Proceedings of the National Seminar & Exhibitionon Non-Destructive Evaluation

NDE 2011, December 8-10, 2011

Where I, I0 are respectively the transmitted and incident photonenergy intensities, μ the absorption coefficient and x thethickness of the medium. On recording the transmitted energydata on to a x-ray film or a digital screen makes the image inblack and white densities. The density is a measure of degreeof darkness of an image. The Figure 1 is a density strip thatshows different densities that can be seen on a radiograph.The density depends mainly on the transmitted energy photonthat falls on the recording medium. The density measurementis a measurement of transmitted photon. The transmitted

DEFECT PROFILE – A DECIPHERING FEATURE FOR DEFECT CHARACTERISATION

S. AdalarasuQIT, QCG, MME, Vikram Sarabhai Space Centre, Trivandrum-695022

ABSTRACT

Recent advancements in computerized imaging technologies have provided ample opportunities for formulatingprograms to study the image of a defect and to understand its nature. Hitherto well-experienced inspectors trained inradiography interpretations do the image identification in a radiograph and correlating the image to a possible defect.The interpreter merely based on his mind set defines the defect characterization and this is highly heuristics. There isno rule-based methodology for interpreting the images and correlating to its characteristics. The influence of illusionsin interpreting x-ray radiograph is very high. To overcome this problem, initiatives are on to automate the flaw indicationinterpretation using ANN. The ANN also requires numerous training with efficient features for flaw charecterisation.Though there are many characteristic features that can jointly identify the defect nature, the feature defect profiling isunique as it distinguishes planar defect and a volumnar defect efficiently. Even this feature alone can be attempted byinterpreters without any automation to fix the defect character in cases of ambiguous images. Details of essentialimage processing techniques and procedures adopted and its outcome are explained in this paper. This paper highlightsthe beneficial usage of this methodology over the historical way of interpretation. The knowledge-based way ofinterpretation can be transformed to rule based interpretation by using this approach. The feature that can distinguisha planar and a voluminar defect is identified and its effectiveness is also demonstrated. The procedures for profilingthe surface of the defect using the image intensity and a developed MATLAB program will indicate the nature of thedefect surface profile. This methodology can also be adopted for estimating the surface finish of in accessible regionswhere the conventional method could not be adopted. The appropriate tools for image processing are explained in thispaper. Defect Profile can be one of the effective features for developing an artificial neural network that can identifyand differentiate various types of defects.

Keywords: Interpretation, Heuristics, ANN, Surfing, Profiling

INTRODUCTION

The x-ray radiography is an approved and well-received testingtechnique for evaluating the integrity of material and to assessthe soundness of the weld joint or the product. The principleof this technique is differential absorption of electromagneticwaves when it is transmitted through a medium. The absorptionis the major subset of attenuation in a material. The absorptionof electromagnetic energy depends upon of the characteristicnature of the medium [1]. This energy absorption is measuredin terms of mass absorption coefficient or linear absorptioncoefficient. The linear absorption coefficient is same as massabsorption coefficient but per unit length. The mass attenuationcoefficient depends upon the effective atomic number and theelectron density of the medium. These two parameters are basicquantities that determine the penetration of x-ray and photonsin a medium. The measurement of attenuation coefficients ofphotons in materials is of significant interest in determiningthe image intensity. The governing equation of photon energytransmission is

I = I0 e- µ x (1) Fig. 1 : Film density Strip (Kodak)

photon differs from the incident photon due to the absorptionof energy by the medium. For a material the transmitted energyis proportional to the thickness of the medium as the effectiveatomic number and the electron density of the medium isconstant. Even a small change in thickness of the medium canmake a variation in the image density. But in case of filmradiography, the image being an analog image, measurementof minute density variation detail is difficult and will beinaccurate. The error in density measurement will be more ifthe image density falls in the shoulder region of the filmsensitometry curve {2,3}

