iSIJ International. Vol. 39 (1 999), No. 10, pp. 10811087

Application

Defect Type

of

in

Artificial Neural Network to Discrimination

Automatic Radiographic Testing of Weldsof

Kimiya AOKIand YasuoSUGA1)

Student, Graduate School of Keio University, Hiyosi, Kouhoku-ku, Yokohama,223-8522 Japan.1) Faculty of Science and Technology, Keio University, Hiyosi, Kouhoku-ku, Yokohama,223-8522 Japan.

(Received on February 25. 1999.• accepted in final form on May11. 1999)

The X-ray radiographic testing method is often used for detecting weld defects as a non-destructivetesting method(N DT)

.Dueto the difficulties in identifying small defects from the X-ray film, skilled laborers

should be trained. However, recently it has been difficult to employ ski[led laborers. Moreover, for theidentification process, not only the laborers skill influence the testing result, but also it is difficult for skilledlaborers to assess small flaws within a short time, In comparison, computer visual image processing systemhave somegood characteristics, allowing objective assessment, high speed judgment, non-human's erroretc. Therefore, an image processing system allows weld defects to detect using X-ray radiography in thepresence of background noise, This paper deals with an image processing method for displaying defectsby computer graphics. Furthermore an application of neural network to discriminate the type of defect wastried. As the result of the investigation, it wasseen that the system constructed is effective to the detectionand discrimination of small weld defects clearly.

KEYWORDS:welding; nondestructive inspection; X-ray film; image processing; neurai network.

1. Introduction

Several types of non-destructive testing method areused for detecting weld defects. Because the X-rayradiographic testing method is particularly useful in

inspecting the inslde of weld metal, it is often used in

industries. Since skilled inspectors for X-ray radiographictesting are gradualiy decreasing, recently several methodsto detect weld defects from films automatlcally havebeeninvestigated to improve the processing efficiency andquantify the inspection results.1 ~9)

However, a X-ray film involves a numberof noise,

and defect Images showvery low contrast and variousshape in spite of the samekinds of defect. Moreover,boundaries between defect image and background areunclear, and it makesdifficult to automate the inspec-tion of X-ray films. Therefore, in this study, the imageprocessing algorithms named"Background SubtractionMethod", and "Region GrowmgMethod" were appliedto detection of defect images.lo) Moreover, an ap-plication of the neural network to judge types of de-tected defect images by characteristic values of themwasconstructed.

2. Experimental Equipment

The films used in this study are acquired by X-rayinspection of welded joints of steel pipes obtained by thesubmerged-arcwelding process. Thefilms include severaldefect images, such as blowhole, slag inclusion, crack or

incomplete penetration. An illustration of relations

between weld bead and X-ray film image is shown in

Fig. l.

Figure 2 shows the arrangement of equipment. Thesystem is assembled by an image scanner, a personalcomputer and so on. Defect images in a X-ray film aretaken by the image scanner or the CCDcameraand theimage data are transmitted to the personal computer.Data are analyzed and processed in the computer, andthe result is shownon a screen. The resolution of imagedata is 240dpi and gray 256 Ievel.

3. Characteristic of X-ray Film Image

Figure 3(a) shows an original image and 3(b) abrightness distribution corresponding to 3(a). Area of

Penetrame

ast nlet

\~

/1numb

Fig, l, Relations between weld bead and X-ray film im'age.

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ISIJ International, Vol. 39 (1999), No. 10

\

Lighting box

Dcfect

Cameraview

Fig. 2.

magescanner

Arrangement of equipment.

O(a) Beadimage

(b) Brightncss distribu[ion

Fig. 3. Characteristic ofbrightness distribution.

Fig. 4. Flowchart.

reinforcement is shownby brighter area in a X-ray film.

If a defect exists in the weld bead, it appears on the beadimage as a shadow. The brightness distribution has a10ng range distribution like a hill on a crossing direction

of bead and also unevennessalong a welding direction,

becauseof reinforcement of the weld bead. Whena defectis very small, it maybe mixed with these distrlbution

and noises. Anddefect images showvery low contrastand various shape in spite of same kinds of defect.

Moreover, the boundaries amongdefect images andtheir background imagesare unclear. These factors maymakethe detection and discrimination of defect imagesdifficult.

