Flaw detection in radiographic weld images using morphological approach

Alaknandaa, R.S. Ananda,*, Pradeep Kumarb

aDepartment of Electrical Engineering, Indian Institute of Technology, Roorkee, Roorkee 247667, IndiabDepartment of Mechanical and Industrial Engineering, Indian Institute of Technology, Roorkee, Roorkee 247667, India

Received 23 February 2005; revised 13 May 2005; accepted 18 May 2005

Available online 26 July 2005

Abstract

It is necessary to detect suspected defect regions in the radiographic weld images to find the type of flaw and its causative factors. This

requires processing of radiographic images by a suitable approach. This paper presents an approach to process these radiographic weld

images of the weld specimens considering morphological aspects of the image. The proposed approach first determines the flaw boundaries

by applying the Canny operator after choosing an appropriate threshold value. The boundaries are then fixed using a morphological image

processing approach i.e. dilating few similar boundaries and eroding some irrelevant boundaries decided on the basis of pixel characteristics.

The flaws detected by this approach are categorized according to their properties.

q 2005 Published by Elsevier Ltd.

Keywords: Radiographic images; Flaw detection; Edge detection; Dilation; Erosion

1. Introduction

Some times, weldments are not properly joined together

during the welding process due to different reasons, which

produce flaws of different types in weldments. These flaws

are seen in the form of gaps, cracks or pores and cavities.

Common weld defects include—lack of fusion, lack of

penetration or excess penetration, gas cavities, slag

inclusions, cracks, undercuts, lamellar tearing, shrinking

cavities etc. The lack of fusion results from too little heat

input and/or too rapid traverse movement of the welding

torch (gas or electric). Lack of penetration or excess

penetration arises from a too high heat input and/or too

slow movements of the welding torch (gas or electric). Gas

cavities occur when gases are trapped in the solidifying

weld metal. During solidification gases are trapped in the

welded zone resulting in porosity. Slag inclusion is of

different types. It can be of any shape in any direction—

slag lines (elongated cavities containing slag or other

foreign matter), wearing faults, faults from bad chipping,

faults at electrode change, faults at junction of seams.

0963-8695/$ - see front matter q 2005 Published by Elsevier Ltd.

doi:10.1016/j.ndteint.2005.05.005

* Corresponding author. Tel.: C91 1332285590; fax: C91 1332273560.

E-mail address: anandfee@iitr.ernet.in (R.S. Anand).

Cracks are often caused by sulphur and phosphorus and are

more likely to occur in higher carbon steels. They are of

two types: longitudinal cracks and transverse cracks.

Longitudinal cracks normally appear in straight lines

along the centerline of the weld bead while transverse

cracks are straight lines perpendicular to centerline and

occasionally appear. Undercut is due to the reduced

thickness of one (or both) of the sheets at the toe of

weld, due to inaccurate settings/procedure. Lamellar

tearing is mainly a problem with low quality steels. It

occurs in plates that have low ductility with laminar

segregation. Shrinkage cavities are due to thermal

shrinkage or due to a combination of steam accompanying

phase change and their shrinkage cavities [1,2].

Radiography using X-rays is one of the NDT techniques

which are used for imaging the weldments for detecting and

locating the defects in them. In an X-ray image of

weldments, variation in intensity is caused by inhomogen-

eity of the weldments. Light patches or lines seen on the

films correspond to different types of discontinuities due to

lack of penetration, cracks, cavities, slag inclusion, under-

cuts and shrinkage cavities. The thickness of material in

welding process, weld type, weld position and radiographic

methods plays an important role in image quality. The flaws

can also be detected during the welding process on hot

welds, which further helps in quality control as well as in

cost reduction.

NDT&E International 39 (2006) 29–33

www.elsevier.com/locate/ndteint

Fig. 1. Original X-Ray figure

Alaknanda et al. / NDT&E International 39 (2006) 29–3330

In classical methods of analyzing the images, human

experts make the quality control of welded joints by

identifying defect characteristics in NDT images and the

results depend on quality of images, previous experiences

of human experts and their mental fitness. The classical

process is slow and results are varying from expert to

expert and even the same person can give different

results for the same image depending on his mental

state [3].

