Assessing the quality of spot welding electrodes tip using image

Report
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
original image
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 It is normal for the electrodes to
wear to such excess that they
need redressing, or replacing.
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New electrode
original image
 This wear varies according to the
applied current and the material
thickness.
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after wearing
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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
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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 Tp and the electrode
width Cp.
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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 xa rows do not
contain the tip boundary.
The subtraction operation
produces zeros as shown in
the bottom Figure.
 For the row xa +1, the
subtraction operation
produces the tip width Tp.
 For the rows from xa + 1
until xa + xg, the subtraction
operation produces a line
with a slope of g
Cullen method
 The slope of g =2
 For the rows from xa + xg + 1
until M, the subtraction
operation produces a value
of Cp.
 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 xg.
 The tip width is then
determined using the
Equation
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Pixels
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Pixels
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First derivative of the tip profile
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Tip profile
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x axis
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x axis
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Assessing the quality of flat tips
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Image segmentation and boundary representation
Canny algorithm and Cullen method
Tp found manually and Tp found using Canny and cullen
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Tp manually
Tp Canny and Cullen
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Image Number
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Tp manually and Tp Canny and
Cullen
Error between Tp found manually and Tp found using Canny and cullen
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Tp Error obtained
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.
Tp Manually obtained and Canny and cullen
 To determine the width of the tip in pixels
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-5
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-15
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Image Number
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Error between manually and automatically
measurement the tip
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Boundary filtering using Fourier transform
 Suppose that we have the
( x7 , y 7 )
boundary shown here
( x6 , y 6 )
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
( x7 , y 7 )
numbers
s(k)=x(1)+iy(1)+ x(2)+iy(2)+...
x(K-1)+iy(K-1)+
i
1
( x2 , y 2 )
( x3 , y 3 )
( x4 , y 4 )
( x6 , y 6 )
( x5 , y 5 )
 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
Tp found manually and Tp found using Canny and Fourier transform
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Tp Manually obtained and Canny and Fourier transform
 To determine the width of the tip in
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pixels automatically for two hundreds
and fifty images, we used
 Canny algorithm for image
segmentation,
 Fourier transform for filtering the
boundary
Tp manually and Tp Canny and Fourier
 Cullen method for extracting the tip
Transform
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. Error between manually and automatically
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Tp manually
Tp Canny and fourier transform
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Image Number
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Error between Tp found manually and Tp found using Canny and Fourier transform
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Tp Error obtained
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0
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-15
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measurement the tip
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Image Number
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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
Tp Manually obtained and Canny and Polygon
automatically for two hundreds and fifty
images, we used
 Canny algorithm for image
segmentation,
 Minimum perimeter polygon for
filtering the boundary
Tp found manually and Tp found using Canny and Polygon
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 Cullen method for extracting the tip
Tp manually
Tp Canny and polygon
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Image Number
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width
Tp manually and Tp Canny and MP Polygons
Error between Tp found manually and Tp found using Canny and Polygon
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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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Tp Error obtained
 The results are shown in red
 The tip width has been determined
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-15
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Image Number
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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
 To determine the width of the tip in pixels
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Tp Manually obtained
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Image Number
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Tp manually and Tp Region growing and Cullen
Error between Tp found manually and Tp found using region growing and Fourier transform
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Tp Error obtained
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.
Tp found manually and Tp found using region growing and Fourier transform
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0
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Image Number
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Error between manually and automatically
measurement the tip
Region growing Algorithm, Fourier Transform
Tp found manually and Tp found using region growing and Fourier transform
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Error between Tp found manually and Tp found using region growing and Fourier transform
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Tp Error obtained
Tp Manually obtained
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Image Number
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Tp manually and Tp Region growing and F
Transform
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Image Number
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Error between manually and automatically
measurement the tip
Region growing algorithm and minimum –
perimeter polygons
Error between Tp found manually and Tp found using region growing , Polygon
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Tp found manually and Tp found using region growing, Polygon
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120
Tp Error obtained
Tp Manually obtained and region growing, Polygon
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Tp manually
Tp region growing and polygon
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Tp manually and Tp Region growing and polygon
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Error between manually and automatically
measurement the tip
The region growing and minimum perimeter polygon case is superior because
the error term for Tp is smaller as shown in Figure above
Graph theory algorithm and Cullen method
Tp found manually and Tp found using Normalized cuts and cullen
The set of points in arbitrary
feature space are represented as
a weighted undirected graph
G  (V , E )
Tp Manually obtained and Normalized cuts and cullen
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Tp manually and Tp Normalized Cuts and Cullen
Error between Tp found manually and Tp found using Normalized cuts and cullen
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Tp Error obtained
where the nodes of the graph are
the points in the feature space,
and an edge is formed between
every pair of nodes.
Tp manually
Tp normalized cuts and Cullen
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The weight on each edge w(i,j), is
a function of the similarity
between nodes i and j .
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Image Number
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Error between manually and automatically
measurement the tip
Which method is the best?
 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
1 Canny algorithm and Cullen method
standard
deviation
4.50
2 Canny Algorithm, Fourier Transform
4.80
3 Canny Algorithm and Minimum –
Perimeter Polygons
4 Region grown Algorithm and Cullen
method
5 Region grown Algorithm, Fourier
Transform
6 Region grown Algorithm, and
Minimum –Perimeter Polygons
7 Graph Theory Algorithm and Cullen
method
4.70
3.80
3.70
3.40
5.60
Doom electrode tip
 The image for the doom electrode is very hard to
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

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 none 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
Laplacian Algorithm
Original image
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
Tp found manually and Tp found using Snake.Cullen
Error between Tp found manually and Tp found using snake.ahmed
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Tp Error obtained
Tp Manually obtained
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Tp Manually obtained
Tp Snake Cullen
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Image Number
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Tp manually and Tp Snake and Cullen
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Error between manually and automatically
measurement the tip
Snake algorithm and Fourier transform
Tp found manually and Tp found using Tp.Snak.Fourier
Error between Tp found manually and Tp found using Tp.snak.Fourier
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Tp Error obtained
Tp Manually obtained
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Tp Manually obtained
Tp snak Fourier
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Tp manually and Fourier Transform
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Error between manually and automatically
Snake algorithm and polygon
Tp found manually and Tp found using Tp.Snake. Polgyon
Error between Tp found manually and Tp found using Tp .polgyon .snak
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Tp Error obtained
Tp Manually obtained
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Tp Manually obtained
Tp Polgyon Snak
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Tp manually and Tp Snake and MP Polygons
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-30
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Image Number
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Error between manually and automatically
Cases
1 Sank algorithm and Cullen method
standard
deviation
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
edge
Thresholding
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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