4. Outline of the Processing

The image processing system is constructed with two

Di. . . .~~

eaDefect

I_ /;'1

(b) ~ '~ F~

~-~~ '~ ' Defect ' ~

-HIFI~1(a) Brightness~,~s distributions)

1:s nevenness

--~ J (b) Brightness distribUtion (A) Onglna] bead image

_ ,~' '~

ea

~~~

(b) ~:

h --~I1:~

(a) Brightness

nevennessdistributlon

--HF J (b) Bnghtness distributlon (B) BackgroundimageJlr:t (a)

eaDefect

~l

~~/Defect ~~~:

(b)

~"t:'q,~;

Defect --~I(a) Brightness

Evenness distribution

--HF J (b) Brightness distribution (C) (A)-(B)

Fig.

(b) Brightness distribution (C) (A)-(B)

5. BackgroundSubtraction Method.

main parts. In the first part, an image of X-ray film is

taken by an image scanner and the defect image aredetected clearly by someimage processing algorithms.

In the second part, the defect type of the detected de-

fect image is discriminated by the neural network. Figure

4showsa flowchart of the processing system.

5. Detection of Defect Images

5.1. BackgroundSubtraction Method(BSM)Figure 5 shows the principle of "Background Sub

traction Method(BSM)". Figure (A) showsoriginal im-

age of weld bead, (a) and (b) are brightness distribution

C 1999 ISIJ 1082

ISIJ Internationai. Vol,

2so

200

xl50

~~100

50

oPosition --~I

(a)

(A) Original bead image

2so

200

x 150

~roo

so

oPosition -~ I

(a)

(B) Backgroundimage

P.~*t*.* *"~--~ J="" (b) '""

*,,*Posni.* --~I

(*)

'""(b)

'""*""~~ J '"*~ Positi.*

Fig. 6. Resuhof subtraction.

across and along a welding direction respectively. Asmentioned above, a X-ray image of welding part hascharacteristic brightness distributions and they makethedetection of defect imagesdifficult. Therefore, the medianfilter is applied to eliminate granular noise, and BSMis

constructed to eliminate the long range distribution.

Whena defect exists in a weld bead, the brightness

of the area corresponding to the defect decreases.

Accordingly, a background image, (B), can be obtainedby supplementing this low brightness area by smoothedbrightness distribution. Then, the background image is

subtracted from the original image, and somesuspecteddefects indicated by lower brightness area are detected

as shownin (C). In this process, the estimation of the

background image is important. In this study, a methodto estimate a background image which is stated in theprevious paperlo) is applied and effectiveness of the

methodis confirmed. Figure 6showsthe effect of BSM.

5.2. Region GrowingMethod(RGM)Binary systematizing the subtracted image, it is

possible to detect defect images roughly. However, dueto the unevennessof film extra noise are also detectedat the sametime. Accordingly, in order to detect onlydefect images with unclear boundary, a new methodnamed"Histogram-Threshold Method (HTM)" and"Region Growing Method (RGM)" are proposed. Inthis process, a kernel of defect which is specially lowbrightness area in a defect image is detected at first, andthen somepixels composingunclear edge of the defect

are searched in an area surrounding the kernel. Some

39 (1999), No. 10

(a) Subtraction image

1083

Defect Class Noise Clas I

3000

h~,~

2000q,:lel,a,

~1000

220 225 230 235 240 4 250 25Bri htness Threshold

(b) Histogram of Subtraction image

~1,I

t hbi\\

~~~:- \.l~

\I~

(c) Imageot defect dass

Fig. 7. Histogr',un-Threshold Method.

pixels found are added to the kernel.

Figure 7showsthe principle of "Histogram-ThresholdMethod". This histogram is obtained from the subtractedimage in Fig. 5(C). Generally, the frequency of NoiseClass histogram is muchhigher than that of Defect Class.

Accordingly, a sharp gradient exists betweenNoise Classand Defect Class histogram as shownin Fig. 7. In orderto classify the two classes, a tangential line, which is

obtained by the least squares method, is calculated.

The threshold is determined as the cross point of thetangential line and the axis of abscissas. Then DefectClass pixels are developed in an image, and remarkablysmall connections of pixels are elimlnated. Theremainingelements are defined as defect kernels and applied to

RGM.Figure 8shows the principle of RGM.First of all, a

surrounding area is set around the remarked kernel (1),

and then suspected defect Images are found in thesurrounding area (2). Then images which link to kernel

or remain yet in spite of application of the erosion process(3) are incorporated with the remarked kernel (4). Thisseries of process is repeated while the region of the defect

image grows. The sameprocess is applied to all kernels.

Then, the detected defect image is trimmed by the noiseelimination filter. By this process, the defect image withunclear boundary are detected and detection of extraimages by unevennessof film is held back.