Image processing plays an important role to detect flaws

in weldments. With the help of image processing a semi-

automated system can be developed which is more reliable

as compared to the classical methods and also helpful

in taking decision with the help of a set of manipulating

tools [4].

2. Morphological image processing

The flaw information in the weld images is obtained by

segmenting the weld image using a boundary-based

approach. Segmentation of an image using boundary-

based approach involves following approaches:

Fig. 2. Binary gradient mask of the original image.

2.1. Edge detection

The edges in the image are formed where there are

abrupt changes in the intensity of the pixels. There are

different types of edge detectors, which can be used for

detecting the edges [5,6]. The Canny edge detector [7] is a

very important tool for local preprocessing to locate

changes in intensity function, having very good signal to

noise ratio. By using the Canny operator, the main

criterions are the detection of all important edges, the

distance between the actual and the located position of the

edge should be minimal and the minimum number of

multiple response to a single edge. In order to reduce noise

in Canny operation, first of all the image f(x) is smoothened

by convolution with a Gaussian function of scale s as

given below in Eqs. (1) and (2).

Gðx; sÞ Z eKðx2=2s2Þ (1)

Fðx;sÞ Z f ðxÞ!Gðx; sÞ (2)

where s is the standard deviation and changes according to

images. F(x,s) represent a surface on the (x,s) plane.

In an image, an edge point is defined as a point whose

strength is maximal in the direction of the gradient. For

each pixel, the direction n of the local edge for the

Canny-operation is estimated by Eq. (3) and expressed as

below

n ZVðG!f Þ

jVðG!f Þj(3)

where G is a 2D Gaussian function and f is an image

function.

The location of an edge (also known as non-maximal

suppression) is obtained with the help of Eq. (4).

v2

vn2G!f Z 0 (4)

The location is tracked along the top of obtained ridges.

All pixels that are not actually on the ridge top are set to

zero. This procedure of tracking the location of edges has

been set to obtain a thin line at the output. Further, the

spurious edges are eliminated by thresholding with

hysterisis. For thresholding, the magnitude of edge is

computed by calculating Eq. (5) as expressed below.

jGn !f j Z jVðG!f Þj (5)

Then two thresholds T1 and T2, satisfying T1!T2, are

used to threshold ridge pixels. Ridges having a value greater

than T2 are said to be ‘strong’ and other ridge values are

considered as ‘weak’. The image obtained after applying the

Canny operation on the image of Fig. 1 is shown in Fig. 2.

The image so obtained has contours as is seen in the image.

It is the binary gradient of the original image.

2.2. Morphological processing

The output obtained in the form of contours has

discontinuities due to crack edges. To obtain a closed

contour morphological image processing is done on the

image. The image is dilated first and then eroding is

performed [8,9]. In this process, high contrast lines having

gaps in the binary gradient mask image disappear using

appropriate structuring elements in dilation. High contrast

lines do not delineate the outline of the object of interest

very well. As compared to the original image, gaps can be

seen in the lines surrounding the object in the gradient mask

image. These linear gaps will disappear if the image after

performing the Canny operation is dilated using linear

structuring elements. Dilation is an operation that ‘grows’ or

Fig. 3. Dilated gradient mask of the binary image. Fig. 5. Superimposition of the segmented image on the original image.

Alaknanda et al. / NDT&E International 39 (2006) 29–33 31

‘thickens’ objects in the binary images. Mathematically

speaking, dilation of A by B denoted as A4B, is defined as

Eq. (6)

A4B Z fz=ðBÞzhAsFÞ (6)

where F is the empty set, B is the structuring element and A

is the object to be dilated.

Dilation is used to fill small holes and narrow gulfs in

objects. The binary gradient mask image is dilated using the

vertical structuring element followed by the horizontal

structuring element. The dilated gradient mask of the binary

image is shown in Fig. 3.

