Assessing the quality of spot welding electrode’s
tip using digital image processing techniques
A .A. Abdulhadi
Coherent and Electro

Optics Research Group
GERI
Presentation headlines
Resistance spot welding
Effects of increased electrode’s diameter
Assessing the quality of the electrode
automatically
Flat tip
Doom tip
Building a system that assess the quality of welding
electrode automatically.
Future work
Resistance spot welding
Resistance spot welding is a quick and
easy way to join two materials
Two electrodes are used to perform
spot welding ; they are placed either
side of the surfaces to be welded
The functions of the two electrodes
are
1) clamping of the work
2) applying the weld force required for
welding
3) applying the weld current necessary
for fusion of the work pieces
4) a final retraction of the electrodes
after the molten nugget has solidified
Effects of increased electrode diameter
Diameter high, area high
Resistance low
Heat low
Pressure lower
Quality of welding nugget is worse
It is normal for the electrodes to
wear to such excess that they
need redressing, or replacing.
This wear varies according to the
applied current and the material
thickness
.
original image
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original image
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New electrode
after wearing
Assessing the quality of the electrode automatically
We need a method to
assess the quality of the
welding electrode
automatically.
We capture an image of the
electrode using a digital
camera
We process this image
using digital image
processing techniques to
evaluate the quality of the
electrode.
Digital image processing techniques to assess
the quality of welding electrode
Extract the electrode from the image
Image segmentation
Determine the boundary for the electrode
Filter this boundary using boundary representation
and description methods
Find the width of the tip using Cullen method
If the width of the tip is smaller than a predefined
threshold, we consider the tip as a good tip
Otherwise the tip needs replacing or redressing
Image segmentation
There are many image segmentation methods
Edge detection
Sobel
Canny
Laplacian
Prewitt
....
Hough transform
Region growing
Graph theory
Snakes active contours
Electrodes types
We have two types of electrodes
Flat tip
Doom tip
For each tip type, we have a bank of
250 images.
Images for tips with high quality
Images for tips with low quality
original image
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Cullen method
The top Figure shows an
image that contains a flat tip.
The bottom figure shows a
schematic diagram of an
ideal flat tip and indicates its
parameters such as the tip
width
T
p
and the electrode
width C
p
.
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Cullen method
This figure show the
boundary of the
electrode.
Let us process the
boundary image on a
row by row basis.
For each row, we
subtract the
x
coordinates of the left
boundary points (shown
in red colour) from the
x
coordinates of the right
boundary points (shown
in blue colour).
Cullen method
The first
x
a
rows do not
contain the tip boundary.
The subtraction operation
produces zeros as shown in
the bottom Figure.
For the row
x
a
+1, the
subtraction operation
produces the tip width T
p
.
For the rows from
x
a
+ 1
until
x
a
+
x
g
, the subtraction
operation produces a line
with a slope of
g
Cullen method
The slope of
g =2
For the rows from
x
a
+
x
g
+ 1
until
M
, the subtraction
operation produces a value
of C
p
.
The first derivative of the 2D
top graph is calculated and
this is shown in the bottom
figure.
Cullen method
The width of the tip is
determined as follows. The
derivative of the tip profile is
thresholded
using a
threshold value
g
.
Then the number of points
whose values are larger than
g
is determined and this
number is assigned to
x
g
.
The tip width is then
determined using the
Equation
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Pixels
Pixels
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x axis
Tip profile
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x axis
First derivative of the tip profile
Assessing the quality of flat tips
Image segmentation and boundary representation
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Canny
algorithm and Cullen method
To determine the width of the tip in pixels
automatically for two
hundreds
and fifty
images, we
have used
Canny
algorithm for image
segmentation, and
Cullen method for extracting the tip
width
The results are shown in red
The tip width has been determined
manually for the 250 images and the results
are shown in blue.
The bottom figure shows
the
differences
between the manual and automatic
determination of the tip width in pixels.
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Image Number
Tp Manually obtained and Canny and cullen
Tp found manually and Tp found using Canny and cullen
Tp manually
Tp Canny and Cullen
0
50
100
150
200
250
300
15
10
5
0
5
10
Error between Tp found manually and Tp found using Canny and cullen
Image Number
Tp Error obtained
T
p
manually and
T
p
Canny and
Cullen
Error between manually and automatically
measurement the tip
Boundary filtering using Fourier transform
Suppose that we have the
boundary shown here
x(n)= x(1)+x(2)+...+x(K

1)
y(n)= y(1)+y(2)+...+y(K

1)
We can represent the
boundary using the complex
numbers
s(k)=x(1)+
iy
(1)+ x(2)+
iy
(2)+...
x(K

1)+
iy
(K

1)+
6 6
(,)
x y
7 7
(,)
x y
2 2
(,)
x y
3 3
(,)
x y
4 4
(,)
x y
5 5
(,)
x y
6 6
(,)
x y
7 7
(,)
x y
1
i
The discrete Fourier transform of
s
(
k
) is
The inverse Fourier transform of these coefficients restores
s
(
k
). That is,
Suppose, however, that instead of all the Fourier coefficients,
only the first
P
coefficients are used.
This is equivalent to setting the term
a(u)
= 0 for
u
>
P

