The document summarizes a mid-semester presentation by a student named G. Praveen Kumar. The research focuses on experimental investigations and mathematical modeling of incremental forming processes. The objectives include analyzing surface roughness, formability, form accuracy, springback, and forming forces. Work done so far includes analyzing forming forces using finite element simulations and investigating incremental hole flanging processes. Wavelet analysis techniques are applied to surface roughness data for classification using an artificial neural network model. Results show the proposed wavelet-based methodology achieves higher classification accuracy compared to other methods.

1. BITS Pilani
Hyderabad Campus
MidSemester Presentation: Second Semester (2019-20)
Department of Mechanical Engineering
Name: G. Praveen Kumar ID No. : 2016PHXF0420H
Supervisor Name : Dr. K. Suresh
DAC members: Dr. Pavan Kumar .P and Dr. Nitin .K

2. BITS Pilani
Hyderabad Campus
Experimental investigations and mathematical
modeling of incremental forming processes

3. BITS Pilani, Hyderabad Campus
Contents
3
 Objectives of the Proposed Research
 Work done
 Wavelet analysis
 Results and discussions
 Publications
 References

4. BITS Pilani, Hyderabad Campus
Objectivesoftheproposedresearch
1. Analysis of surface roughness in parts formed by incremental forming.
2. Experimental and theoretical studies on formability in incremental forming.
3. Analysis of form accuracy, spring back and forming forces in incremental forming process.
4. Experimental investigation in incremental hole flanging process.
5. Finite element (FE) simulations of incremental forming.
4

5. BITS Pilani, Hyderabad Campus
Workdone
Analysis of forming force in incremental forming process
Finite element (FE) simulation in incremental hole flanging process
5

6. BITS Pilani, Hyderabad Campus
Digitalimageprocessing–Surfaceroughness
Digital Image processing
Euclidean
distance
Hamming
distance
Wavelet analysis
Waveletanalysis
6

7. BITS Pilani, Hyderabad Campus
Waveletmethod
Wavelet transform implemented in to three types
1. Continuous wavelet transform
2. Discrete wavelet transform
3. Wavelet pocket transform
7

8. BITS Pilani, Hyderabad Campus
DWT decomposes the signal into a pair of low frequency (approximation) and high frequency (detail) coefficients
and the low frequency coefficients are subjected to the next level of decomposition and this process gets iterated.
Thus, DWT uses a set of low pass filter ‘a(q)’ and high pass filter ‘d(q)’ which corresponds to the wavelet ‘ψ(t)’ and
scaling ‘φ(t)’ functions respectively Mathematically,
Where, ‘ψ’ is the wavelet function (mother wavelet), ‘DWT (p,q)’ are the wavelet coefficients of the signal x(t) , ‘p’
is the frequency/scale aspect of x(t), ‘q’ is the time shift/temporal aspect of the mother wavelet and ‘ψ*’ is the complex
conjugate of the wavelet function.
dt
q
t
t
x
q
p
DWT
p
p
)
(
)
(
)
,
( 2
2
*

 



 (Ref 1) (1)
Waveletmethod
8

9. BITS Pilani, Hyderabad Campus
Fig. 1 Wavelet decomposition
Original Image
Detail Coefficients (1)
H1,V1 & D1
Approximation
coefficients (1)
Detail Coefficients (2)
H2,V2 & D2
Approximation
coefficients (2)
Detail Coefficients (3)
H3,V3&D3
Approximation
coefficients (3)
Level 1
Level 2
Level 3
Waveletdecomposition
9

10. BITS Pilani, Hyderabad Campus
Waveletselection
Fig. 2 Magnitude of entropy for various wavelets after three levels of decomposition
10

11. BITS Pilani, Hyderabad Campus
ANNModel
ANN is an adaptive system, the weights of the neurons in the hidden layer are not visible to observe. The output of the ‘ith’
hidden layer is expressed by using eq. (2).
Where, ‘f’ is the activation function that transforms the input/output vectors into limited number of hidden layers
‘wi’ weights/units between the hidden and input nodes,
‘qi’ input values set and ‘s’ is the scalar/bias.
The weights are obtained by iterative training process based on the input and output patterns
In each iteration, the value of MSE is used to adjust the weights between the hidden layers
The training process gets stopped when the required threshold values of MSE are achieved. Back propagation algorithm is
used widely to train the ANN algorithm.
   