The digital radiographic images obtained with the FPD haveexcellent uniformity, repeatability, and linearity, as well asmodulation transfers function (MTF) and detective quantumefficiency (DQE) that are superior to any other conventionalimage acquisition device. In case of film radiography the filmlatitude will be linear for a limited range of exposures. On thecontrary in FPD system, the dynamic range is considerablylarge and hence minute changes in image density will not beleft unnoticed. However the contrast resolution of a FPD isfar better than a film mainly because the images in FPD are of12 or 14 bit depth. The color depth or bit depth, is a computergraphics term describing the number of bits used to representthe color of a single pixel in a bitmapped image. This conceptis also known as bits per pixel (bpp), particularly whenspecified along with the number of bits used. Higher colordepth gives a broader range of distinct colors. Color depth isonly one aspect of color representation expressing how finelylevels of color can be expressed{4,9}. The higher the numberof colours then the more realistic the image will appear. Onthe other hand the number of individually addressable pointsthat make up the image determines the resolution of an image.In case of FPD, it is the number of pixels that make up a screenimage. In case of films the number of lines that make up animage. The resolution of the films is of the order of 10 lpm,that of FPD is of the order of 4 lpm. Higher the resolutionthen the more detail the image can resolve and the sharper itappears when viewed. The details of an image, properlyharnessed, can yield an insight into the defect. Even thecharacteristic nature of the defect can be extracted from suchimages. In this paper one such attempt wherein the profile ofa defect is deciphered using the image depth information.

DEFECTS AND ITS PROFILE

Defects in any manufacturing process are an inevitable one.The normally occurring defects in welds are crack, lack offusion, and lack of penetration, porosity, piping, tungsteninclusion and undercuts. The underlying cause for porosity is

the entrapment of gas within the solidified weld. Lack of fusionis the poor adhesion of the weld bead to the base metal;incomplete penetration is a weld bead that does not start atthe root of the weld groove. Incomplete penetration formschannels and crevices in the root of the weld which can causeserious issues in pipes because corrosive substances can settlein these areas. These types of defects occur when the weldingprocedures are not adhered to; possible causes include thecurrent setting, arc length, electrode angle, and electrodemanipulation. But all the defects are not tolerable in the enduse of the product. The planar defects are considered to be thefunctional critical defects rather than volumnar defects. In x-ray radiography these defect images can be distinguished byits appearance. In automated interpretation, the geometricalfeatures extracted from these images distinguish the planarand volumnar defects. But there are cases wherin the defectspiping and hot cracks are to be distinguished using many moreextracted features. In such cases if the defect profile is alsotaken into consideration, then the defect classification will beprecise and unambiguous. The radiographic image is aprojected image of a defect on a plane. The defect however isof three dimensions. This is true in case of digital imagingalso. However in case of digital systems, as the defect imagesare of 12 bits or 14 bits these images are embedded with moredetails about the surface condition of the defect as the x-raysis transmitted through the surface of the defect duringexposure. In order to establish the concept that the images areembedded with the details of surface roughness/profile of themedia through which x-ray passes, an experiment usingstandard surface roughness blocks were designed andconducted. The standard surface roughness block will be withthe details as shown in figure.2. One such standard roughnessblock, as shown in figure 3. is used for the experiment. Thisstandard set of roughness block is a face turned roughnessplate specimen as shown in figure.4 and this block is used inthis experiment to validate the adopted methodology for defectprofiling. This reference block is a product Microsurf 319 fromRubert & Co. Ltd. Sections 1 to 8 represent areas in decreasingorder of surface roughness. Each area is represented by aroughness number (N classes) according to BS 1134/1972 &ISO/ R 468. The surface roughness is expressed in terms ofRa i.e., Area average Radius and its tolerance is Ra +12% to -17%.

