Image processing for radiographic films of weld inspection

7/27/2019 Image processing for radiographic films of weld inspection

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Abstract - One of the professional domains inindustry is weld inspection which deals with

investigating the inside or outside (surface) of the

weld to trace any defects which may cause failure in

the system. Likewise, one of the methods of weld

inspection is radiographic film interpretation (RI). In

this method the specific weld will be captured in

radiographic films and then an inspector will

interpret them to identify any defects similar to the

job that an orthopaedist does.The aim of this paper is to develop a fully

automatic computer vision system to analyse welds

radiographic films for defects detection. For this, the

adaptive method of smoothing plus thresholding has

been applied first. This is followed by a

morphological operation to remove small objects, and

then further smoothing has been used and finally by

applying a boundary detection method, the boundary

of the defects has been delineated. To validate the

software, the method was applied on several weld

radiographic films which possess distinct defects. The

system was able to detect all defects which were

visually confirmed by an expert.

I. INTRODUCTIONelding is the joining of materials in the

welding zone by the application of heat

and/or pressure, with or without the addition offiller material. This is often done by melting the

work pieces and adding a filler material to form a

pool of molten material (the weld pool) that cools

to become a strong joint. Auxiliary materials, for

example shielding gases, flux or pastes may be

used to render the process possible or to facilitate

it. The energy required for welding is supplied by

outside sources. The materials could be sheets,

plates, pipes or some industrial productions such as

boiler, reactor, and heat exchanger and so on

(Agreste, 2006).

Manuscript received May 14, 2011. This work has been

supported by the Kingston University London, Engineering

Faculty.Farrokh Faramarzi, MSc. Mechatronics Systems Student,

Kingston University London (United Kingdom), (Phone:+44(0)7411304849, e-mail: [email protected])

Mohammadreza Motamedi, MSc. Mechatronics Systems

Student, Kingston University London (United Kingdom),

(Phone: +44(0)7783839225, e-mail: [email protected])

The authors are currently writing their thesis aboutdesigning and manufacturing a mobile robot which should go

through a pipe and inspect the inside surface automatically by

digital image processing.

On the other hand, there are many different

welding methods that depend on the accuracy of

the process; the weld designer chooses the one

which is appropriate.

Meanwhile, one of the most important areas in

industry is safety check. In other words, we can

consider if, in a site, the construction of a reactor is

finished and every joint is welded and ask is it

ready to go in the line and start working because,

after doing construction in every production

industries, the quality control part should check the

components, quality, quantity and many other

parameters which are important for the quality of

the product and effect efficiency of that.

In addition, the other famous and vital part of

quality control is inspection that investigates if the

product is ready to go on line or has some problems

and should be repaired. In the welding field, like

many other industries, after operation by welder,

the weld inspector should inspect the work piece to

see whether it has any defects such as crack,

porosity and so on or not (Kwon, et all. 2003).

Imagine there is a reactor in a refinery site which

works in around 1000 degree of Celsius and morethat 500 bars pressure. If this reactor failed, it

would be like an atomic bomb, which can destroy

its entire environment within at least 1 kilometre

radius. Thus the refinery, which has this reactor,

spends much money to inspect its components like

welds per year. The inspection engineering team

inspects all of the welds that the reactor has by

means of different methods dependent on the

condition and accuracy of the job (Vandevoorde

and Josuttis, 2003).

There are several different types of weldinspection which are briefly described below. It

should be considered which part of weld is

important the most, which means that the inspector

is looking for a crack on the weld surface or inside

it, he is looking for any defect whether it is a coarse

one or very fine one, he is looking for one

particular defect or cluster shape of defects.

Depending on these and many other factors the

weld designer can choose a method to inspect the

weld.

One of the very prevalent methods of inspectionin industries is Radiographic Testing Interpretation

Image processing for radiographic films of weld inspection

F. Faramarzi and M. Motamedi

W
http://en.wikipedia.org/wiki/Meltingmailto:[email protected]:[email protected]://en.wikipedia.org/wiki/Melting


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(RTI). In this way, a technician captures

radiographic films of welds, then a weld interpreter

starts to interpret the films and find defects visually

by means of specific light (the job that an

orthopaedic doctor does). The interpreter does this

by using his knowledge and experiences and

recognising the different kinds of defects.

On the other hand, in electrical engineering and

computer science, computer vision system is any(computerised algorithm) form of image

processing for which the input is an image, such as

a photograph or video frame. Moreover, the output

of image processing may be either an image or a

set of characteristics or parameters correlated to the

image. Furthermore, the purpose of this research is

to develop a fully automatic computer vision

system to analyse welds radiographic films for

defect detection.

