The present research concerns automatic control of the mechanical quality of a weld on a circumferential component. A method of image processing is developed that searches for the limits of the weld melted zone for each angular position, so as to extract the evolution in terms of the main geometric characteristics of the zone (penetration depth, etc...). It is an image segmentation operation since it achieves the extraction of useful elements for interpretation.</p

Corneloup, G.

Gueudré, C.

Moysan, J.

Digital Repository @ Iowa State University (ISU)

WELD INSPECTION BY RADIOSCOPY: COMPUTER SIMULATION AND AUTOMATie SEGMENTATION C. Gueudre Boursiere de Doctorat CEA - Region Bourgogne CEA-Centre d'Etudes de Valduc 21120 Is-sur-Tille France J. Moysan, G. Comeloup MECASURF - Laboratoire de Contröle Non Destructif (EA 1429) Institut Universitaire de Technologie 13625 Aix en Provence Cedex France INTRODUCTION The present research concems automatic control ofthe mechanical quality ofa weid on a circumferential component. A method of image processing is developed that searches for the Iimits of the weid melted zone for each angular position, so as to extract the evolution in terms ofthe main geometric characteristics ofthe zone (penetration depth, etc ... ). lt is an image segmentation operation since it achieves the extraction of useful elements for interpretation. The variable X -rayed thickness, due to the tangential irradiation of the circular component, does not allow us to interpret the radioscopic image simply. This is why we were led to simulate its formation. The characteristic transition of the weid boundary corresponds to the interface between the melted zone and the parent metal. It is obtained by radioscopy foreachangular position ofthe component. lt is brought to the fore by the space distribution of certain gradients ofthe image. The distribution of gradients according to the penetrated thickness is studied, taking into account the real noise. The processing parameters are directly linked to its evolution in the image. The set of the different boundaries obtained is finally processed by a method of deformable models. Testsmade on real welds show the strength and the performance of the algorithm: the boundaries can be detected unambiguously for each of the 180 images obtained every 2 degrees, and the whole set gives a perfect knowledge of the weid quality. Review of Progress m Quanlltaflve Nondestructive EvaluatiOn, Val /6 Edtted by D.O. Thompson and D.E. Chimen!t, Plenum Press, New York, 1997 813 STUDY DESCRIPTION The examined welds are heterogeneous welds obtained by electron beam on circumferential hollow components whose inner volume is filled with absorbent material. This component geometry only allows tangential irradiation. The complete control of a weid of this type is achieved by a succession of tangential radioscopic irradiations obtained at the different angularpositionsei ofthe component. The beam ofphotons is in the plane of the weid and tangential to it. The image obtained, for the angular position e i=Oo, represents the weid profile (Figure 1). Local variations in welding parameters may entail a deviation in the fusion zone. The weid in this case may not correctly play the roJe of mechanicallink The expertise mainly aims at measuring geometric characteristics such as the penetration depth of the welding bead or its dissymmetry. The process consists of extracting the boundaries of the weid proflies from each radioscopic image in order to characterize the general shape of the welding bead. ANALYSIS OF RADIOSCOPIC IMAGES The detection of weid profile boundaries is not simple. Characteristics typical of our control type disturb the quality of radioscopic images. The consequence of using tangential projections is that several profiles are piled and this degrades the contrast between the melted zone and the parent meta!. Because ofthe circular geometry ofthe component, the beam radiates through the variable thicknesses that are important at the bottom of the bead and small in the out er component of the weid The gray Ievel that corresponds to the melted zone is therefore not constant and the bottom of the bead is hardly visible W e decided to simulate the radioscopic forrning process in order to define a segmentation technique which was adapted to the problern 814 0 Welding bead sect10n R OUI ,_ f D parenl melal I • mclled zonc D absorbenl m alerial pd pcnetration dcplh Rmn inner radius of componenl Rout outer rad1us of componenl z' imagc line (ccntral axis) R' 1mage column (Irans ersal ax1s) Figure 1. Formation of a weid profile image at an angular position ei· SIMULATION OF A RADIOSCOPIC IMAGE The final digital image that contains the information useful to the control depends on the different elements that make up the radioscopic system (1-2]. The radiant image is distinguished from the digital one. The first