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Assessing the quality of spot welding electrode’s tip using digital image processing techniques

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A .A. Abdulhadi

Coherent and Electro-Optics Research Group

GERI

- 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 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

- 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.

New electrode

after wearing

- 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.

- 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

There are many image segmentation methods

- Edge detection
- Sobel
- Canny
- Laplacian
- Prewitt
- ....

- 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

- 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.

- 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).

- 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

- 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.

- 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

Image segmentation and boundary representation

- 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.

Tpmanually and Tp Canny and Cullen

Error between manually and automatically

measurement the tip

- 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)+

- 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

- 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.

Tp manually and Tp Canny and Fourier Transform

Error between manually and automatically

measurement the tip

- 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.

- 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.

Tp manually and Tp Canny and MP Polygons

Error between manually and automatically

measurement the tip

- 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

- 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.

Tp manually and Tp Region growing and Cullen

Error between manually and automatically

measurement the tip

Tp manually and Tp Region growing and F Transform

Error between manually and automatically

measurement the tip

Tp manually and Tp 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 Tp is smaller as shown in Figure above

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 .

Tp manually and Tp Normalized Cuts and Cullen

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.

- 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 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 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.

Original image

Thresholding

Snake Algorithm

Snake Algorithm

Tp manually and Tp Snake and Cullen

Error between manually and automatically measurement the tip

Tp manually and Fourier Transform

Error between manually and automatically

Tp manually and Tp Snake and MP Polygons

Error between manually and automatically

we have calculated the standard deviation for error between the manual and automatic methods for the two hundreds and fifty electrode doom tip images.

- 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

- To embed the system that can assess the quality of spot welding electrodes into a spot welding machine.

- 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