Tomographic analysis method

Abstract

Tomographic analysis method for a part, the method comprising the following steps: - acquisition of at least one tomographic image using a tomography device, - division of the tomographic image into elementary sub-parts and obtaining a plurality of two-dimensional images (32) for each sub-part, - description of the images (32) by applying at least one digital processing tool to each image of the sub-part and obtaining a value for at least one representative parameter, - determination of spatial variations in each sub-part, along three directions (X, Y, Z), of the values ​​of each parameter and detection of values ​​outside compliance ranges, - calculation of a compliance score for each sub-part, and - comparison of the compliance score of each sub-part to a predetermined compliance threshold to determine the compliance of the sub-part. Figure to be published with the abbreviation: 5

Description

Title of the invention: Tomographic analysis method Technical field of the invention

[0001] The invention relates to a method for tomographic analysis of a mechanical part. The part is, for example, part of an aircraft, and more specifically of a turbomachine, such as part of a fan casing, a fan blade, or part of a fixed blade structure. Prior art

[0002] It is known to inspect structural mechanical parts by tomographic methods, for example radiographic or acoustic, in order to detect the presence of possible defects on the surface and inside the part.

[0003] These methods are reliable, non-invasive and allow inspection of the inside of the parts, which makes it possible to quickly determine the condition of the part and decide whether to potentially put it back into operation or replace it.

[0004] A tomographic measurement consists of scanning an observed object, here a mechanical part, by means of a beam of waves, and measuring the transmitted beam in all directions in order to reconstruct a three-dimensional image of the object.

[0005] Fig. 1 illustrates a tomography device 1 during the inspection of a mechanical part 20.

[0006] The tomography device 1 includes at least one emitting device 3, configured to emit an incident beam 5 of wave pulses towards the part 20, for example radio frequency waves, X-rays or acoustic waves, and at least one receiver 7 capable of capturing a transmitted beam 9 of waves, arranged on either side of the part 20.

[0007] The part 20 is generally mounted on a support 11 that rotates about an axis A, so that it can be observed from all directions during the tomographic measurement. A reverse mounting arrangement is possible, where the part is fixed and the transmitting / receiving device pair rotates about it.

[0008] The part 20 observed by tomography may be made of a composite material, as shown in detail in [Fig.2].

[0009] Such a composite material may have a so-called two-dimensional weave, which includes weft fibers generally oriented along a weft direction X and warp fibers generally oriented along a warp direction Y, woven together on a weaving plane P, or several weaving planes P superimposed along a thickness direction Z.

[0010] The fibers can be carbon fibers, glass fibers, or a mixture of both. They are embedded in a matrix 23 comprising for example one or more polymers and / or resins, which solidify to form the final part 20.

[0011] Alternatively, as in the example in [Fig.2], the weaving of the fibers 21 can be three-dimensional, with fibers 21 extending along the three directions of space X, Y, Z.

[0012] The amplitudes of the waves transmitted under the different angles of observation are translated into grey levels in a processing device 13, and a digital analysis makes it possible to reconstruct a three-dimensional grey level image of the volume of the room 20. The processing device 13 includes a computer.

[0013] Tomographic analysis then makes it possible to obtain a visual view of the exterior and interior of the part. This type of inspection has the major advantage of allowing visualization through the thickness of the material and the part being studied, while being non-destructive and reliable.

[0014] A human operator can then assess the quality of the part by examining the three-dimensional image for the presence or absence of any anomaly or damage, and by evaluating the material's conformity to applicable standards. For example, in the case of a woven composite part, the anisotropy of the weave, the continuity of the fibers, and that of the matrix are relevant parameters visually assessed by the operator.

[0015] However, the analysis of a part such as a fan blade by a qualified inspector is time-consuming, this "manual" and tedious operation, much of which is spent scrolling through the tomography images. The operation also generates significant eye strain, which can reduce the inspector's vigilance and the quality of their analysis after a certain period.

[0016] Moreover, from a technical point of view, the implementation of such an analysis by human beings can generate biases in assessment and ultimately potentially a large variability in results. Presentation of the invention

[0017] The invention aims to overcome these drawbacks by providing an analytical method that allows for a rapid and systematic assessment of the condition of a mechanical part without requiring the intervention of a human inspector. This makes inspection faster and reduces the risk of error.