1) N12 Ra 50 µm - CLA 2000 µ”

2) N11 Ra 25 µm - CLA 1000 µ”

3) N10 Ra 12.5 µm - CLA 500 µ”

4) N9 Ra 6.3 µm - CLA 250 µ”

Fig. 2 : Details of surface roughness Fig. 4 : Roughness BlockFig. 3 : Surface Roughness Blocks

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416 Adalarasu : Proceedings of the National Seminar & Exhibition on Non-Destructive Evaluation

5) N8 Ra 3.2 µm - CLA 125 µ”

6) N7 Ra 1.6 µm - CLA 63 µ”

7) N6 Ra 0.8 µm - CLA 32 µ”

8) N5 Ra 0.4 µm - CLA 16 µ”

This set of roughness blocks are of 2mm thickness and are X-rayed using a 225 Kv X-ray unit and the images are graphed,processed and analysed using a 14-bit FPD & RHYTHMREVIEW software. The images of specimens (shown infigure.5) are acquired & each roughness area is cropped. Theimages are read using MATLAB Ver. 7.8. Then the image isread to a unit 8 variable and surfing operation was executed.Then the isometric views in XZ and YZ planes of eachroughness portion are developed. Once again the data in uint8

is converted to double format and surfing operations areexecuted. The isometric view and XZ view of N5 so obtainedis shown in figure.6. By definition the surface roughness isdefined within a length of 0.8mm, the same length is to beconsidered in the image also. Length of Roughness classportion measured as 23.58mm. The DR image equivalent tothat of 0.8mm is cropped & converted to .tiff type. Bycalculation for the FPD used in this experiment linear lengthof 0.8 mm is measured as 8 pixels. So the image is croppedand the portions to be analysed are taken as portion of 8x4pixels. Then these portions are read in the program and Ravalue for each portion is analysed. Corresponding MAT LABcoding for analyzing the portion from roughness area N8 isshown in Fig. 7 {5-8,10}.

Here the output is 3.2608 which is close the actual value. Theprocess is repeated for the other images and the values obtainedare as shown in Table.1. As the methodology is found to bemost appropriate, the same technique is repeated on the defectimages also. The image of porosity in figure-8 is analysed andthe observations are shown in Figures 9,10.

The image is cropped to 1.5 mm x 1.5 mm analysis portionincluding porosity completely is calibrated & cropped usingRHYTHM REVIEW software. Analysis window taken hereis of 15 x 15 pixels size. Similar way the image of incomplete

Fig. 5 : Digital image of the specimens

Fig. 8 : Defects ImageFig. 7 : MATLAB Coding

Fig. 6 : Isometric & XZ plane Views of N5 Roughness Block

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fusion also analysed. The observations are shown in Fig. 11,12.

RESULTS AND DISCUSSIONS

From the above observations it can be seen that the views ofthe defects and their image depth details variation alonglongitudinal and transverse directions are distinctly different.The porosity, being a volumnar defect, shows a smooth surfaceprofile. On the contrary the crack, being a planar defect, showsa varying profile in its views and defect depth detail variationsalong longitudinal and transverse directions. The characteristicsurface profile difference of these two defects is due to itsnature of occurrence and it is well understood. However thismethod for extracting this feature of defect profile differenceis much useful for configuring automated neural networks fordefect classifications. Also this method can be used forresolving ambiguous image interpretations.Fig. 9 : Views of Porosity (Isometric)

Views of Porosity (XZ Plane & YZ Plane)

Fig. 10 : Grey Level Variation Vs pixel length along the long l& trans directions

418 Adalarasu : Proceedings of the National Seminar & Exhibition on Non-Destructive Evaluation

CONCLUSIONS

In this study, a methodology using MATLAB for imageprocessing and a characteristic feature extraction fordeciphering a defect is tools are established. The proceduresfor estimating the roughness of the defect and thereby profilingits surface are developed here. The feature that can distinguisha planar defect and a volumnar defect is identified anddemonstrated. Using this method defects can be classifiedeither as a planar defect or as a volumnar defect. By thismethodology the historical way of interpretation using

heuristics and human knowledge base is transformed to rulebased interpretation. This experimental study needs to beextended to other possible weld defects for specific defectidentification.

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2. The Photographic Researches of Hurter and Driffled(1920, repr 1974).

Fig. 11 : Views of a Crack ( Isometric & XZ Plane)

Fig. 11 : Views of a Crack ( YZ Plane)

Fig. 12 : Grey Level Variation Vs pixel length along the longl & trans directions

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3. http://www.answers.com/topic/sensitometry-and-film-speed#ixzz0i9X9rMuW

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