Table of Contents ECNDT '98
Session: NDT of Welds
Automatic Detection and Characterisation of Aluminium Weld Defects: Comparison between Radiography, Radioscopy and Human Interpretation.V. KAFTANDJIAN*, A. JOLY; INSA - CNDRI, INSA Bat. 303, 20 av. A. Einstein, 69 621 Villeurbanne Cedex;
T. ODIEVRE, M. COURBIERE, C. HANTRAIS; PECHINEY, Centre de Recherches de Voreppe, BP 27, 38 340 Voreppe.
*Corresponding Author Contact:
|TABLE OF CONTENTS|
2.1. Global enhancement
First of all, the grey-levels are stretched in such a way to use the whole dynamic range. Then, in case the illumination of the object in non uniform, or if the detector response is not the same in all points of the screen, there is a correction in the horizontal direction. During this stage, the grey-levels of the digitized films are inverted in order to be in the same range than radiocopic images (brighter grey-level for defects less dense than the base metal).
2.2. Limitation of the image to the weld seam
The limitation of the image to a region of interest (ROI) prevents from the detection of false defects outside the weld. This step requires two inputs : the weld width (or any width chosen by the user) and the pixel size. The procedure finds approximately the middle of the weld as the lowest grey-level of each column (the weld seam is horizontal)1. All the middle pixels are then fitted by a straight line in the mean least square sense. Knowing the width of the ROI and the middle line, a mask is generated and applied to the image. 1 If the weld is overpenetrated, the darkest pixel is the real middle, but if there is an incomplete penetration, the darkest pixel is slightly shifted from the middle. It is not so important as the image is then limited to a width chosen by the user.
2.3. Detection of lack and excess of penetration
Figure 1 : Raw images of a lack (a) and excess of penetration (b);
c) d) same images after a Laplacian filter and histogram equalization.
The intermediate image containing lacks and/or excesses of penetration is kept in memory, and in case the specification indicates that lacks of penetration are prohibited, the procedure can stop. The filtered and equalized image is no more used in the following, because the noise is too high to detect porosities. The next step begins thus with the image after global enhancement.
2.4. Detection of porosities
|| a)|| b)||Figure 2 : a) raw image and vertical profile along a porosity; b) flattened image and corresponding grey-level profile.
At this step, the image analysis is different for big and small porosities.
a) Big porosities : correlation with a round shaped mask
|Round shaped mask :|
simulation of an ideal porosity
The correlation processing consists in computing a degree of similarity with the mask for each pixel x, giving a higher level when the similarity is good. The similarity is here defined as :
s(x) = 255 - max(ai - yi), where ai are the mask coefficients and yi the corresponding values in the image. This processing can be compared to convolution masking  but is much more simple and quicker because it delivers directly the similarity degree. When applying a convolution mask, a parameter needs then to be computed on the filtered image in order to reveal the degree to which the image contains features that match the mask.
||a) || b)
||Figure 3 : image before (a) and after (b) correlation with a round shaped mask. The grey- level profiles show that this method enhance the defects and reduce the noise.
Figure 4 : Binary image corresponding to that of figure 3.
b) Small porosities
Porosities whose area is less than 8 pixels are considered as noise by the correlation step. Another method has thus to be found from the image obtained after flattening (figure 2b). This image presents too much noise to be thresholded directly. A low pass filter is first applied, with a small window in order that porosities are not so affected. This image is used as reference image for growing big porosities as explained before. For small porosities, we use the morphological top hat transform , but instead of a global threshold, we apply a neighborhood criterion (taking into account mean grey-level and standard deviation). A logical AND is used to have big and small porosities on the same image, thus ready for feature extraction.
|:||ratio of the porosities area over the area of 100 mm of weld|
|SAD :||maximal sum of aligned diameters|
|SLD :||sum of linear2 defects|
|MLD :||maximum diameter of linear defect|
Images were acquired on a standard medium resolution device. A mini focus X-ray tube (0.4 *0.4 mm) is associated to an image intensifier. The pixel size at the object level is 166 µm in size, and the geometrical unsharpness is kept smaller (0.15 µm). Operating conditions were optimized to reach the best sensitivity as measured by wire type Image Quality Indicator (3.3 % on 6 mm sheet).
Lacks of penetration are detected with good reliability. The smallest detectable porosity depends on the criteria chosen in the software : if very small objects are to be detected, noise is detected as well. For that reason we chose a minimal significant area of 6 pixels. All the objects of less than 6 pixels are eliminated. This is a compromise with the noise detection. With this criterion, the smallest reliably detected porosity is 0.46 mm in size. Of course the detection limit depends on the initial image quality.
5.1.Image acquisition and defect detection
Films were obtained as indicated by an internal specification, and digitized with 100 ?m step. Of course lacks of penetration were detected, as for radioscopy. The compromise between the limit of detection for porosities and the noise detection is obviously the same than for radioscopy. However, the 6 pixels criterion represents here a smaller object, due to the pixel size. Hence, the smallest reliably detected porosities are 0.3 mm.
5.2.Comparison with the expert interpretation of films before digitization
The films were interpreted by a skilled expert before digitization. The expert was asked to detect porosities of more than 0.2 mm in diameter, to count and calibrate them (with a lens to magnify the defects). The comparison was then done in terms of the total number of detected porosities, the ratio of porosities area over the weld area ( parameter, as described in § 3), and maximal diameter (ø parameter).
It is of prime importance to notice that the classification of the welds according to the standard was exactly the same for the software or the expert, either taking the or the ø parameter.
The following figure shows the influence of the area criterion used to select only the significant defects. It appears clearly that if we try to detect very small objects, noise is detected also, that is why the number of detected defects is too high compared to the number given by the expert. One weld is particular (n° 233), because very few defects are detected, and for any chosen criterion. In fact, the expert report indicates a lot of very small defects in this weld (ø = 0.2 mm), that are under the detection limit of the software.
However, it seems obvious that the number of detected defects is more reliable when a minimal area of 7 pixels is chosen (ø ( 0.3 mm).
This is a first step, as it is now necessary to check that the detected objects are actual defects, in order to evaluate the false detection rate.
Figure 5 : Comparison of the number of objects detected by the software and the expert.
The next figure illustrates the comparison of the parameter, as computed by the software or the expert. As expected, when too many porosities are detected by the software, the ratio obtained is also to important, as the area of porosities is bigger. Some cases are however interesting to discuss on. On the weld n° 233 for instance, the parameter of the software is lower, but the weld is classified correctly because only very small defects are missed, and in the end the porosities area is not so modified (1.2 mm2 on 100 mm of weld). On the weld n° 231, another aspect is interesting : the software detects less defects than the expert, although the parameter looks too high.
On the image of this weld (figure 7a) it is visible that some defects are very close together, so that the software detects them as one (figure 7b). That is why on this weld the number of detected defects is smaller than that of the expert, while the and ø parameters are greater (figures 6 and 8).
Figure 6 : Comparison of the parameter.
|Figure 7 : a) Digitized film of weld n° 231 where some porosities are very close together b) Binary image delivered by the software, where close defects are detected as global ones, thus reducing the number of detected defects and increasing the maximal diameter.|
The comparison of the maximal diameter shows that the image processing developed for big porosities is quite efficient. Indeed, the size of big porosities is estimated correctly, as the mean error is 120 µm, which is close to the pixel size. Moreover, the diameter is either over or under estimated, which indicates that the software does not systematically grow the defects. Thus, we can assume that the criteria integrated in the region growing procedure (§ 2.4.a) are reasonable.
Figure 8 : Comparison of the ø parameter.