[0018] To this end, the invention relates to a method for tomographic analysis of a part, the method comprising the following steps:

[0019] - acquisition of at least one tomographic image of the part by means of a tomography device,

[0020] - division of the tomographic image into elementary sub-parts and obtaining a plurality of two-dimensional images for each sub-part, each image comprising a plurality of pixels, each pixel exhibiting a respective gray level,

[0021] - application of at least one digital processing tool to each image of the sub-section and obtaining, for each tool applied, a value of at least one representative parameter for each image,

[0022] - determination of spatial variations in each sub-part, along three directions characteristics, values ​​of each parameter, and detection of parameter values ​​outside predetermined compliance ranges,

[0023] - calculation of a conformity score for each sub-part, based on the values ​​of the pa meters outside the compliance ranges, and

[0024] - comparison of the conformity score of each sub-part to a conformity threshold predetermined to determine sub-part conformity.

[0025] Such a process allows for an analysis of the entirety of a mechanical part, the detection of non-conformities, and the formulation of a global diagnosis of the condition of the part without requiring the presence of a human inspector.

[0026] The method may include a two-dimensional image processing step, implemented before the step of applying at least one digital processing tool, during which each pixel whose grey level is outside a predetermined median range is replaced by a corrected pixel having a grey level within said median range.

[0027] Such a step makes it possible to eliminate pixels whose grey levels have extreme values, and which could generate strong gradients and distort subsequent calculations, by being considered as singularities by the digital tools.

[0028] The corrected grey level can be determined randomly from a distribution of grey levels of the image pixels whose grey level is in the median range.

[0029] Such a feature makes it possible to blend the corrected pixels into the rest of the image, with a distribution of the "white noise" type.

[0030] At least one digital processing tool may include an autocorrelation calculation, including the generation of an autocorrelation image from the image, the detection of a central peak of the autocorrelation image and the determination of an isotropy ratio of the central peak, the isotropy ratio being chosen as the representative parameter value.

[0031] Such a tool makes it possible to qualify the anisotropy of an image, and for example to detect the preferential weaving directions of a composite part.

[0032] At least one digital processing tool may include a filtering calculation, comprising applying at least one Gabor filter to the image and obtaining a filtered image, determining, for each pixel of the image, a sum of the corresponding values ​​of the images filtered with each of the Gabor filters, the maximum on the image of the sums being chosen as the representative parameter value.

[0033] Such a tool makes it possible to detect local interlacing anomalies.

[0034] Several Gabor filters can be applied, with respective bases obtained from the characteristic directions of the image by rotation.

[0035] In one embodiment, four Gabor filters can be applied, with respective bases obtained from the characteristic directions of the image by rotation, for example, by an angle of 0°, 45°, 90° and 135° respectively for a woven structure at 0° and 90°. Such a characteristic makes it possible to process all image orientations.

[0036] At least one digital processing tool may include a calculation of the standard deviation of the gray levels across all pixels of the image, said standard deviation being chosen as the representative parameter value. Such a tool makes it possible to detect a local overrepresentation of a phase of the material, for example, a resin clump in the case of a woven composite part.

[0037] Three different digital processing tools can be applied to each image, the step of calculating the conformity score including the calculation of a partial score for each digital processing tool and for each direction, from the quantity of parameter values ​​out of conformity ranges obtained for said digital processing tool and said direction, the conformity score being the sum of the partial scores.

[0038] Such a characteristic makes it possible to observe several different factors in order to establish a more precise diagnosis of the condition of the part.

[0039] The compliance ranges for the parameters are predetermined from healthy test specimens, each compliance range comprising the lower 99 percentiles of the values ​​of the associated parameter measured on said healthy test specimens.

[0040] Such a feature makes it possible to reliably detect non-conforming values ​​in order to improve the accuracy of the analysis.

[0041] The invention also relates to a computer program product comprising instructions enabling, when executed on a computer controlling a tomographic analysis device, the implementation of the process as above. Brief description of the figures

[0042] [Fig-1] [Fig.1] is a schematic side view of a tomography device during the implementation of a process according to the invention,

[0043] [Fig.2] [Fig.2] is a schematic detail view of a part made of woven composite material,

[0044] [Fig.3] is a schematic view of a step in dividing a tomographic image into elementary sub-parts, and

[0045] [Fig.4] is a schematic view of the application of extreme grey level correction by white noise replacement

[0046] [Fig. 5] is a schematic view of the use of the autocorrelation function to highlight an indication of woven composite. Detailed description of the invention

[0047] A tomographic analysis method according to the invention will now be described. This method implements the analysis device 1 described previously, and aims to detect the presence of anomalies in a part 20, made for example of composite material.