The rest of this project is organized as follows:

the next section describes the image acquisitionradiographic films of welds and then four different

prevalent types of weld defects in industry will be

explained. Following that image processing

algorithm will be shown in four steps, and finally

the experimental results of other images would be

illustrated.

II.METHODOLOGYA. Image acquisition

In Figure 1 a section view of a weld between two

captured from the cap (top) of the weld and bydeveloping the film, it will be available for the

interpreter.

For capturing the image of the weld, the camera

should be located above the specimen with

particular distance to emit the X-ray. Likewise, the

raw-film should be placed behind the specimen and

after the film processing; the final image will be

ready to interpret by inspector.

Fig. 1. Section view of a weld, welding two sheets (a),

section view of the weld (b), radiographic film of the

weld (c) sheets can be seen. The radiographic film is [3]

III.TYPES OF DEFECTSIn this paper from all different types of defects

in weld inspection, four major ones will be

discussed which is seen in many troubled materialsin industry. They are Burn Through, LOF, LOP and

Slag.

A. Burn Through

A Burn Through is a defect which is a localised

collapse of the molten pool due to excessive

penetration resulting in a hole in the weld run

(Figure 2). In this defect the edge of the root will

not be sharp and it is one sign to recognise it in the

radiographic film. In Figure 3 a film of BurnThrough is shown.

In this film (Figure 3) there are two separated

areas in the middle of the weld which the intensity

of them is less than other parts.


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Fig. 2. Burn Through [3]

Fig. 3. Radiographic film of Burn Through

B. Lack of Fusion (LOF)

One of the other defects which can be found in

weld inspection is called Lack of fusion (LOF). In

this defect during the welding, the side wall of the

sheet and melt material does not fuse together

(Figure 4 and 5). Consider Figure 5, there are some

black lines in the same direction which shows LOF.

Fig. 4. Lack of Fusion (LOF)

Fig. 5. Radiographic films of LOF (Lack of Fusion)

C. Lack of Penetration (LOP)

One of the other common defects in welding is

Lack of penetration (LOP), in this defect the melt

weld material cannot reach to the root of the weld

and the root will not be welded (Figure 6).

Considering the radiographical film of LOP (Figure

7) the edges of the weld will stay sharp like a long

(or short) line.

Fig. 6. Lack of Penetration (LOP)

Fig. 7. Radiographic film of LOP (Lack of Penetration)

D. Slag

The other prevalent defect in weld inspection is

Slag (Figure 8). Interpass slag inclusions usually

consist of non-metallic impurities that solidified on

the weld surface and were not removed between

weld passes. The specific properties of this defect

that can be found out from the radiographic film

(Figure 9) is as irregularly shaped darker density

spot, usually slightly elongated and randomly

spaced.

Fig. 8. Slag in the weld

Fig. 9. Radiographic film of Slag

IV.IMAGE PROCESSING ALGORITHMStep one, smoothing plus thresholding

So that the image is processed in black and

white thresholding should be used. Then asmoothing operation can be applied to clean the

image. However the question is which one should

be used first. As it can be seen in Figure 4 the

results of thresholding and smoothing are very

similar, but by looking closely you can see some

small differences around the holes, which indicates

that the latter technique should be used. More

specifically, the aim of image smoothing is to

enhance the resolution of camera images, therefore

will be the primary image processing algorithm

followed by thresholding.


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Fig. 10. Deciding which step is better at first smoothing then

thresholding (a) or thresholding then smoothing (b)

The purpose of this process is to specify two

defects (Burn Through) in the film so it is possible

to segment the objects in the image. Segmentation

refers to the operation of partitioning an image into

component parts or into separate object so that only

the defects are visible. One of the most prevalent

uses of segmentation is thresholding which has

different uses; single thresholding, doublethresholding, adaptive thresholding and global

thresholding. This image was chosen since it was

needed to threshold by means of histogram

computing, and global thresholding. By using

thresholding all of the pixels in an image change to

either 0 (black) or 1 (white). The threshold in this

image is 0.3882, which means that all of the pixels

under 0.3882 become 0 and all pixels above 0.3882

become 1.

Step two, Morphology operation

The next step is to use Morphology operations,

for continuing image is in Figure 10. First the

boundaries of the welds should be connected via a

closing operation. By means of closing operation

the objects connect to each other and fill the holes

in a region, as well as eliminate inlets on the

boundary. See Figure 11.

Fig. 11. Using closing operation (Morphology)

On the other hand, since the closing operation

consists of dilation and then erosion operations

dilation was used as well (Figure 12).

