
Linear 2 16.0651 8.9745 4.4873 71.75 0.000
x 1 15.1051 8.2143 8.2143 131.35 0.000
y 1 0.9600 0.1308 0.1308 2.09 0.191 insignificant
Square 2 18.8772 18.8772 9.4386 150.93 0.000
x2 1 18.2857 13.4012 13.4012 214.29 0.000
y2 1 0.5914 0.5914 0.5914 9.46 0.018
Interaction 1 2.1170 2.1170 2.1170 33.85 0.001
xy 1 2.1170 2.1170 2.1170 33.85 0.001
2. Residual Error 7 0.4378 0.4378 0.0625
Lack-of-Fit 3 0.4367 0.4367 0.1456 539.12 0.000
Pure Error 4 0.0011 0.0011 0.1456
Total 12 37.4970 0.0003

4. RSM Modelling
4.3 Confirmation tests
• Confirmation tests are used to check the robustness of the model created.
• The process parameters for Experiments which are already done are fed into the RSM model and the
response is predicted.
• The difference between the already Experimentally obtained data and newly predicted data is termed as
Error. The error must be below 10% for the model to be accepted.

4. RSM Modelling
Tool rotation
speed (RPM)
Traverse
speed
(mm/min)
Experimental
Ra
Predicted
Ra
% error
700 50 5.9 5.832235 1.148564407
1300 50 3.07 2.962575 3.49919544
700 80 6.28 6.231901 0.765902866
1300 80 2.93 2.842241 2.995177474
700 65 5.749 5.864844 -2.015028701
1300 65 2.54 2.735184 -7.684409449
1000 50 3.8 3.975181 -4.610018421
1000 80 3.979 4.114847 -3.414106559
1000 65 3.96 3.87779 2.076010101
1000 65 3.92 3.87779 1.076785714
1000 65 3.9 3.87779 0.569487179
1000 65 3.96 3.87779 2.076010101
1000 65 3.96 3.87779 2.076010101
Table 5.10 - Experimental and predicted values of Surface Roughness
Rotational
speed
(RPM)
Traverse
speed(mm/min)
Vickers
Hardness
(HV)
Predicted
Vickers
hardness
% error
700 50 87 87.4303 -0.4946
700 65 85 86.7421 -2.0495
700 80 73 73.2859 -0.3916
1000 50 75 77.6152 -3.4869
1000 65 92 89.2978 2.93717
1000 65 91 89.2978 1.87055
1000 65 92 89.2978 2.93717
1000 65 93 89.2978 3.98086
1000 65 92 89.2978 2.93717
1000 80 79 78.3793 0.7857
1300 50 63 65.2773 -3.6148
1300 65 81 81.2385 -0.2944
1300 80 84 85.3923 -1.6575
Table 5.10 - Experimental and predicted values of Vickers hardness

4. RSM Modelling
Rotational
speed (RPM)
Traverse
speed
(mm/min)
UTS (MPa) Predicted UTS % error for
UTS
% EL Predicted
%EL
% error for
%EL
700 50 265.9 266.6568 -0.2846 35.47 34.698 2.17649
700 65 273.1 271.4069 0.61996 35.53 33.6482 5.29637
700 80 240.2 240.3309 -0.0545 35.8 33.9131 5.27067
1000 50 253.4 252.27 0.44594 32.15 34.142 -6.196
1000 65 286.6 284.3649 0.77987 31.47 32.9348 -4.6546
1000 65 285 284.3649 0.22284 31.44 32.9348 -4.7545
1000 65 285 284.3649 0.22284 31.44 32.9348 -4.7545
1000 65 286.6 284.3649 0.77987 31.47 32.9348 -4.6546
1000 65 286 284.3649 0.57171 31.47 32.9348 -4.6546
1000 80 253.7 260.5652 -2.706 32 32.3814 -1.1919
1300 50 208 210.8641 -1.377 33.88 32.9528 2.73672
1300 65 258.1 258.2252 -0.0485 32.1 32.0123 0.27321
1300 80 261.4 261.3763 0.00907 31.3 31.9026 -1.9252
Table 5.12 Showing UTS and %EL experimental and values predicted using RSM

4. RSM Modelling
4.4 Graphs and analysis
1. Surface roughness
2. Vickers hardness
3. Ultimate tensile strength
4. Percentage elongation

4.4.1 Surface roughness
13001000700
6.0
5.5
5.0
4.5
4.0
3.5
3.0
806550
Rotational speed (RPM)
Mean
Traverse speed (mm/min)
Main Effect plot for Surface roughness (Ra) • The reference line represents the overall mean.
• RPM  line is not ||r to the X axis  affects Ra
• Mm/min  line is almost ||r to X axis  has less
effect.
• However, in the middle (i.e., 60-70 mm/min) there is
mean effect.
• Optimum combination is - 1300 RPM and 65
mm/min.