[0048] The part here is a portion of a blade, for example, a fan blade. Said fan blade has a principal elongation direction chosen as the Z direction of space, the X direction being chosen to be substantially tangent to the external surface of the blade and the Y direction being the thickness direction of the blade.

[0049] A thickness of part 20, measured along the Y direction, is for example between 5 and 25 mm, in particular close to 10 mm.

[0050] The method includes a first step of acquiring at least one three-dimensional image of the part 20 using the tomography device 1.

[0051] An incident beam 5 of wave pulses is emitted by the emitting device 3, in the direction of the part 20. The incident beam 5 is, for example, a beam of radio frequency waves.

[0052] The waves pass through the part 20, and a transmitted beam 9 from the part 20 is captured by the receiver 7. The intensity distribution of the transmitted beam 9 obtained is converted into a two-dimensional greyscale image of the part 20 by the processing device 13.

[0053] The part 20 is rotated by means of the support 11, and two-dimensional images of the part 20 are acquired in all directions.

[0054] A three-dimensional image 30 of the part 20 in shades of grey, or tomographic image, is then reconstructed by image processing using the processing device 13.

[0055] By way of non-limitation, part 20 is a part made of three-dimensional woven composite material, as shown in [Fig.2].

[0056] The composite material comprises fibers 21 along the three directions X, Y and Z of space, embedded in a matrix 23. The average gap between two neighboring fibers 21 in the same direction is on the order of 2 mm.

[0057] The fibers 21 can be of the same or different materials (glass, kevlar or carbon for example).

[0058] The matrix 23 comprises at least one organic polymer and / or at least one resin.

[0059] The different types of fibers and the matrix are referred to as phases of the composite material composing part 20.

[0060] The three-dimensional image 30 therefore comprises a plurality of voxels, each presenting a respective level of grey representative in particular of the density of the phase of the corresponding material in the part 20.

[0061] The tomographic analysis process then includes a step of dividing the three-dimensional tomographic image 30 into elementary sub-parts 31, as shown in [Fig.3].

[0062] These sub-parts 31 have dimensions, along three directions of space X, Y, Z, calculated from the characteristics of the material of the part 20. The sub-parts 31 are for example of parallelepiped shape.

[0063] These dimensions of the sub-parts 31 can vary depending on the location of the sub-part in the part 20, as shown in [Fig.2] which is a planar section of a mapping of the sub-parts 31 in an XZ plane of the part 20.

[0064] The dimensions of subparts 31 are chosen for example as follows.

[0065] According to the Y direction, the dimension of each sub-part 31 is chosen to be equal to the thickness of the part 20 according to this Y direction.

[0066] Along the Z direction, the dimension of each sub-part 31 is chosen to be equal to an integer n of weaving column spacings (also referred to as pick spacing in English, PS), i.e. an integer number of pitches between neighboring fibers 21 spaced along the Z direction. The integer n is chosen from experimental and modeling results obtained with the material, and corresponds for example to a total length between 15 mm and 25 mm.

[0067] Along the X direction, the dimension of each subpart 31 is chosen so that the subpart 31 constitutes a representative elementary volume (REV).

[0068] The dimensions of sub-parts 31 along the X and Z directions are for example close to each other (for example between half and double each other).

[0069] The method according to the invention having as its objective to simulate an analysis by an inspector, a plurality of two-dimensional images 32 is then obtained for each sub-part 31, simulating sections of the volume of the sub-part 31.

[0070] The images 32 are planar sections in grey level, and each image thus comprises a plurality of pixels, each pixel presenting a respective grey level.

[0071] These images can be sections of the tomographic image in planes perpendicular to the X, Y and Z directions of space, or in oblique planes (i.e. not orthogonal to the X, Y and Z directions).

[0072] The number of images 32 obtained for each sub-part 31 is predetermined from empirically, according to the number of cuts relevant for the analysis of the conformity of subpart 31.

[0073] All of these images 32 constitute the raw data to be processed, representative of the material and the part 20 to be analyzed, for example to highlight the anomalies in the interlacing of the fibers 21.

[0074] The process advantageously includes an image processing step 32, shown in [Fig.4], to remove spurious grey levels that could distort subsequent digital processing.

[0075] Pixels in image 32 whose grey level is outside a predetermined median range are replaced by pixels whose grey level is within this median range.