n
i
i
i
i
T
i
i
s
f q
w
q
w
p
1
(2)
11

12. BITS Pilani, Hyderabad Campus
Class 1 Class 2 Class 3 Class
34 0 2 Class 1
0 65 1 Class 2
2 2 56 Class 3
Confusion matrix of the ANN model
ANNModel(Contd.,)
12

13. BITS Pilani, Hyderabad Campus
Resultsanddiscussion
Fig. 3 Class wise classification accuracies of various methodologies
13

14. BITS Pilani, Hyderabad Campus
Conclusions
 It was observed that, the classification accuracies of the proposed wavelet based methodology are
higher than the classification accuracies of Hamming and Euclidian distance methods.
 The classification accuracies of wavelet based methodology are found to be in the order of 94.44%,
98.48% and 93.33% for class 1, class 2 and class 3 respectively.
 The overall classification accuracy values of the proposed wavelet based methodology are higher
(95.41%) than the Hamming (78.3%)and Euclidian (80%)distance methods.
 The proposed method has provided the good classification accuracy (maximum error percentage of
4.59%) of surface roughness and has the scope of on–line measurement.
14

15. BITS Pilani, Hyderabad Campus
International Journal:
1. “Experimental studies on incremental hole flanging of steel sheets”. Advances in Materials and
Processing Technologies (2019): 1-11. (Published in Taylor and Francis) (Scopus)
International Conference:
1. “Analysis of formability in incremental forming processes”, 8th international conference on materials
processing and characterization 2018, GRIET, March 17-19 2018, Hyderabad, Telangana, India.
(Published in Materials Today: Proceedings,Elsevier) (Scopus)
2. “Experimental study on forming force measurement for AA 1100 sheets by incremental forming” , 9th
international conference on materials processing and characterization 2019, GRIET, March 8-10 2019,
Hyderabad, Telangana, India. (Accepted)
Publications
12

16. BITS Pilani
Hyderabad Campus
THANKYOU

17. BITS Pilani, Hyderabad Campus
Wavelet transform (WT) is one of the widely executed multi-domain analysis technique in the field of feature
extraction and image processing
WT uses a set of asymmetric basis (mother wavelet) functions to measure the similarity between the signal under
investigation and wavelet function. The similarity between the signal and wavelet is more, the better descriptors
can be extracted.
WT decomposes the signal in a two-dimensional time-frequency domain to achieve the wavelet coefficients.
Therefore, it is capable of providing the temporal information and localize the same in the frequency domain.
This multi-resolution feature enables the application of WT to process the non-stationary and non-
periodic signals

18. BITS Pilani, Hyderabad Campus
Wavelet transform is a mathematical method used to divide a given function into different frequency components and
study each component with a resolution that matches its scale. One important advantage of the wavelet transform
is that better accuracy
Wavelets (t) are, as the name suggests, small waves with a limited range and an oscillatory character.The independent
variable t is sometimes called a spatial variable.
The wavelet transform is generally used for the decomposition and approximation of measurement signals, then,
removal of noise and, finally, the reconstruction of the output signal.
A decomposed image can be further decomposed into multiple sub-levels. This practice is called multiresolution
decomposition

19. BITS Pilani, Hyderabad Campus
]
[
)
(
)
( 2
2
)
1
(
2
/
)
1
(
q
t
q
a
t
p
n
p
p




 

]
[
)
(
)
( 2
2
)
1
(
2
/
)
1
(
q
t
q
d
t
p
n
p
p




 

(5)
(6)
From eq. (1), it is clear that, a suitable wavelet function (mother wavelet) requires
to be selected for implementing the DWT.

20. BITS Pilani, Hyderabad Campus
DWT is implemented to extract the statistical descriptors and the extracted descriptors are subjected to
classification using artificial neural network (ANN) algorithm.
ANN is an adaptive system composed of various neurons and it structures its network based on the learning from the
given input information. Typical ANN structure consists of an input layer, hidden layer and output layer.
Typical ANN structure consists of an input layer, hidden layer and output layer.
The input layer corresponds to the input data (descriptors set) and the output layer represents the targets (classes). The
hidden layer consists of various computational nodes (neurons) and the number of hidden layers and neurons influence
the generalization ability of the network.
ANN is an adaptive system, the weights of the neurons in the hidden layer are not visible to observe. The output
of the ‘ith’ hidden layer is expressed by using eq. (2).