1
. Then
we get an approximation for the boundary.
The low frequency components account for the global shape of
the boundary
Whereas the high frequency components account for the fine
details in the boundary shape
Canny
algorithm, Fourier transform
To determine the width of the tip in
pixels automatically for two hundreds
and fifty images, we used
Canny algorithm for image
segmentation,
Fourier transform for filtering the
boundary
Cullen method for extracting the tip
width
The results are shown in red
The tip width has been determined
manually in the 250 images and the
results are shown in blue.
The bottom figure shows the differences
between the manual and automatic
determination of the tip width in pixels.
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50
100
150
200
250
300
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80
90
100
110
120
130
140
Image Number
Tp Manually obtained and Canny and Fourier transform
Tp found manually and Tp found using Canny and Fourier transform
Tp manually
Tp Canny and fourier transform
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50
100
150
200
250
300
15
10
5
0
5
10
Error between Tp found manually and Tp found using Canny and Fourier transform
Image Number
Tp Error obtained
T
p
manually and
T
p
Canny and Fourier
Transform
Error
between manually
and automatically
measurement
the tip
Canny
algorithm and minimum
–
perimeter polygons
A closed boundary can be
approximated can be approximated
with arbitrary accuracy by a polygon.
For a closed boundary, the
approximation becomes exact when
the number of vertices of the polygon
is equal to the number of points in
the boundary, and each vertex
coincides with a point on the
boundary.
The details and the noise in the
boundary can be reduced by
decreasing the number of vertices.
Canny
algorithm and minimum
–
perimeter polygons
To determine the width of the tip in pixels
automatically for two hundreds and fifty
images, we used
Canny algorithm for image
segmentation,
Minimum perimeter polygon for
filtering the boundary
Cullen method for extracting the tip
width
The results are shown in red
The tip width has been determined
manually in the 250 images and the results
are shown in blue.
The bottom figure shows the differences
between the manual and automatic
determination of the tip width in pixels.
0
50
100
150
200
250
300
70
80
90
100
110
120
130
140
Image Number
Tp Manually obtained and Canny and Polygon
Tp found manually and Tp found using Canny and Polygon
Tp manually
Tp Canny and polygon
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100
150
200
250
300
15
10
5
0
5
10
15
Error between Tp found manually and Tp found using Canny and Polygon
Image Number
Tp Error obtained
T
p
manually and
T
p
Canny and MP Polygons
Error between manually and automatically
measurement
the tip
Region
growing algorithm and Cullen method
The
region growing is a procedure that groups pixels
or
sub

regions
into larger regions based on
predefined criteria for
growth
Starting
with a single pixel (seed) and adding new
pixels
slowly
1