If a type of detected image is judged by expert systemor neural network which learns a rule of professionalinspector, the boundaries of defect image has to bedetected like recognizing by hurnan's (inspector's) senseof vision. Accordingly, RGMwhich is able to detect aunclear boundary of defect is effective as a previous

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ISIJ International, Vol. 39 (1999), No. 10

(1)

Setting ofSurroundings a times

(1) Labelingimages

Subtraction image

(2)

To find

Proposed Defectin Surroundings

(3)

Toshrinkand addsuspected defect

images

(4)

NewSurroundings

Fig.

~~

+ suspecteddefect images

~

I I~i~jl [Shrmking_j

:~~~~~~~(~1 7tlmes

~i

+

+(2)

~

8. Region Growing Method.

Kernel of

Defect

Detected

Defect

image

Fig. 9. Effect of image processing.

processing of neural network.The effect of the newimage processing algorithms is

shown in Fig. 9. From the figure, it is confirmed that

this algorithms are effective to detect weld defect im-

ages in a X-ray film.

C 1999 ISIJ 1084

(2) Definition oflocal area

Defect area Bnghtnessave. =Kd

Surroundings of defect

Original

: Brightness ave, = Ksd

image(4) Definition

If C> ThresholdC=K - K _> This imagesd d

is DefectElse -> not Defect

of Defect

Fig, lO, Elimination of imitation.

5.3. Elimination of Imitation

In the BSM,because the filter size is set up in the size

of the biggest defect imageexpected, the image(imitation)

which is not a defect image and caused by a big stagelike unevennessand so on maybe detected. Accordingly,10cal contrast of each detected image is calculated andonly defect image is selected. Figure 10 showsalgorithms.First of all, each detected image is labeled (1), and then

a surrounding area is set around each detected image(2). Then difference of a intensity between inside andoutside of the remarked imageon the original image (3),

and the detected image is judged whether it is a defect

image or not by the local contrast C(4). Defect imageshave higher contrast than that of imitations generally.

A threshold of this process is determined statistically by

samples.

Last of the previous processing, the detected images

are labeled in order to distinguish each other. Thelabeleddefect images are subjected to the next process so as todiscriminate type of defects one by one.

6. Discriminatior] of Types of Defect

6.1. Type of Defect ImageFigure 11 shows characteristics of each type of weld

defect image. At first, defect image can be classified

roughly into two types by its shape (Circular of Linear).

In case the shape of the defect image is roughly circular,

the defect imagecan be discriminated betweenablowholeand slag inclusion by the boundary shape, contrast andposition on the bead. In case the shape of the defect

image is roughly linear and it is located on an edge ofbead, the defect is an undercut probably. In case the

defect is located on a middle area of bead, it can bediscriminated between a crack and insufficiency ofpenetration by the boundary shape and contrast. As

ISIJ International, Vol. 39 (1999). No. 10

e)eOc:~

~u~)

~o

~

e~G)(:~es

J::;

vo

~~

.~l

L)

(D~~:$

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Slag inclusion

~"s~~

5~

o'ss*

E',

o~~

Undercut

D~~j I i=1 AL :

Defect

(5)Complexity

C5 = L* /A(6)ponnal coeft~rcient

C6=Ird2 /4A

(7)Heywouddiameter

C7 =D=~~7~~

Defect(3)Ratio of horizontai length to area

C3~M/A(4)Ratio of perpendicuiar length to area

C4=N/A

Incomp]ete penetrat]on

Fig. 12. Characteristic values of weld defcct.

Fig. Il. Classification ofdefect type

mentioned the defect type can be discrlminated roughlyby the characteristics such as position, shape, contrastand so on. Accordingly, in order to discrimlnate the

defect type, the characteristic values are measuredandcalculated.

6.2. Characteristic Values

In this study, following 10 characteristic values areproposed.Cl : Position. It Is information of the location where the

defect exlsts.

C2: Ratio betweenhorizontal andperpendicular length.

It is information to discriminate a rough shape of

a defect image.

C3: Ratlo ofhorizontal length to area. It is informationto measurea degree of a circle.

C4: Ratio of perpendicular length to area. It is

information to measurea degree of a circle.

C5: Complexity. Whenafigureis acircle, thisparameteris minimum. It is information to measure acomplexity of outline.

C6: Formal coefficient. It is information of a outline

complexity calculated by another factor with C5.

C7: Heywounddiameter. The diameter of the circle

which has the samearea to a defect image. It is

information of slze.

C8: Averageofintensity. It is information ofdepth. Thedepth on a film of a defect image is Important factor

to distinguish for an inspector too.

C9: Dispersion of intensity.

Tlle characteristic valL]es are measured.