image is characteristic ofthe radiation transmitted by the component to be controlled. The digital one is formed and transmitted by the detection system Radiant Image The X-ray tube focal spot is not a point source but has a visible size which depends on the source used In this case, a microfocus tube is available to us, which reduces the value of the geometric unsharpness. This configuration makes a projective rnagnification possible. The flow of photons emitted by the source and penetrating a given area is attenuated by the interaction of a certain number of them with the matter. F our types of interaction can be distinguished: the photoelectric absorption, the Compton I Rayleigh scattering and the pair production. The importance of the four types depends on the incident energy of photons and on the atomic number of the area. Therefore, in the energy field used for the present study, only the Compton scattering and the photoelectric absorption are to be taken into consideration. The global attenuation of the material is brought to the fore by the radiation that has undergone no interaction whatever with the matter. lt is given by: (1) where I is the intensity of the nonabsorbed ernerging radiation, 10 the intensity of the incident radiation, J.1 the linear attenuation coefficient of the material and x the thickness ofthe penetrated material. Moreover, the radiation that is scattered by the matter and that is not re-absorbed adds to the information and degrades the image quality. The intensities that correspond to a line of the radiant image for the coordinate z' are obtained taking into account the two kinds of penetrated material, the parent metal and the melted zone, whose respective thicknesses are xpm and Xmz, and whose respective attenuation coefficients are J.lmn et J.lmz (see Figure 1). Foreach value ofR that varies between the inner radius Rinn and the out er radius Rout of the component, the ernerging radiation is: This calculation is repeated for alllines z of the image in order to form the simulated image. Digital Image (2) The detection system including an analog-to-digital converter changes the radiant image into a digital image. The response curve which gives the relation between intensity and gray Ievel must be established. 815 The technique used consists of radiating a step-plate object whose material is identical to the parent metal. The thicknesses ofthe step-plates increase at a constant rate and comply with the range of thicknesses that are penetrated by the tangential radiation. The average gray Ievel Gl3 is calculated in each of the resulting image areas. This Ieads us to obtain the curve Gl3 = f(xp) by a cubic polynomial approximation, according to the thickness Xp of each step-plate. To obtain this responsein intensity, calculating for each thickness the intensity ofthe ernerging radiation given by expression I is sufficient. The gray Ievel columns of the real and the simulated image are calculated in the transversal axis R' ofthe weld profile (see Figure I). This Ieads us to note the success of the simulation as the Ievels ofthe two columns are almost identical (Figure 2). The transition between the parent metal and the melted zone of the two images is not sudden but is achieved according to a shading of gray Ievels. The contrast is therefore weak and does not permit us to use a traditional detection method based on a search for boundaries ofhigh contrast. Knowing the location of the weld profile boundary of the simulated image shows that it can be defined by the ends of a plateau, thus marking the Iimits of a homogeneous area in gray Ievels. A segmentation technique based on searching for this plateau, according to each column ofthe image, seems tobe an adequate solution to determine the Iimits ofthe weld melted zone. The presence offluctuations in gray Ievels should be noted on the real image column. This presence should be taken into account to detect the searched boundaries. SEGMENTATION OF REAL WELD PROFILES The tluctuations observed on the real image columns are due to the integration of the different types ofnoise ofthe acquisition system (noise arising from the quantum statistics ofthe X-ray radiation and from the electronics). Studying the columns at different places of the real image led us to notice that the noise changes according to the penetrated thickness and that the fluctuations are more important when the penetrated tangential thickness is small. 100 80 "' ~> ~ 60 ;;.... ~0 . "" ö 20 0 Parcnt meta! Parcnt mctal ~0 ~ ... . ....... . ............. - .. . '( • vvv--·..-, ~ J' ---.. • ......