choose the pixel
,
2

check the neighbouring pixels and add them to
the region if they are similar to
the seed,
3
–
repeat step (2) for each of the newly added
pixels; stop if no more pixels can be added
Region
growing algorithm and Cullen method
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150
200
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300
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80
90
100
110
120
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Image Number
Tp Manually obtained
Tp found manually and Tp found using region growing and Fourier transform
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150
200
250
300
6
4
2
0
2
4
6
8
10
12
14
Error between Tp found manually and Tp found using region growing and Fourier transform
Image Number
Tp Error obtained
T
p
manually and
T
p
Region growing and Cullen
Error between
manually
and automatically
measurement the tip
To determine the width of the tip in pixels
automatically for two hundreds and fifty
images, we used
Region growing for image
segmentation,
Cullen method for extracting the tip
width
The results are shown in red
The tip width has been determined
manually in the 250 images and the results
are shown in blue.
The bottom figure shows the differences
between the manual and automatic
determination of the tip width in pixels.
Region growing
Algorithm, Fourier Transform
0
50
100
150
200
250
300
70
80
90
100
110
120
130
140
Image Number
Tp Manually obtained
Tp found manually and Tp found using region growing and Fourier transform
0
50
100
150
200
250
300
5
0
5
10
15
20
25
Error between Tp found manually and Tp found using region growing and Fourier transform
Image Number
Tp Error obtained
T
p
manually and
T
p
Region growing and F
Transform
Error between
manually
and automatically
measurement
the tip
Region
growing algorithm and minimum
–
perimeter polygons
0
50
100
150
200
250
300
70
80
90
100
110
120
130
140
Image Number
Tp Manually obtained and region growing, Polygon
Tp found manually and Tp found using region growing, Polygon
Tp manually
Tp region growing and polygon
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50
100
150
200
250
300
2
0
2
4
6
8
10
12
14
Error between Tp found manually and Tp found using region growing , Polygon
Image Number
Tp Error obtained
T
p
manually and
T
p
Region growing and polygon
Error between
manually
and automatically
measurement
the tip
The region
growing
and minimum perimeter polygon case is superior because
the error term for
T
p
is smaller as shown in Figure
above
Graph theory algorithm and Cullen method
The
set of points in arbitrary
feature space are represented as
a weighted undirected
graph
where
the nodes of the graph are
the points in the feature space,
and an edge is formed between
every pair of nodes
.
The weight on each
edge w(
i,j
),
is
a function of the similarity
between nodes
i
and
j
.
)
,
(
E
V
G
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Image Number
Tp Manually obtained and Normalized cuts and cullen
Tp found manually and Tp found using Normalized cuts and cullen
Tp manually
Tp normalized cuts and Cullen
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150
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300
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15
10
5
0
5
10
15
Error between Tp found manually and Tp found using Normalized cuts and cullen
Image Number
Tp Error obtained
T
p
manually and
T
p
Normalized Cuts and Cullen
Error between
manually
and automatically
measurement
the tip
we have calculated the standard
deviation for
error between the
manual and automatic methods
for the
two hundreds and fifty
electrode tip images.
The
results are shown in
table.
The
results of this table reveal
that the region growing
and
Minimum
–
Perimeter
Polygons
gave the
most accurate method
for determining the tip
width.
On the other hand, the graph
image segmentation algorithm
produces the worst results.
Cases
standard
deviation
1
Canny
algorithm
and
Cullen
method
4
.
50
2
Canny
Algorithm,
Fourier
Transform
4
.
80
3
Canny
Algorithm
and
Minimum
–
Perimeter
Polygons
4
.
70
4
Region
grown
Algorithm
and
Cullen
method
3
.
80
5
Region
grown
Algorithm,
Fourier
Transform
3
.
70
6
Region
grown
Algorithm
,
and
Minimum
–
Perimeter
Polygons
3
.
40
7
Graph
Theory
Algorithm
and
Cullen
method
5
.
60
Which method is the best?
Doom electrode tip
Original image
Laplacian
Algorithm
The image
for the doom electrode is very hard to
segment.
This is because of the shining parts of
the tip doom.
We show here the segmentation results using
Laplacian
Sobel
Prewitt
Canny
None of these edge detection algorithms works properly
Also, we have attempted
these algorithms
Region
growing
Graph theory
Hough transform
Also
n
one of these edge detection algorithms works
properly
The only image segmentation algorithm we tried it and
can segment the doom electrode successfully is the
Active contours snake algorithm
Sobel
Algorithm
Prewitt Algorithm
Canny Algorithm
Snake Algorithm
Snake are curves defined within an image domain that
can move under the influence of internal forces
coming from within the curve itself and external
forces
The internal and external forces are defined so that the
snake will conform to an object boundary
traditional snake is a curve
X(s)=[x(s),y(s)], s
[0,1]
Finding a method for determining the diameter of the
tip automatically.
Image segmentation & representation
Original image
Thresholding
Snake Algorithm
Snake Algorithm
Snake algorithm and Cullen
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150
200
250
300
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20
10
0
10
20
30
Error between Tp found manually and Tp found using snake.ahmed
Image Number
Tp Error obtained
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50
100
150
200
250
300
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40
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100
120
140
Image Number
Tp Manually obtained
Tp found manually and Tp found using Snake.Cullen
Tp Manually obtained
Tp Snake Cullen
T
p
manually and
T
p
Snake and Cullen
Error between manually and automatically
measurement the tip
Snake algorithm and Fourier transform
0
50
100
150
200
250
300
20
10
0
10
20
30
40
50
60
Error between Tp found manually and Tp found using Tp.snak.Fourier
Image Number
Tp Error obtained
0
50
100
150
200
250
300
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40
60
80
100
120
140
Image Number
Tp Manually obtained
Tp found manually and Tp found using Tp.Snak.Fourier
Tp Manually obtained
Tp snak Fourier
Tp
manually and Fourier Transform
Error between manually and automatically
Snake algorithm and polygon
0
50
100
150
200
250
300
30
20
10
0
10
20
30
40
Error between Tp found manually and Tp found using Tp .polgyon .snak
Image Number
Tp Error obtained
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50
100
150
200
250
300
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40
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100
120
140
Image Number
Tp Manually obtained
Tp found manually and Tp found using Tp.Snake. Polgyon
Tp Manually obtained
Tp Polgyon Snak
Tp
manually and
Tp
Snake and MP Polygons
Error between manually and automatically
Cases
standard
deviation
1
Sank
algorithm
and
Cullen
method
7
.
3801
2
Sank
Algorithm
,
Fourier
Transform
6
.
8742
3
Sank
Algorithm
and
Minimum
–
Perimeter Polygons
7
.
1366
we have calculated the standard deviation
for error between the manual and automatic
methods for the two hundreds and fifty
electrode doom tip images.
Built system that can assess the quality of spot
welding electrodes easily
We need to improve the
performance of determining the
tip width automatically.
To do this
We use a high performance
illumination source to
illuminate the electrode
We capture an image for the
shadow of the electrode
The shadow image is easy to
process
thresholding
Then we can extract the tip
width easily using Cullen
method
Original image
Thresholding
edge
edge
Future work
To embed the system that can assess the quality of spot
welding electrodes into a spot welding machine.
Conclusions
We have used image processing algorithms
successfully to assess the quality of spot welding
electrodes automatically.
We have built a system that can assess the quality of
spot welding electrodes using simple image processing
techniques
Thresholding
Simple filtering methods such as median filtering
Any questions
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