• The contours are band like.
• It can be inferred that for a Ra of less than 3µm,
• Tool rotation speed =1200 RPM to 1300 RPM
• Welding speed = 55 mm/min to 80 mm/min
Traversespeed(mm/min)
1300120011001000900800700
80
75
70
65
60
55
50
>
–
–
–
–
–
–
< 3.0
3.0 3.5
3.5 4.0
4.0 4.5
4.5 5.0
5.0 5.5
5.5 6.0
6.0
(Ra)
Roughness
Contour Plot for Surface Roughness (Ra)

(RPM) Mm/min Result Reason
a Low Low Highest Ra Tool rotation speed generates heat. This heat leads to localized plastic deformation which in
turn, is responsible for smoother surface.
• Hence, low tool speed less heat generated  less plastic deformation rough
surface
Tool traverse speed supplies the heat generated.
• Less welding speed  more heat supplied.
b Low high
c High Low Very less Ra High amount of heat generated. High amount of heat supplied.
A lot of plastic deformation. Hence, less surface roughness.
d High High Less Ra So, here high heat generated, less amount of heat supplied to the sample (but Welding
speed has less effect on Ra). High heat generated would be more than the optimum, hence,
more roughness
e High Medium Least Ra optimum values.

4.4.2 Vickers hardness and UTS
13001000700
90.0
87.5
85.0
82.5
80.0
77.5
75.0
806550
Mean
Main Effect plot for Hardness (HV)
13001000700
280
270
260
250
240
806550
Mean
Main Effect plot for UTS (MPa)
• The reference line represents the overall mean.
• Both the Parameters have high degree of impact on the Vickers hardness and UTS . For both the parameters, it can be seen that
the graph peeks in the middle and hence the main effect plot showed maxima in the middle.
• For both the parameters, Vickers Hardness & UTS first increases with increase in the value of parameters, but then decreases
it the values are further increased.
• The optimum combination would be 1000 RPM and 65 mm/min.

1300120011001000900800700
80
75
70
65
60
55
50
>
–
–
–
–
–
< 65
65 70
70 75
75 80
80 85
85 90
90
(HV)
Hardness
Contour Plot for Surface Hardness (HV)
1300120011001000900800700
80
75
70
65
60
55
50
>
–
–
–
< 220
220 240
240 260
260 280
280
UTS (MPa)
Contour Plot for UTS (MPa)
• This graph too indicates us that the central region has the maximum hardness when both Rotational Speed and Traverse
Speed are at median.
• It can be observed that the contours are in circular pattern. as compared to the bands like contours of Surface Roughness
• So an optimum range would be from 830-1130 RPM and around 55-70 mm/min.

No general trend can be observed here with respect to the individual parameters.
Tool Rotation Speed Traverse Speed Result
a low low High Hardness
b low high low hardness
c high low Least hardness
d high high High hardness
e median median Highest hardness

4.4.4 Percentage elongation
• The reference line represents the overall mean.
• RPM  not ||r to the X axis  affects %EL
• Mm/min  line is almost ||r to X axis  has less effect
• However, in the middle (i.e., 60-70 mm/min) there is mean
effect
• Observation
• % EL decreases sharply as both the parameters increase,
• but rebounds after a sharp dip which is when both
the parameters have median values.
• optimum for minimum %Elongation would be 1000 RPM
and 65 mm/min.13001000700
36
35
34
33
32
31
806550
Mean
Main Effect plot for Percentage Elongation

• This graph indicates that the
central White region has the least %elongation
when both Rotational Speed and Traverse Speed are
a. at median
b. at high traverse and rotational speeds.
• It can be observed that the contours are in circular
pattern.
1300120011001000900800700
80
75
70
65
60
55
50
>
–
–
–
–
< 32
32 33
33 34
34 35
35 36
36
%EL
Contour Plot for % Elongation

6/19/2017 77
A general trend of decrease in % elongation can be
observed with
• RPM , mm/min  %EL
• however the contribution both the parameters
towards %EL is observed to be different.
• At different points
Tool Rotation
Speed (RPM)
Traverse
Speed (mm/min)
Result
a low low High %EL
b low high High %EL
c high low Less %EL
d high high Least %EL
e median median Least %EL

5. Desirability approach
 Composite Desirability (D)
It is the overall index calculated from combination of each response variables processed through a geometric
mean
• This index is responsible for showing the best condition to optimize all responses variables at the same
time.
• The goal is to obtain the highest possible value for D.
• Our goal is to get, Using Minitab 16’s Response Optimizer,
 minimum  roughness and % Elongation
 maximum UTS and Hardness.

