·Table of Contents
·Computer Processing and Simulation
Computer Technologies and X-Ray Flaw Detection of WeldsA.E. Kapustin, I.I. Bardusova
Computer Technologies Department.
Research Design and Technology Institute for Welding and Protective Coatings with Pilot Production (WPC-I)
12-B Platonov Street, 220071 Minsk, Belarus
Phone: +375 17 239 98 88
Quality of X-ray films interpretation by a flaw detector operator always bears rather a subjective character. Such factors, as qualification level of a flaw detector operator, their state of health, fatigue, sharpness of sight, limited evaluation capability, and also quality of the X-ray images (fuzzy boundaries, bad visibility, weak expressiveness of the defect image in relation to background etc.) render their negative effect. Welds quality assessment also requires execution of defined complex of metrological and calculation-analytical measures, which are hard to carry out due to absence of tools for realization of such operations. It is obvious, that the quality of flaw detection of welds depends to a large extent upon qualification and skill level of the experts in the given area.
Proposed is method of implementation of computer technologies in area of X-ray flaw detection of welds, as a way to solve each of the above-stated problems.
Gained experience on computer interpretation of X-ray films proves, that the technology of the defect analysis should be defined by the following positions :
|Fig 1: Groove-type standard as per GOST 7512, where 1,2,3,4,5,6 - grooves.|
|Sample Number||Depth of the Grooves||a||b||c||h||L|
||Table 1: (millimeters)|
Figure 2 gives computer modeling of side views of calibration standards, which are far from corresponding to their image as per mentioned above GOST, which provides occassion to certain speculations. The possibilities of computer image processing, on one hand, help in solution of the task of picture calibration and definition of its sensitivity, but, on the other - raise a number of questions on how and what an expert views evaluating standard.
||Fig 2: Computer simulation of groove-type standard "in section" on gray level value on X-ray film.|
As clear from figures 1 and 2, it is very difficult to judge visually on reliability of the information represented by standard, and, moreover, then to do any evaluation of defects on a film. To solve such problem in an efficient and timely manner without computer facilities powered with appropriate program processing appears practically impossible.
Film calibration can be conducted by indicating to the program unit under standard image its extreme angular points. Sensitivity can be defined by the program, as the least depth of a groove, detected on a film, on the indicated longitudinal axial line and calculated according to GOST 7512 . After that, its image is modeled in horizontal projection and probabilistic values calculation of the standard surface, which should have been received on the film, takes place.Figure 3 gives visual representation about the obtained calculated standard imposed on computer-simulated image of standard. If forcefully to place on film (figure 3) calculated background level against standard, it is obvious, that the latest groove will not be visible.
As practice shows, deviations of calculated values of gray levels from those eye-perceived is observed on majority of films. Calculated standard, imposed by such method, will always fit in average range of relative error, available on film.
|Fig 3: Actual (1) and calculated (2) allocations of gray levels against standard (3).||Fig 4: Standard and part of image simulated thereupon. Zone (1) of calculated surface of standard everywhere has identical grey level. Zone (2) - grooves of standard.|
Proceeding from true dimensions and standard image on the X-ray film itself, a conclusion on information reliability, on the basis of which a flaw detector operator assesses a film, is made.
Having input datas of brightnesses of standard points and analysis data for simulation of calculated standard, it is possible to model the image of standard, which should be reflected on a film. A variant of such simulation is represented on Figure 4.
There is a problem - simulated area of surface of standard in a zone of the first groove "lighter" than surface of standard in a zone of the last groove (Figure 4). Typical example of human perception of image not absolutely corresponding to that reflected on a film. Analysis of gray level of restored surface of standard shows, that it is everywhere identical. In the given situation comes into force the psycho-physiological law of Weber-Fechner, according to which distinction in visual perception/sensation at examining of two surfaces with various brightness is directly proportional to contrast of brightnesses of these surfaces . Our perception is deeply affected by imaging gray levels of standard grooves, base metal background on a film, sharpness of sight and other factors. What do we see on films and how can we evaluate the information represented by standard without the appropriate equipment? How to incorporate the psycho-physiological law of Weber-Fechner at film interpretation?
|Fig 5: Results of interactive analyzer programs operation.|
As experimental running of programs shows, reliability of selection on visual perception coefficient, calculated as per indicated formula, makes 75-80 %. Calculation based on a more complex technique increases reliability up to 90%. Values of visual perception coefficient Kr. permit for a flaw detector operator to evaluate reliability of described boundary, as a defect, at low contrast of squares of the outline and surrounding.
Program search and selection of defects allow to study in detail each described defect. For this purpose, the grid chart is superimposed on a selected search area and the defects are "marked" according to their initial values of coordinates. The values of obtained defect parameters are mapped in the special information window. The additional service functions allow to increase the size of the image of selected defect, to examine it layerwise "in section" both on axes X and Y. It is possible to model 3D image of defect at its symmetric conditionally layout depthwise (axis Z). At separate command, indication of special parameters, such as the Weber-Fechner coefficient, calculated coefficient of visual perception, average brightness of defect, average brightness of defect surrounding, overfall of brightness in defect, overfall of visibility, threshold of defects search, is also possible.
The definition of the linear and depth sizes of the found defects, their squares, perimeters, location, and also straight-line characteristics of weld itself does not represent large complexity. The transition from pixel sizes to the real sizes is made by recalculation on calibration coefficients obtained earlier.
Another problem is identification of types of the found defects. A simple, minor defect, e. g. in the form of single round pore in a raster format, represents a figure at best close to a rectangle. The more complex single defects are described as polygons of arbitrary configuration. Lack of fusion, perceived by a flaw detector operator as an extended continuous object, can be detected by computer as a collection of objects with intervals. If there are congestions of pores or slags, they, being superimposed against each other in the plane image, give the resulting object of the very complex geometrical form.
The program, by logical analysis of defects characteristics and their localization in a weld, with application of methods for selection of simplier defects from the complex ones, identifies with certain probability such defects, as pores, congestion of pores, slag, congestion of slags, lack of fusion and crack. However, final identification of defect type is kept at expert's discretion. A flaw detector operator in interactive mode can change the type of the defect, found by the computer, or in general to remove detected defect from the defects list, according to which weld quality is concluded, if they considers this defect being a film defect, not a welding defect.
However, the initial picture of the defects, found by the computer, will be all the same stored in the database. Figure 6 shows results of recognition and analysis of defects.
|Fig 6: Outcomes of operation of programs for recognition and analysis of defects. 2D vertical section and conditionally symmetric 3D image of the selected defect.|
|Fig 7: Fragments from the program of a learning complex.|
The proposed computer technologies solve only part of the problems of x-ray testing automation. However, there are a number of important problems, which require contining of the research with additional money and effort inputs. Unfortunately, the authors are currently unable to continue the work due to unavailability of the required funds and research equipment.
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