Patent Grant
10914690
The invention falls within the field of designing, characterizing, and tracking parts in industry, in particular parts that are to be subjected to high levels of mechanical stress, such as aeroengine parts for example.
The technique of X-ray computed tomography (CT) is known. This experimental technique makes use of different absorption of X-rays by different materials in order to reconstruct a three-dimensional image of the part under study by computation on the basis of a series of X-ray images. The information contained in tomography images is of great use since it relates to the entire volume of the part and gives access not only to its microstructure, but also essentially to its defects.
Nevertheless, this richness also gives rise to two major difficulties. Firstly, acquiring information with fine spatial resolution requires a large number of X-ray images, and thus a long time to acquire the images, and it requires considerable data storage means to be available. Furthermore, searching for defects is itself an operation that is difficult and demanding, and does not lend itself to automation.
Techniques are also known for comparing images that are similar to each other, known under the technical terms of image correlation or image alignment. Those techniques consist in putting two images into the best possible coincidence and doing so by determining a transformation belonging to a selected class. The residual difference (or “residues”) between the images after applying the transformation reveals changes (or defects depending on context) that have not been eliminated by the transformation.
Between two images, modifications often take place in the form of movements of rigid bodies, and also in the form of enlargement or deformation. Such modifications may constitute the selected transformation class. It is then possible to calculate the residues for the optimum transformation within this particular class.
This concept may be formalized as follows. The gray level values for any point x in the two images are written f1(x) and f2(x). A set T of transformations (the class of transformations) is also introduced, such that for all transformation T∈T, the image f becomes the transformed image T[f] (where T is a functional acting on the entire image f). It is useful to identify the transformation T* (optimum transformation) that achieves the minimum:
T*=argminT∈T∥f1(x)−T[f2(x)]∥
The residue is a new image g defined as follows:
g(x)=f1(x)−T*[f2(x)]
Furthermore, a generic class of transformations is that induced by the set of continuous displacement fields, u(x), which is such that:
Tu[f(x)]=f(x+u(x))
The topological difference Δ(f1, f2) of two images is the residue field g obtained when the class over which optimization is performed is the class of continuous displacement fields.
Nevertheless, by way of example, it is also possible to alter the brightness and the contrast of an image by an affine transformation such as:
Tg[f(x)]=αf(x)+β
It is also possible to combine a plurality of transformations of this type, to constrain the parameters or the fields involved so as to satisfy specific constraints, or to give them more freedom. For example, the two parameters of the affine transformations described above (α and β) may be calculated as a function of the coordinates of the voxel in question.
Although some commercial programs exist for image correlation, it is unusual for them to give access to the raw and complete transformation T* that minimizes the difference between the images, since the displacement fields are often not constructed in global manner, but rather by interpolation between discrete local displacements. Furthermore, the brightness and contrast corrections are often not accessible. In practice, the transformation T* is thus often not used, even though the topological difference is used, e.g. for the purpose of recognizing a face, an article, or a specific shape, e.g. in medical imaging.
In certain uses, such as automatic recognition, the only part of the image alignment that is used is the association with a reference when the norm of the residue is considered to be small. Finally, even if the user of the computer program can be invited to make use of the residue or the topological difference on the screen, they are rarely produced as results that can be exported from image correlation software.
To solve the above-mentioned difficulties, there is proposed a method of characterizing a part, the method comprising a step of obtaining an X-ray tomography image of the part and then a step of correlating said image with a reference, the method being characterized in that the correlation step comprises searching among a predefined set—or class—of X-ray tomography image transformations for a transformation that minimizes the difference between said image and the reference in order to characterize the inside of the part.
Particularly, but not necessarily, the set of transformations may include continuous displacement fields, it being possible for each field to be discretized.
Preferably, the method uses a continuous parameterization of said set of transformations.
The advantage of this method can be seen from the following discussion. Specifically, the parameterization of the pertinent space T is of much smaller dimension than the image itself.
Specifically, at least in theory, it is possible to superpose two tomography images of the same object exactly by moving a rigid body with six degrees of freedom, by applying a scale factor with one degree of freedom, and by linearly adjusting gray levels with two degrees of freedom. Thus, in order to correlate these two images, it is necessary to determine nine unknowns. This number nine is the dimensionality of the space T and should be compared with the complexity of determining an entire image of the order of 1 Giga voxel, i.e. 109 unknowns. Thus, knowing the transformation T* makes it possible to achieve considerable savings in terms of data. This also makes it possible to obtain the topological difference Δ(f1, f2) directly and to find therein defects that are incompatible with the transformation class T. Transformations that are more complex than those mentioned above may also be used.