Neur211 netY~*ork

OOOOO(~~~~(~~(~~ir~)ivc6~)c7 VcsV'c9 ~i'clo

connection IL= IO

'.""..'*." "*=,

OL=5

1085

BH Sl CR IP UC

Defect types

Fig. 13. Constitution ofneural network.

CIO: Contrast. A difference of intensity between inside

and outside of a defect image.

A concrete definition of these parameters is given in

Fig. 12. A type of detected defect image can be dis-

criminated by general analysis of these characteristic

values. However, several features are uncertaln, and re-

lationship amongcharacteristic values is very compli-cated. Therefore, in order to analyze these values syn-thetically, the neural network wasapplied.

6.3. Learning of Neural NetworkThe neural network with three layers is applied to

discriminate a type of defects. This time, an errorback-propagation algorithms was used for network

O1999 ISIJ

ISIJ International, Vol. 39 (1 999), No. 10

c:

ocQc:

'~

o!"

~5,~oe,eoc:~

~:a)o~

1OO

30

60

40

20

O[~] ;~

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Fig. 14. Effect of parameter on percentage of discriminationin neural network system.

Cl0,2 113

C6o,0266

BHo.0049

Fig. 15. Discrimination ofweld defect.

learning. In an actual learning of the neural network,characteristic values are transformed into relative values

from Oto I and then input to an input layer. Figure 13showsthe construction of the neural network. The inputlayer has 10 units corresponding to the characteristic

values. Theoutput layer has five units, becauseeach unit

corresponds to five types of a weld defect, slag inclusion,

blowhole, crack, incomplete penetration and undercut.

For example, in case of teaching blowhole image, the10 characteristic values are measuredfrom defect imageand input to the input layer. Onthe output side, the unit

corresponding to the blowhole is instructed 0.995 andthe others are input 0.005. The neural network outputs

some values on this input, the internal weights areupdated so the error betweenthe output of network andteaching datumwill decrease. Andthe neural network is

learned this teaching datum. In this time, the typical

teach data of 35 patterns wereprepared. If it is necessaryto subdivide the type of defects, teach data should beincreased. In order to verify whether each characteristic

values is effective, eachonewasremovedfrom input dataof neural network and the ratio of discrimination to theteach data wascalculated respectively. Andthe result is

shown in Fig. 14. Whenany characteristic values wasremoved, the ratio of discrimination decreased. There-fore, it is confirmed that all parameters havean influence

on the neural network.

6.4. Discrimination by the Neural NetworkThe learned neural network discriminates the type of

defect detected by the previous processing above men-tioned. That is to say, the 10 characteristic values

are measuredfrom an detected defect image automati-cally and input to the learned neural network. Thenthe neural network calculates and outputs values to five

units of the output layer. Because each output unitscorresponds to the type of defect, the output unit whichhas highest value indicates the input image's defect type.

Anexampleof discrimination process of type of defectis shown in Fig. 15. In this figure, the SI unit in the

output layer of the neural network shows the highestvalue of 0.9959 and it is confirmed that the neuralnetwork hasjudged that the defect type is slag inclusion,

As the results of experiments of 27 defects, 25 defects

are judged rightly. Therefore, the right discriminationratio is 92.6 o/o approximately. It wasconfirmed that the

system can be effective to detect and discriminate welddefects larger than 0.5mmandmaybe applied to practical

use.

7. Conclusions

In this study, application of artificial neural networkto discrimination of defect type in automatic radio-graphic testing of welds was constructed. Main results

obtained are summarizedas follows:(1) In order to eliminate the long range distribution

caused by the reinforcement, the Background Subtrac-tion Method(BSM)wasconstructed.

(2) By the Region Growing Method (RGM), thedefect image with unclear boundary are detected anddetection of extra images by unevenncss of film is heldback.

(3) It was confirmed that newimage processing al-

gorithms constructed in this study is effective to detect

weld defect images in a X-ray film.

(4) Somequantitative characteristic values deter-

minedby measuring shape and dimension of a detectedimage were proposed, and it was confirmed that the

parameters are effective to discrimination of the type ofdefects.

(5) In order to discriminate the type of the defect

detected by image processing, a neural network whichhas three layers was applied. As the input data, 10characteristic values of defect are used and output valueindicates the defect type automatically. As the result ofexperiment, it wasconfirmed that the system is effective

to detect and discriminate the defects.

In this study, the discrimination of the type of defects

detected by BSMand RGMusing neural network is

mainly investigated. Studies on detection of only defect

imagesandjudgementof imitations should be continued.So, we will continue studying on the automatic inspec-tion system of X-ray film and report in future.

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