---,. ""-:1 Mcltcd zon~· !' :I I ' ·- · ·---·._., Homogeneaus platcau Pixels Simulated image Realimage Figure 2. Gray Ievel columns of a real and a simulated image in the transversal axis R' ofthe weld. 816 Our first suggestion therefore is to quantifY the amplitude of these fluctuations in order to include them in the segmentation algorithm. To do so, we analyzed the image in terms ofgray Ievel gradients. The calculation ofthe gradient is made on a research area equal to the geometric unsharpness ( estimated to be seven pixels) in order to determine the transition between the parent meta! and the melted zone. lndeed, ifthe calculation was made on two consecutive pixels, the information would be drowned in the column fluctuations. Analysis of Gradient Distribution According to the Penetrated Thicknesses This analysis is made on the weid profile real image in which six areas called An (where n varies between 1 and 6) are defined. Each area corresponds to a range of different penetration thicknesses (A1 corresponds to the smallest thicknesses and ~ to the greatest). The problern is to determine a reference gradient value, rg, that best allows us to characterize each area. The results are expressed as percentages of the total number ofpoints ofeach analyzed area (Figure 3). For example, 100% ofthe points ofarea ~ are represented by points whose gradient is less than or equal to 3. The value of the reference gradient is in this case considered as being equal to 3. Search for the Limits of the Melted Zone We are first interested in the weid profile detection ofthe initial image obtained at ei=()O, Weshall then extend the method to the following images. Figure 4 represents a gradient column in area ~· We wish to extract the lateral ends of the melted zone from the column. This operation is made simple by the presence of points of maximum gradient which surround the desired area. It allows a pre-location of the plateau as the simulation reveals that the points of maximum gradient result from the pile of weid profiles and do not correspond to the desired transition. On the other band, from these detected points, we searched for the first points whose gradient was smaller than or equal to the reference gradient. These points actually correspond to the Iimits ofthe melted zone. This principle is successively repeated for each image area, taking each time the corresponding value ofthe reference gradient. After the research, some abnormal points that do not belong to the Iimits of the melted zone are eliminated by continuity constraints based on the necessary space proximity of the points. The result is shown on Figure 5. a. 100 ~- ·------- ._Al ----'0' -- -o ./ A2 ~ G) A ~ c QJ A4 ~ G) i:l.. 0 A5 20 . A6 0 0 2 3 4 5 6 Reference gradient Figure 3. Definition ofa reference gradient for each area An ofthe image. 817 111 ... c .., 'ii "' .. 0 30 25 20 15 10 ' 5 I Parcnt melal (rg = 3) a I I' I c I I Mcllcd /Oll~ (rg= 3) 0 \' " 'I r.l l 1-'J.--~·o......L.• "-'1 ...I:...__ilL__-t' I A 0 40 80 b i,f; 120 Pixels a· ftrst pomt of maximwn gradrent l b. socond pomt of maxrmwn gradrcnt c: se<uch arca of mcltcd zonc lnmts Pnrcnt meta) (rg= 3) {I II I ' 'I I --4-160 200 240 Figure 4. Search for the Iimits ofthe melted zone on a gradient column ofarea ~· Boundaty Extraction The thus determined points must be connected to define a unique and closed form. The B-spline curves are an interesting tool to close the proflies and have deformation properties that allow us to initialize the segmentation of the following images of the series (3]. These curves have often been used in segmentation techniques based on deformable models. This method was first applied by Kass, Witkin and Terzopoulos (4], then studied and generalized by other researchers [5-6-7]. The general principle is to initialize the B-spline curve in the centre of an homogeneaus area and deform it, in an iterative way and according to energy measurement criteria, in the direction of the required boundary. Control ofthis energy evolution informs us ofthe presence ofa minimum that relates to the optimal correspondence step between the B-spline curve and the boundary. In the present case, the points corresponding to the Iimits of the melted zone being previously detected, we can define a deformable model adapted to our problem. Constraints based on prior knowledge of the weld are added to the model to allow easy completion ofthe boundary in the bottarn ofthe bead and on the profile surface. The bottarn of the bead is a much disturbed area that must be detected precisely as it has direct influence on the penetration depth measurement. Micrographic tests teach us that it is characterized by a minirnum radius of curvature, which imposes a constraint on the radius ofthe B-spline curve in the bottarn ofthe bead. The second constraint is to push the boundary towards the profile surface (Figure S.b). This area is characterized by points of maximum horizontal gradients. The initial image being segmented, we use the deformation properties of the deformable model to achieve the segmentation of the following images in the series. The method consists of sufficiently dilating the initial image B-spline curve 0 (9i=Oo) so that it can surround the weid profile of image 1 (9i=2o ). From that position, applying the previously defined detection algorithm once again is sufficient. The B-spline curves allow us to globally follow the Iimits of the melted zone on the whole set of the 180 radioscopic images, whilst considering the segmentation result on an image as the initialization of the segmentation process for the following image. 