METHOD FOR THE NON-DESTRUCTIVE TESTING OF THE VOLUME OF A TEST OBJECT AND TESTING DEVICE CONFIGURED FOR CARRYING OUT SUCH A METHOD

Abstract

A method for the non-destructive testing of the volume of a test object, during the course of which a volume raw image of the test object is recorded by a suitable non-destructive imaging testing method. Then, those regions of the volume raw image are identified that are not to be attributed to the test object material. It is checked whether an identified region is completely embedded in regions that are to be associated with the test object material. If necessary, such a region is assimilated to those regions that are to be associated with the test object material, forming a filled volume raw image. Finally, a difference is generated between the volume raw image and the filled volume raw image, forming a first flaw image.

Claims

1. A method for the non-destructive testing of the volume of a test object, the method comprising: recording a volume raw image of the test object by a non-destructive imaging testing method; identifying the regions of the volume raw image that are not to be attributed to the test object material; checking an identified region as to whether it is completely embedded in regions that are to be associated with the test object material, and, if necessary, assimilating the identified region to the regions that are to be associated with the test object material, forming a filled volume raw image; and generating the difference between the volume raw image and the filled volume raw image, forming a first flaw image.

2. The method according to claim 1, further comprising: applying a filter algorithm to the volume raw image for amplifying possible flaw indicators, forming a filtered volume raw image; and limit value generation of the filtered volume raw image, forming a second flaw image.

3. The method according to claim 2, wherein the first flaw image and the second flaw image are merged into a combined flaw image.

4. The method according to claim 3, wherein, in the combined flaw image, the regions that are not associated with the test object material are suppressed in the filled volume raw image.

5. The method according to claim 1, wherein a subtraction of the volume raw image is carried out for forming the filtered volume raw image.

6. The method according to claim 1, wherein the non-destructive imaging testing method is a tomography method based on X-radiation, ultrasound, or eddy currents.

7. The method according to claim 1, wherein the test object is a series-produced workpiece.

8. The method according to claim 3, further comprising: forming a volume reference image from volume raw images of one or more test object(s) which was/were classified to be “In order” on the basis of predefined test criteria; and generating the difference between, on the one hand, the first flaw image, the second flaw image, or the combined flaw image and, on the other hand, the volume reference image.

9. The method according to claim 8, further comprising registering a volume raw image to a volume reference image or to a 3D model of the test object takes place.

10. The method according to claim 9, further comprising registering the volume reference image to the 3D model of the test object.

11. A testing device for the non-destructive testing of the volume of a test object, the testing device comprising: an image forming unit configured to record a volume raw image of the test object by a non-destructive imaging testing method; and an image processing unit configured to: identify regions of the volume raw image that are not to be attributed to the test object material, check an identified region as to whether it is completely embedded in regions that are to be associated with the test object material, and, if necessary, assimilate the identified region to the regions that are to be associated with the test object material, forming a filled volume raw image, and form a first flaw image by generating the difference between the volume raw image and the filled volume raw image.

12. The testing device according to claim 11, wherein the image processing unit is further configured to: apply a filter algorithm to the volume raw image for amplifying possible flaw indicators, forming a filtered volume raw image, and limit value generation of the filtered volume raw image, forming a second flaw image.

13. The testing device according to claim 12, wherein the image processing unit is further configured to merge the first flaw image and the second flaw image into a combined flaw image.

14. The testing device according to claim 13, wherein the image processing unit is further configured to suppress in the combined flaw image the regions that are not to be associated with the test object material in the filled volume raw image.

15. The testing device according to claim 11, wherein the image processing unit is further configured to carry out a subtraction of the volume raw image in order to form the filtered volume raw image.

16. The testing device according to claim 11, wherein the image forming unit is further configured to form volume raw images of a test object by a tomography method based on X-radiation, ultrasound, or eddy currents.

17. The testing device according to claim 11, further comprising a classification unit configured to classify a test object as being “In order”/“Not in order” on the basis of predefined test criteria.

18. The testing device according to claim 13, wherein the image processing unit is further configured to: form a volume reference image from volume raw images of one or more test object(s) which was/were classified to be “In order” on the basis of predefined test criteria, and generate the difference between, on the one hand, the first flaw image, the second flaw image, or the combined flaw image and, on the other hand, the volume reference image.

19. The testing device according to claim 18, further comprising a registration unit configured to at least one of: register a volume raw image to a volume reference image or to a 3D model of the test object, and register the volume reference image to a 3D model of the test object.