Fig. 12. Using dilation operation (Morphology)

As you know dilation operation allows objects

to expand, connects disjoint objects and enlarges a

region which we need here. The results from

Figures 11 and 12 show, it is appropriate to dilate

the image only to erode it (do not use closing

operation). This is because eroding the image the

boundary will shrink the right hole and get a bad

result. After using many other morphology

operations such as erosion, opening, closing, and

skeletonisation, it was found that using a closing

operation after dilation yields a positive result(Figure 13).

Fig. 13. Using closing operation after Figure 12

Step thr ee, Smoothing

As Figure 13 shows, the edges of the cap are

much unshaped and another command should beused to clean it. So image smoothing has been used

again to solve this problem; however there are

several types of smoothing such as average, disk,

Gaussian, laplacian, log, motion, prewitt, sobel,

unsharp, that can be utilised. After visual testing

and examining of them, the most efficient one will

be chosen and the best result will be achieved. See

Figure 14. In this step, the edge was examined for

specifying which holes a programme had to be

written for by means of edge function. It also tested

different types of edges such as sobel, prewitt,roberts, canny and zerocross. The best one, which

was sobel, did not give suitable result (Figure 15).

Fig. 14. Using smoothing, disk type after Figure 13

Fig. 15. Using edge function

Step four, Boundary functions

After testing is completed a boundary should be

drawn around the holes to specify defects. Before

identifying the boundaries, the black and white

pixels should be reserved, since the boundary

function can detect white regions only and the

specified regions are black (Figure 16).


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Fig. 16. Convert the black and white pixels to each other

By writing boundary functions in the software it

is possible to get all of the holes in the image

however the problem is that the computer will

recognise the two big spaces in two sides of holes

as objects and draw boundary around them as well

(Figure 17).

In Figure 17 the software recognises four holes.

One positive solution is to introduce the two

specified holes to the computer for obtaining the

boundary. In order to do this, labelling commands

Fig. 17. Getting boundary (not acceptable result)

have been used. By means of this function all four

holes will be separated and then function label 2

and 3 will be added to each other. See the result in

Figure 18.

Fig. 18. Adding label 2 and 3 from Figure 17

When the boundary functions are written, itwould just specify the two holes. Figure 19

indicates the boundaries by showing the original

image.

Fig. 19. Processed image of radiographic film of Burn Through

V. EXPERIMENTAL RESULTS ANDDISCUSSION

This part has illustrated the final results, written

by MATLAB software, by comparing the primary

images via visual inspection. Likewise, more

images will be processed to show that different

images can be processed by the same program.

Here, radiographic films with several specific

defects have been shown.

A. Burn ThroughAs it has been shown in the first part of the

process as well as these images, it is possible to

achieve the best solution and reduce the time. In

the last image (c) the two cavities in the pipe are

marked by the red circles (Figure 20).

Fig. 20. Burn Through


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B. Lack of Penetration (LOP)

Fig. 21. Lack of Penetration (LOP)

C. Lack of Fusion (LOF)



D. Slag

One of the major problems in film are the manydifferent possible interpretations of each observer.

Each interpreter has their own idea and although

most of the time the interpretations are the same,

some complex films interpreters have different

thoughts.

Moreover, manual interpreting takes a lot of

time. This means that a qualified interpreter should

watch the film carefully, recognise the defects and

write report of it.

Fig. 24. Slag

Regarding the programs that we should write

for the computer, the images will be processed and

recognised so that we know type and distance of

each image.

As a final point, it is interesting to note that by

designing this program the films would be

interpreted automatically along with detailedcomputer reports. Regarding the advantages of this

idea, we can save time and, increase accuracy, as

that it has been mentioned above.

VI.ConclusionTaking everything into consideration, the aim

of this paper was to show that image processing has

been done for some radiographic films and the

related defects have been specified in each image

by this process. As it had been shown before, theyhave been done by some prevalent operations in

image processing such as point processing,

filtering, image smoothing, edge detection,

segmentation (using thresholding), boundary

tracing, region labeling, and Morphology

operations. These functions lead us to write the

program which takes the image as an input and

gives the defects and their details as an output.

(Pudney, 1998)

A method has been developed to detect the

defect in the weld. Using the radiographic imagesfor this digital image, several image processing

algorithms were applied and the system has been

validated by visual judgment. Finally the result is

very encouraging and shows the potential use of

the system for more application in the field of weld

inspection.

To sum up, by developing the technology in

industries and considering that everything has been

changing daily, we should find solutions for saving

time and keeping accuracy in each process. It is

obvious that the conclusion vision and image

processing can have a bright future in weld

inspection. In order to achieve this, digital image


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processing is definitely one of the most critical

technologies for solving numerous problems that

human face with in reality.

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Image processing for radiographic films of weld inspection