818 a) Constraint on cun•c radius b) B-splmc curvc Constraint on / prolilc surfacc Figure 5 a) Extraction ofpoints corresponding to the Iimits ofthe melted zone in each area An, according to the image columns. b) Segmentation ofthe initial image at angular position ei=Oo· WELD QUALITY CONTROL From the general shape ofthe weid calculated by our algorithm, we developed several shape and position parameters that quanticy the weid aspect and assess its quality. In the present case, the penetration depth is the most significant parameter to check whether or not the weid correctly performs its mechanicallink role. Figure 6 shows the results of the control on a welding bead. The penetration depths calculated on the 180 images ofthe series are represented on it (every 2 degrees). The fluctuations that can be observed in the bottom of the bead originate from the natural physical characteristics of the welding by the electron beam method. They are made perfectly obvious by our segmentation algorithm Figure 6 . Automatie control ofthe welding bead (arbitrary scale). 819 CONCLUSIONS In the present study, we presented a segmentation method that can be applied to digitized radioscopic images of a circumferential seam, and that automatically allows control ofits mechanical quality. A simulation allowed us to foresee on the image the gray Ievel variations obtained from a homogeneous weid. The Iimit between the melted zone and the parent metal can then be defined as being the ends of a plateau, defining a constant gray Ievel area. The gray Ievel fluctuations, induced by the real noise of the acquisition system, have been included. They were quantified in terms of gradients and according to the thicknesses penetrated by the tangential radiation. A reference gradient value is then given to each image area, which finally allows the extraction of the points of the melted zone Iimit. The so detected points are then connected thanks to a deformable model method adapted to our application, allowing us to trace the boundary of the melted zone. The method deformation properties allow the segmentation of the set of the weid radioscopic images. This method perfectly lends itself to a fast and automatic control of manufacturing processes. The calculation time necessary to segment a series of 180 images obtained every 2 degrees is about 80 seconds on a 486 DX2-66 PC and can thus be improved. Our control has shown its efficiency and strength in an industrial environment. At present, we are working on the possibility of detecting a defect in the weid by the shape irregularity analysis ofthe extracted boundary. ACKNOWLEDGEMENTS This research is supported by the 'Commissariat a !'Energie Atomique' - Center of Valduc and the Burgundy Region, in collaboration with LABOMAP-Cluny and MECASURFILCND-Aix en Provence laboratories. REFERENCES 1. A.B Della Rocca, S. Ferriani, L. La Porta, "Computersimulation ofthe radiographic image forming process: implementation and applications", NDT & E International (1995), Vol. 28, No. 3, p. 163-170. 2. V. Kaftandjian, "Reconnaissance automatique de defauts dans les produits metalliques par radioscopie numenque", Ph.D. Thesis, INSALyon, 1995. 3. C. Gueudre, J. Moysan, G. Comeloup, "Contröle qualite d'une soudure par traitement numerique d'un volume de donnees radioscopiques", Colloque international sur le contröle qualite par vision artificielle, IUT Le Creusot (1995), p. 7-18. 4. M. Kass, A. Witkin, D. Terzopoulos, "Snakes: Active contour models", Int. Journal of Computer Vision (1987), p. 321-331. 5. L.D. Cohen, I. Cohen, "A finite element method applied to new active contour models and 3D reconstruction from cross sections", Proceedings ofthe International Conference on Computer Vision, Osaka Japan (December 1990), p. 587-591. 6. N. Rougon, F. Preteux, "Deformable markers: mathematical morphology for active contour models control", Proceedings ofthe SPIE Conference (1991), p. 78-89. 7. E. Maurincomme, D. Friboulet, G. Finet, I. Magnin, J.H.C Reiber, "Adder: a snake-based segmentation approach for intravascular ultrasound images", Proceedings of the 2nd conference on Digital Image Computing: techniques and Applications, Sydney Australia (December 1993). 820 

Weld Inspection by Radioscopy: Computer Simulation and Automatic Segmentation

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Weld Inspection by Radioscopy: Computer Simulation and Automatic Segmentation

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