20. The method according to claim 2, wherein the filter algorithm is a cutoff filter or a median filter.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Other advantages and features of the various embodiments of the testing device according to the invention are also apparent from the following exemplary embodiments. It is pointed out that the method features discussed there can be directly transferred to the device according to the invention to the extent it is characterized, within the context of the present invention, as carrying out certain process steps.

[0050] The exemplary embodiments will be explained with reference to the drawing, in which the Figures show the following:

[0051] FIG. 1 is a first exemplary embodiment of a method according to the invention,

[0052] FIG. 2 is an exemplary section through a test object's volume raw image to be processed,

[0053] FIG. 3 is a section through a cleaned-up combined flaw image of the test object obtained according to an embodiment of the invention, with the section shown corresponding to the section from FIG. 2,

[0054] FIG. 4 is a cleaned-up combined three-dimensional flaw image of the test object obtained according to an embodiment of the invention, according to FIG. 2, and

[0055] FIG. 5 is a schematic view of a method according to an embodiment of the invention.

DETAILED DESCRIPTION

[0056] FIG. 1 schematically shows the process flow of an embodiment of a method according to the invention. In this embodiment, the method is applied to a three-dimensional volume raw image obtained by means of X-ray computer tomography on a pump housing formed as a casting. The starting point of the method is in Block 100 a three-dimensional volume raw image of the pump housing. This is subjected in Block 101 to a method step which is referred to in the context of the present invention as segmentation. During segmentation, those regions of the volume raw image are identified that are not to be attributed to the test object material. The basis for such an association can be, for example, an analysis of the gray values in the volume raw image including a threshold analysis.

[0057] However, the problem with an association made in this manner is that larger faults, piping or included gas bubbles in the test object, for example, locally cause a gray value in the volume raw image that matches the gray value of the air surrounding the test object. In the case of these flaw regions, there is therefore the danger that they are not attributed to the test object volume. In the segmentation step, the volume raw image is therefore analyzed in order to identify those regions in the volume raw image that are not to be attributed to the test object material. In a subsequent processing step, the regions of the volume raw image identified herein are checked as to whether they are completely embedded in regions that are to be associated with the test object material. In a process step subsequent thereto, the identified regions that are completely embedded in the test object material are assimilated to those regions that are to be associated with the test object material, for example, by associating a medium gray value of the test object volume with them. By assimilating the identified regions to the surrounding regions to be associated with the test object material, a filled volume raw image is formed in step 102.

[0058] In the subsequent method step in Block 103, a difference is generated between the volume raw image according to Block 100 and the filled volume raw image according to Block 102, and results in a first flaw image from which first flaw indications are apparent.

[0059] At the same time, the volume raw image from Block 100 is subjected to further processing steps in a second branch of the method. In Block 201, the volume raw image is filtered, for example by means of a cutoff filter or median filter. In the subsequent processing step 202, a difference is generated between the volume raw image from Block 100 and the filtered volume raw image from Block 201. Subsequent thereto, in process step 203, a limit value is generated of the filtered difference image from block 202, whereby flaw indications that do not exceed a predefined level are blanked out. Second flaw indications, which are correlated to smaller flaws in the test object material, remain. The sensitivity of the method according to an embodiment of the invention can be influenced to a substantial extent by the selection of the threshold used here. In Block 204, a second flaw image is provided as a result of this second processing branch.

[0060] Then, the first flaw image (Block 104) from the first processing branch and the second flaw image (Block 203) from the second processing branch are merged into a combined flaw image, which takes place in step 301.

[0061] In the subsequent masking step according to Block 302, those regions of the flaw image are masked in the combined flaw image according to Block 301 that are not to be attributed to the test object volume. Here, the filled volume raw image from Block 102 can be used. It is possible to extract from the filled volume raw image, e.g. by means of threshold analysis, which image regions are to be attributed to the test object volume. By means of this masking step, all flaw indications are eliminated that are outside the test object, which therefore must be artifacts. The result of this masking step is a cleaned-up combined flaw image in Block 303, which can then be subjected to subsequent processing steps, for example for a partially or fully automated flaw detection and classification.

[0062] FIG. 2 shows a sectional view through the test object, which was obtained by means of a volume raw image according to Block 100 serving as the starting point of the method according to an embodiment of the invention.