After dilation of an output image of the edge detection

operation, smoothening of the object is done in order to

make the segmented object look natural. The object is

smoothened by combining dilation with erosion to preserve

the original size. Erosion of A by B is the set of all points z

such that B, translated by z, is contained in A and

represented by Eq. (7)

AQB Z fz=ðBÞz 4Ag (7)

The object is smoothened by eroding the image twice

using appropriate structuring element (e.g. line, disk or

diamond). When the dilation is followed by erosion it is

known as morphological closing, which is mathematically

denoted by Eq. (8)

A$B Z ðA4BÞQB (8)

The output of the morphological function applied on the

dilated image is shown in Fig. 4. The superimposition of the

segmented image on the original image is shown in Fig. 5.

In this image, an incomplete penetration type of weld defect

is seen.

Fig. 4. Segmented images.

3. Results and discussions

It is very useful in industries to find out the flaws

even during process. The discussed technique can be

used to find out the position of the flaw in the weldment,

which will further help in finding out the type and the

cause due to which flaws are produced. This can be

useful in reducing the flaws by controlling the relevant

weld parameters.

The algorithm has been tested on different industrial

radiographic images depicting the various types of

welding defects choosing appropriate threshold value

for the Canny operation to find the edges. Different

contours of weld defects are extracted which are

smoothened by using morphological functions to detect

the flaws in an image. On digitized image of Fig. 6a

discontinuities are detected using the Canny edge

detection. The obtained edge profile is presented in

binary format using an appropriate threshold value.

Morphological operations are applied to dilate and to

smoothen the obtained binary image, which gives outer

border of segmented objects of an image after super-

imposition on the original image. The image obtained

after superimposition is shown in Fig. 6(b). Fig. 6(b)

shows a slag line defect present in the weld. The above

algorithm has been applied on four such radiographic

images having different type of welding flaws. Both, the

original and processed images are shown in each case.

Segmented images of respective original images

Figs. 7(a)–10(a) have been shown in Figs. 7(b)–10(b).

Fig. 6. (a) Original image of a slag line defect. (b) Segmented image of the

slag line defect.

Fig. 7. (a) Original image of a lack of fusion type defect. (b) Segmented

image of a lack of fusion type defect.

Fig. 8. (a) Original image of slag inclusions defects. (b) Segmented image

of slag inclusions defects.

Fig. 10. (a) Original image of an incomplete penetration defect. (b)

Segmented image of an incomplete penetration defect.

Table 1

Category of defects in segmented radiographic images

Segmented image appearance Type of flaw

Thin line along the edge of weld. Line may be wavy

and diffuse depending upon the orientation of defect

with respect to the X-ray beam

Lack of fusion

Edge line in the middle of weld Incomplete

penetration

Line more or less interrupted, parallel to the edges of

weld

Slag line

Irregular contour of any shape and size Slag inclusion

Fine line, straight or wandering in direction Cracks

Line some times broad and diffused along the edge

of weld

Undercuts

Rounded contours having dark shadows Porosity (gas

cavities)

Alaknanda et al. / NDT&E International 39 (2006) 29–3332

These radiographic images can be categorized on the

basis of information available in them. Table 1 summar-

izes this information.

4. Conclusions

The morphological approach has been implemented here

for the segmentation of weld defects in radiographic weld

images. The segmented image can be used to characterize

the flaws in weldments. Flaws characterized in segmented

images can be categorized in different types like lack of

Fig. 9. (a) Original image of gas cavity type defects. (b) Segmented image

of gas cavity type defects.

fusion, incomplete penetration, slag line, slag inclusion,

cracks, undercuts, porosity and wormholes. Once the

images are categorized the causes due to which flaws are

generated can be analyzed and can be taken care off in

future during the welding process.

Acknowledgements

The authors are thankful to the Head, Department of

Electrical Engineering, Indian Institute of Technology,

Roorkee for providing the required computational and

experimental facilities. The financial assistance provided by

the Council of Scientific and Industrial Research, New

Delhi, India is gratefully acknowledged.

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