This makes it possible to provide a rapid determination of the 3D image of a part by virtue of the above-described characteristic in which use is made of a set of transformations to be applied from one or more reference articles while making use of a smaller number of X-ray images than in the usual methods. This increase in speed for image acquisition implies that the tomography equipment is busy for a shorter length of time. This is done at the cost of less redundancy in the information since fewer X-ray images are taken, and also of an increase in the amount of computation needed, but in numerous situations these two aspects are no handicap.
By way of example, the set of transformations comprises at least one set of continuous displacements (with or without change of scale), at least one set of alterations of brightness and of contrast, or at least one set of scale changes (with or without continuous displacement).
In an implementation, the reference comprises the image of a standard part. In particular, it is possible, in this way or otherwise, to determine whether the particular part is acceptable, e.g. by using a transformation that is identified at the end of the search. It is specified that under such circumstances, the acceptable nature of the part may be formalized by expressing conditions on the identified transformation T*.
In a variant, the reference comprises an image, referred to as “virtual”, of the part constructed from a computer assisted design model.
The set of transformations may comprise at least the transformations corresponding to modifications of at least one parameter of a model of the part.
Optionally in combination with the above-described aspects, the method may also include modifying the parameterization of a computer assisted design model of the part by using a transformation identified at the end of the search.
In a variant, the reference comprises an explicit representation of the boundaries of the part and of its components or elements, if any. Said set of transformations may then comprise at least a class of transformations that conserve topology. If the part has a plurality of components, the identified optimum transformation T* leads to the image being segmented on the basis of a priori knowledge about the article. The segmentation may also be performed differently, but still with the help of the identified transformation.
In a variant, the reference comprises the representation of an elementary pattern, e.g. a phase modulated periodic pattern.
The method may be performed using as the reference an X-ray tomography image of the part, the part having been subjected to a mechanical load between taking the two images. The topological difference then makes it possible to identify defects (in the above-defined sense of not being resorbable by the selected transformation class) as induced by the load. It is then possible, in this way or otherwise, to determine whether the nature of the defect and in particular its size, its shape, or its location is acceptable in the light of functional specifications, rules of art, or indeed standards constraints.
The method may be performed in particular with a part made of composite material, or an aircraft turbojet blade, which may specifically be made of fiber reinforced composite material, e.g. woven materials.
The description of the invention is continued below with reference to the accompanying figures.
In an implementation as shown in
In a variant, the differences between the part under study and the standard part are found and identified effectively by using the topological difference Δ(reference 50) during a step 300. For example, as shown in
It is also possible to establish a correlation between two different samples, and if they are parts made of composite material, this can reveal differences from a point of view weaving between the two samples. This is shown in
In general manner, it is thus possible to perform non-destructive tests (NDT) on composite material parts, e.g. turbojet blades. The technique described leads to savings in time for inspecting, and acquiring and storing data. Thus, by way of example, during a step 350, it is possible merely on the basis of the transformation T*, to decide whether the part should be retained or rejected.
In another implementation, shown in
Under such circumstances, the parameters 30 of the CAD model may themselves comprise a specific transformation class T. Thus, tomographic data can be used on the basis of the image of the CAD model by writing the tomographic image directly in a description language suitable for dialog with the CAD design team of the part.
The dialog then consists in providing, in the form of the CAD model, a good predetermination of the solution for assisting in constructing the tomographic image (step 200) on the basis of the tomographic data. In return, the image as constructed in this way then makes it possible, in a step 400, to correct the CAD model by means of the parameters of the identified transformation T* so that it is as close as possible to the part actually made.
The method is performed until the algorithm used converges or becomes stationary, e.g. in a simple context of adjusted gray levels.
Defects of orientation or of alignment can affect the response of the complete composite structure, and an initial adjustment as proposed is a good way of improving and validating a CAD model taking account of such imperfections.