[0063] FIG. 3 now shows an equivalent sectional view of the same test object that was obtained on the cleaned-up combined flaw image according to Block 303, which is the result of the method according to an embodiment of the invention. It is very clear that the number of the flaw indications apparent from the sectional view is considerably increased in FIG. 3 compared to FIG. 2. At the same time, all regions of the image situated within the outline of the test object are associated with the test object material, so that all flaw indications in FIG. 3 can be subjected to further automated processing. Therefore, the method according to an embodiment of the invention permits reliably avoiding such flaws that are caused by flaw indications above a certain size no longer being attributed to the test object material.

[0064] FIG. 4 now shows a perspective view of a combined flaw image of the pump housing, of which FIGS. 2 and 3 show sectional views. The illustration according to FIG. 4 and the underlying data were obtained by a superposition of the cleaned-up combined flaw image according to Block 303, which was obtained according to an embodiment of the invention, with the filled volume raw image according to Block 102, which was also obtained during the course of the method according to an embodiment of the invention. On the one hand, the structure of the examined test object is clearly apparent from this combined illustration, on the other hand, the flaw indications are easily recognizable to an operator of a testing system configured according to an embodiment of the invention. Furthermore, in the data set which is the basis of the illustration according to FIG. 4, the flaw indications can very easily be subjected to an automated inspection and classification.

[0065] FIG. 5 shows an embodiment of the method according to the invention, which comprises the method according to FIG. 1. This embodiment permits the effective suppression of artifacts in a volume raw image of a test object that were caused by the non-destructive image forming testing method used. A requirement in order for the developed method to be capable of being carried out is that reference volume raw images are provided of a plurality of test objects, which were inspected, for example, by means of other non-destructive testing methods and which where classified as being “In order” within the context of the testing task to be carried out. This plurality of reference volume raw images is registered in step 402 to a reference model, for example a three-dimensional CAD model of a test object. Within the context of the present invention, registration means that the volume raw images obtained from actual test objects are aligned to a reference model of the test object. This registration is essential for the developed method to succeed, because here, volume raw images are to be compared with each other that were obtained on different test objects, which did not inevitably had to have the same orientation in space during the recording of the reference volume images, due to the testing method used. The result of the registration step 402 is a plurality of registered, i.e. aligned to a reference model, reference volume raw images of different test objects that were classified as being “In order”.

[0066] In the subsequent processing step 403, the registered reference volume raw images, if necessary, are subjected to a suitable filtering, such as, again, a cutoff filter or median filter, in order to have structures that possibly exist come out more clearly.

[0067] In the subsequent processing step 404, an averaging process is carried out over the plurality of the registered, optionally filtered, reference volume raw images in order to form an adaptive reference background image.

[0068] In step 601, a volume raw image of a test object to be classified is recorded by means of a non-destructive imaging testing method, which matches the image-forming testing method used for recording the reference images in step 401. In the subsequent step 602, the recorded volume raw image of the test object to be classified is aligned in a registration step in Block 602 to the reference model according to Block 500. The aligned volume raw image formed here of the test object to be classified is then subjected in Block 603 to the method according to FIG. 1. However, a difference is still generated here between the result of the method according to FIG. 1, i.e. between the cleaned-up combined flaw image according to Block 303, and the registered flaw reference image formed in step 404. In addition, a threshold analysis can be carried out on the formed difference image in order to suppress smaller deviations from the reference image. Also in this case, the result of the method according to an embodiment of the invention can be influenced decisively by the suitable selection of the threshold. The background-corrected, cleaned-up, combined flaw image resulting in Block 603 can then be subjected in Block 604 to a flaw classification, which may proceed, in particular, in a partially or fully automated manner. Using the result of this flaw classification in Block 604, a deciding unit can classify the analyzed test object as being “In order”/“Not in order” in Block 605. On the one hand, this classification can take place fully automatically, on the other hand, an intervention by an operator is also conceivable in this case.

[0069] Another embodiment of the method according to the invention provides that the volume raw image that was obtained on a test object analyzed by means of the method discussed above and classified as being “In order” in Block 605 is added to the set of the reference volume raw images according to step 401. A self-learning system is thus developed in which slowly changing influences, such as minor geometry changes of the analyzed castings within the series due to ageing phenomena of the molds used, or changed bubble inclusions, can be automatically compensated by slightly changed process parameters in casting processes.

[0070] Whilst exemplary and embodiments of the invention have been described herein, the skilled person will appreciate that other embodiments are possible and contemplated. The invention is intended to encompass all such embodiments that fall within the scope of the appended claims.