Here Ra has an importance of 0.75. We did 4 iterations. For each response, the importance was varied while keeping others at 1.
Department of Mechatronics Engineering, MIT Manipal 79
Table 5.19 Specifications loaded in Response optimizer

Cur
High
Low0.89014
D
Optimal
d = 0.82391
Minimum
Roughnes
y = 3.1035
d = 0.85161
Maximum
Hardness
y = 88.5482
d = 0.89631
Maximum
UTS (MPa
y = 276.3566
d = 1.0000
Minimum
%EL
y = 31.0861
0.89014
Desirability
Composite
50.0
80.0
700.0
1300.0
TraverseRotation
[1178.7879] [69.3939]Cur
High
Low0.89863
D
Optimal
d = 0.76704
Minimum
Roughnes
y = 3.2855
d = 0.87962
Maximum
Hardness
y = 89.3887
d = 0.92899
Maximum
UTS (MPa
y = 279.3961
d = 1.0000
Minimum
%EL
y = 31.0311
0.89863
Desirability
Composite
50.0
80.0
700.0
1300.0
TraverseRotation
[1130.3030] [68.4848] Cur
High
Low0.88416
D
Optimal
d = 0.80328
Minimum
Roughnes
y = 3.1695
d = 0.86278
Maximum
Hardness
y = 88.8835
d = 0.90934
Maximum
UTS (MPa
y = 277.5683
d = 1.0000
Minimum
%EL
y = 31.0509
0.88416
Desirability
Composite
50.0
80.0
700.0
1300.0
TraverseRotation
[1160.6061] [69.0909]Cur
High
Low0.89318
D
Optimal
d = 0.82391
Minimum
Roughnes
y = 3.1035
d = 0.85161
Maximum
Hardness
y = 88.5482
d = 0.89631
Maximum
UTS (MPa
y = 276.3566
d = 1.0000
Minimum
%EL
y = 31.0861
0.89318
Desirability
Composite
50.0
80.0
700.0
1300.0
TraverseRotation
[1178.7879] [69.3939]
(a) 0.75 importance to Ra (b) 0.75 importance to UTS (c) 0.75 importance to HV (d) 0.75 impt.to %EL
By looking at all the 4 graphs the inferences that can be drawn are
1. When Surface Roughness is given an importance of 0.75, it has
 The maximum overall composite Desirablity
 The maximum UTS
 The minimum %EL
 The maximum Hardness
2. This leads us to select the process parameters mentioned by the 1st approach as the
desirable optimization approach.
3. Hence, 1130.30 RPM and 68.48 mm/min is selected as the process parameter.

6. ANN Modelling
 6.1 Architecture of ANN
Figure 5.14 shows the architecture of the ANN developed.
• 2 inputs (tool rotation speed and traverse speed)
• 2 hidden layers.
• The first hidden layer has 10 neurons while the second hidden layer has 3 neurons.
• 3 outputs, namely, Vickers hardness, UTS and %Elongation.
Figure 5.14 Architecture of the Artificial Neural Network

6. ANN Modelling
 6.2 Process parameters
2 Number of hidden layers 2
3 Number of hidden neurons 13
4 Transfer function used Logsig(sigmoid)
5 Number of patterns used for training 70%
6 Number of patterns used for testing 15%
7 Number of patterns used for validation 15%
8 Number of epochs 1000
9 Learning factor 0.01
10 Momentum factor 0.9
11 Training function Trainscg
12 Max_fail 60
13 Goal 0
14 Time infinity
15 Minimum gradient 0.000001
16 Show iteration till 250
17 sigma 0.00005

83
6. ANN Modelling
 6.3 Training of ANN
Figure shows the training of the Neural Network by 101 epochs, or, iterations.
 Blue line
 Input process parameters of already conducted
experimental data to train the neural network.
 An output graph is created.
 Red line
 same input is again put in the neural network and
ANN’s response is recorded.
 This tells how much the neural network was trained.

84
6. ANN Modelling
 6.3 Training of ANN
Figure shows the training of the Neural Network by 101 epochs, or, iterations.
 Green line
 After training, new experiments were conducted
 now these process parameters were sent as input in order
to compare the Experimental data and the ANN predicted
output response.
 Dotted line
 The dotted line shows the best Validation performance.
 The best performance is at 41 epochs, i.e.,
After 41 iterations of the 9 experimental data the
best results can be found.

6. ANN Modelling
 6.4 Learning using scaled conjugate gradient algorithm
• This graph shows the gradient value. The minimum
gradient we selected was 0.000001. That is not visible
in graph (a). Here the gradient value which we get is
1.7834
• For graph (b) 101 iteration took place.
The best performance was 60 after 101 iterations.