3D models can be generated that are made discrete in the form of individual voxels or that are represented by a parametric model, or a computer assisted design (CAD) model, based on a priori knowledge about the woven array of composite fiber reinforcing materials. It is thus possible to correlate the image of a part and an image derived from a mold and to modify the input parameters of the model, i.e. the directions of the strands and also their dimensions. By way of example,
In a variant, if the paths followed by the yarns are not included in the reference image obtained by a CAD model, the paths of the strands are determined directly from the tomographic image, e.g. by using a tracking algorithm, which is provided with the results of the correlation with the reference image obtained by a CAD model.
In a variant shown in
Using the identified transformation T*, the defined component in the reference image can thus be situated in the image and can be deformed in order to match the real image.
Thus, if it is desired to find a closed curve, it suffices to start with an ideal image of a closed line such as a perfect circle and then allow it to vary progressively towards the line as is present in the image of the medium.
This approach is more robust than the usual thresholding and segmenting techniques that do not automatically preserve the correct topology for the looked-for article. Thus, with these techniques, missing points in a curve that ought to be closed are obtained, or a thick curve is obtained when it ought to be fine.
The segmentation as performed in the described method, i.e. automatically on the basis of a previously defined topological element, serves to minimize intermediate steps of image filtering where the contributions of noise, bias, and measurements are not always easily determined, and thus where information is easily degraded by such filtering.
The invention is not limited to the implementations described, but extends to any variant within the ambit of the scope of the claims.
| Number | Date | Country | Kind |
|---|---|---|---|
| 13 63095 | Dec 2013 | FR | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/FR2014/053229 | 12/9/2014 | WO | 00 |
| Publishing Document | Publishing Date | Country | Kind |
|---|---|---|---|
| WO2015/092212 | 6/25/2015 | WO | A |
| Number | Name | Date | Kind |
|---|---|---|---|
| 6041132 | Isaacs | Mar 2000 | A |
| 20040165760 | Veneruso | Aug 2004 | A1 |
| 20090238432 | Can | Sep 2009 | A1 |
| 20100194749 | Nightingale | Aug 2010 | A1 |
| 20100220910 | Kaucic | Sep 2010 | A1 |
| 20160275688 | Chiang | Sep 2016 | A1 |
| Entry |
|---|
| Leclerc, Hugo, et al. “Digital Volume Correlation: What are the limits to the spatial resolution? Corrélation d'images volumiques: quelles limites à la résolution spatiale?.” Mechanics & Industry 13.6 (Published online Nov. 22, 2012). |
| Pan, Bing, Dafang Wu, and Zhaoyang Wang. “Internal displacement and strain measurement using digital volume correlation: a least-squares framework.” Measurement Science and Technology 23.4 (2012): 045002. |
| Alghamdi A, Mummery P, Sheikh MA. Multi-scale 3D image-based modelling of a carbon/carbon composite. Modelling and Simulation in Materials Science and Engineering. Nov. 8, 2013;21(8):085014. |
| Bay, B. K. “Methods and applications of digital volume correlation.” The Journal of Strain Analysis for Engineering Design 43.8 (2008): 745-760. (Year: 2008). |
| Roux, Stéphane, et al. “Three-dimensional image correlation from X-ray computed tomography of solid foam.” Composites Part A: Applied science and manufacturing 39.8 (2008): 1253-1265. (Year: 2008). |
| International Search Report dated Mar. 18, 2015, in PCT/FR2014/053229 Filed Dec. 9, 2014. |
| Leclerc, et al., “Digital Volume Correlation: What are the limits to the spatial resolution?” http://hal.inria.fr/docs/00184187/21/PDF/MI2012-ccsd.pdf, 2013, XP055129980 (42 pages). |
| Pan, et al., “Internal displacement and strain measurement using digital volume correlation: a least-squares framework,” Measurement Science and Technology, vol. 23, No. 4, 2012, XP020221141 (13 pages). |
| Alghamdi, et al., “Multi-scale 3D image-based modelling of a carbon/carbon composite,” Modelling and Simulation in Materials Science and Engineering, vol. 21, No. 8, 2013, XP020254298 (13 pages). |
| European Office Action dated Feb. 3, 2020 in European Patent Application No. 14 827 807.0, 5 pages. |
| Limodin, N., et al., “Analysis and Artifact Correction for Volume Correlation Measurements Using Tomographic Images from Laboratory X-Ray Source”, Experimental Mechanics, vol. 51, 2011, pp. 959-970. |
| Number | Date | Country | |
|---|---|---|---|
| 20180195978 A1 | Jul 2018 | US |