21. BITS Pilani, Hyderabad Campus
The wavelet transform is a mathematical tool that decomposes a signal into a
representation that shows signal details and trends as a function of time. You
can use this representation to characterize transient events, reduce noise,
compress data, and perform many other operation

22. BITS Pilani, Hyderabad Campus

23. BITS Pilani, Hyderabad Campus
WT can be implemented in three ways, continuous wavelet transform (CWT),
discrete wavelet transform (DWT) and wavelet packet transform (WPT). As
CWT and WPT lead to the generation of redundant information and requires
more computational time, the implementation of these techniques is limited,
especially in the applications where the prime focus is on-line/in-process
monitoring [Yan and Gao, 2014]. In contrast, DWT results in lesser
computational time and have many applications in the field of on-line fault
diagnosis, feature extraction and image processing [Radhika et al., 2010,
Vamsi et al., 2019 & Plaza and Lopez, 2018].

24. BITS Pilani, Hyderabad Campus
In this investigation, for each surface roughness class, four features containing
six images are considered. For class 1, six images corresponding to six
different roughness values are synthesized. Therefore, the input parameter
(descriptor) set of class 1 has twelve rows (four from hd, four from vd & 4 from
dd) and 36 columns (6 images x 6 cases). Similarly, the order of the input
data set for class 2 is twelve rows and 66 columns (6 images x 11 cases).
Finally, the input data set of class 3 has twelve rows and 60 columns (6
images x 10 cases). The original input parameter set for the ANN algorithm is
having the order of twelve rows (descriptors) and 162 columns
(observations).

25. Fig. 2 (a) 1/8 scaled model of a Shinkansen bullet train (b) Ford logo using the F3T
technology
25

26. Fig. 1 Applications of ISF: a inner side of a hood for Honda S800 model car [8]; b normal feature lines of TOYOTA iQ
compared with sharpen feature line of TOYOTA iQ-GRMN [8]; c customised ankle support [10] d customised
Buddha face (AMINO website); e sample with 4.5-mm-thickness material of hot rolled steel [8]
26

27. Fig. 3 The manufacture of an ankle support, from request, to scanning of a live subject
(reverse engineering), to setting up a solid model and CAD drawing, to embedding the
shape for toolpath planning, to creating a toolpath for manufacture, to checking for
accuracy
27

28. Fig. 5 Prototypes for Automotive industries
28

29. Fig. 6 Non –Automotive Prototype Examples 29

30. Fig. 7 Titanium cranial implant (right image courtesy of SimiCure).
30

31. Fig. 8 Shapes used to demonstrate the viability of the process and for experiments
31

32. Table 2 Potential applications area of AISF
32

33. 33

34. 34
One-way treatment of contact
One-way contact types allow for compression loads to be transferred between the slave nodes and the
master segments. Tangential loads are also transmitted if relative sliding occurs when contact friction is
active. A Coulomb friction formulation is used with an exponential interpolation function to transition from
static to dynamic friction. This transition requires that a decay coefficient be defined and that the static
friction coefficient be larger than the dynamic friction coefficient. The one-way term in oneway contact is
used to indicate that only the user-specified slave nodes are checked for penetration of the master
segments. One-way contacts may be appropriate when the master side is a rigid body, e.g., a punch or die
in a metal stamping simulation. A situation where one-way contact may be appropriate for deformable
bodies is where a relatively fine mesh (slave) encounters a relatively smooth, coarse mesh (master). Other
common applications are beam-to-surface or shell-edge-to-surface scenarios where the beam nodes or
the shell edge nodes, respectively, are given as the slave node set. There are a number of keyword
options that activate one-way contact.

35. 35
ELFORM:=ELFORM Element formulation
options:
EQ.1: Hughes-Liu,
EQ.2: Belytschko-Tsay (default),
EQ.3: BCIZ triangular shell,
contd…

36. 36