6. ANN training
Rotational
speed (RPM)
Traverse
speed
(mm/min)
UTS
(MPa)
Predicted
UTS
% error % EL Predicted
%EL
% error Hardness
(HV)
Predicted
Hardness
% error
700 50 265.9 266.6568 -0.2846 35.47 34.698 2.17649 87 87.4303 -0.4946
700 65 273.1 271.4069 0.61996 35.53 33.6482 5.29637 85 86.7421 -2.0495
700 80 240.2 240.3309 -0.0545 35.8 33.9131 5.27067 73 73.2859 -0.3916
1000 50 253.4 252.27 0.44594 32.15 34.142 -6.196 75 77.6152 -3.4869
1000 65 286.6 284.3649 0.77987 31.47 32.9348 -4.6546 92 89.2978 2.93717
1000 65 285 284.3649 0.22284 31.44 32.9348 -4.7545 91 89.2978 1.87055
1000 65 285 284.3649 0.22284 31.44 32.9348 -4.7545 92 89.2978 2.93717
1000 65 286.6 284.3649 0.77987 31.47 32.9348 -4.6546 93 89.2978 3.98086
1000 65 286 284.3649 0.57171 31.47 32.9348 -4.6546 92 89.2978 2.93717
1000 80 253.7 260.5652 -2.706 32 32.3814 -1.1919 79 78.3793 0.7857
1300 50 208 210.8641 -1.377 33.88 32.9528 2.73672 63 65.2773 -3.6148
1300 65 258.1 258.2252 -0.0485 32.1 32.0123 0.27321 81 81.2385 -0.2944
1300 80 261.4 261.3763 0.00907 31.3 31.9026 -1.9252 84 85.3923 -1.6575
Table 5.20 Experimental and predicted values

6/19/2017 87
6. ANN Modelling
 6.4 ANN Training
0
50
100
150
200
250
300
350
700/50 700/65 700/80 1000/50 1000/65 1000/66 1000/67 1000/68 1000/69 1000/80 1300/50 1300/65 1300/80
Experimental and Predicted values
UTS (MPa) Predicted UTS % EL Predicted %EL Hardness (HV) Predicted Hardness
Figure 5. 17 Summary of the experimental and predicted values

Conclusions

Conclusions
Objective 1. Investigation of effect of FSP parameters
What we have been able to achieve is
6.37
68
228
25
2.93
93
284
31.3
-54.00313972
36.76470588
24.56140351
25.2
-100 -50 0 50 100 150 200 250 300 350
Ra
HV
UTS
%EL
Effect of FSP on AA 5052-O
%change after FSP original

Conclusions
Optimum RPM Optimum mm/min Contribution of parameters
Ra 1300 (high) 65 (median) Tool rotation speed has highest influence
HV &
UTS
1000 (median) 65-70 (median) Both
%EL 1350 (high)
1000 (median)
80 (high)
60 (median)
Tool rotation speed has highest influence
Objective 2. Build a prediction model
 Using RSM
a. The model developed is robust as it passes ANOVA and Confirmation tests with less than 5% error between the
experimental and predicted values.
• We have been able to pin point
• optimum range of values
• Effect of process parameters

 Using ANN
a. A robust ANN model using Scale conjugate gradient was developed which could be trained in 41
iterations to give a validation performance of 8.866. This model can be used to predict Vickers Hardness,
UTS and %Elongation with high accuracy.

6. ANN modelling
The desirability method is a method used for determining the best conditions for process adjustment, making possible
simultaneous optimization of multiple responses.
According to Derringer and Suich (1980), the algorithm will depend on the optimization type desired for response
(maximization, minimization or normalization) of desired limits within the specification and the amounts (weights) of each
one response, which identifies the main characteristics of different optimization types

Conclusions
Objective 3. Desirability approach
1. For simultaneous responses, we have been able to build a composite desirable model with 0.75 importanc eto Surface
roughness, which would give us
Property Value
Ra 3.2855 µm
UTS 279.39 MPa
HV 89.388 HV
%EL 31.03

Scope for future work
In the FSF process, a modified friction stir welding tool is plunged into the top work piece,
• simultaneously creating frictional heat, stirring, and forming the work material into a new shape.
• The new shape has many possible applications.
Figure 6.1 Illustration of new friction stir processes
Currently, for depositing Monel for corrosion protecting of Steel
• electroplating is done.
• However, this process is very problematic and inconsistent
INSTEAD
In the friction surfacing process, a consumable, usually a softer material is rotated and forced into the stronger substrate
material. As the frictional heat builds, the spinning work material softens and is deposited onto the surface.
• FSD = FSF + friction surfacing.
• The transferred material forms to the shape where it is deposited.
 The future of this technology is the capability to friction stir deposit material in situ for coating, reduction of stress
concentration at joints and welds, crack filling, etc.
.

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