[0076] The median range extends, for example, between 10% and 90% of the full grayscale of the acquisition device. Pixels with a grayscale level between 0 and 10% and between 90% and 100% of the maximum of the scale (0 generally corresponding to black and 100% generally corresponding to white) are therefore considered parasitic and replaced.

[0077] These values ​​of 10% and 90% are given as an indication and may vary depending on the case, depending on the nature of the material and the sensitivity of the sensors of the acquisition device 1, for example.

[0078] In the example shown [Fig.4], the black pixels of the unprocessed image 32, on the left, which correspond to a void, are considered to have extreme levels of grey.

[0079] The replacement grey level value is advantageously determined based on the grey level distribution of the image 32, for example chosen to be equal to the median grey levels of the pixels in the image.

[0080] More advantageously, the gray level of each replaced pixel in image 32 is determined randomly based on the gray level distribution of the pixels in image 32 located in the median range. Thus, the replaced pixels form white noise in the image and do not alter the subsequent processing steps.

[0081] Such a replacement by a random value of grey level is shown in [Fig.4], where the left image 32 has had its extreme pixels replaced by a "white noise" determined from the distribution of non-extreme grey levels of the initial image 32.

[0082] The process then includes a step of describing the images 32 by applying at least one digital processing tool to each image 32 of subpart 31 and obtaining, for each tool applied, a value of at least one representative parameter for each image 31.

[0083] Advantageously, at least three such digital processing tools are applied on each image 32, independently, and obtaining at least one value of a parameter for each of these tools. For example, the method includes an autocorrelation calculation and the determination of an anisotropy ratio for each image 32, the application of at least one Gabor filter and the determination of a sum of the maxima of the filtered images for each image 32, and a standard deviation calculation of the gray levels and the obtaining of a standard deviation value for each image 32.

[0084] Autocorrelation calculation is a method used in the literature to account for the anisotropy, texture or periodicity of an image.

[0085] Such a calculation is shown in [Fig. 5]. An autocorrelation image 33 is generated for each of the images 32, comprising a central peak 34.

[0086] A threshold is calculated from the values ​​of the local maxima of the autocorrelation image 33 by excluding the central peak 34, and the central peak 34 is isolated by means of this threshold, to obtain an isolation image 35 comprising only the central peak 34.

[0087] The anisotropy of the image 32 can then be characterized by calculating the eigenvalues ​​and eigenvectors of the inertia matrix of the isolation image 35 of the central peak 34.

[0088] An anisotropy ratio is calculated as the ratio of the lengths of the eigenvectors, and characterizes the presence of a preferred direction of the image 32. Values ​​close to 1 of the anisotropy ratio characterize images 32 where the two principal directions have similar weights, while values ​​far from 1 denote anisotropy.

[0089] It is this preferential orientation of image 32 which is measured because it is impacted in the case of the presence of an anomaly in interlacing of fibers 21.

[0090] A Gabor filter is a linear filter whose impulse response is a sinusoid modulated by a Gaussian function.

[0091] The frequency of the sinusoid could, for example, be chosen to be equal to 0.05 (in pixel1), and the standard deviation 0, of the Gaussian is calculated according to the equation: 3(2 / „(2) p • = 2^7

[0092] Advantageously, four Gabor filters are applied successively to the image 32, with respective bases obtained by rotations from the principal directions of the image 32 by angles which could be, for example, equal to 0°, 45°, 90° and 135°.

[0093] For each pixel of the image 32, the sum of the values ​​obtained with the four Gabor filters is calculated, and the highest sum among the pixels of the image is chosen as the parameter value representative of the application of the tool on the image 32.

[0094] This maximum value can be compared to values ​​from a base of data, and takes on very important values ​​in the case of the presence of interlacing anomalies with linear or non-linear signature.

[0095] The standard deviation of the gray level distribution for each image 32 is calculated, and the standard deviation value is the representative parameter value. This value is characteristic of a non-negligible cluster of one of the material phases, for example, a resin cluster, linear or non-linear. Indeed, the number of pixels with a low gray level (corresponding to the resin phase) will result in an increase in the standard deviation of the gray levels in image 32.

[0096] The method then includes a step of determining spatial variations in each sub-part 31, along the three directions X, Y, Z, of the values ​​of each parameter. Such a representation of the spatial variations makes it possible to simulate the inspection of a sub-part 3A by scrolling through the sections with an inspector.

[0097] The dynamics of images 32 plays an important role in the detection of interlacing anomalies.

[0098] Thus, for each of the characteristic parameters of the previously applied digital tools, and for each of the X, Y and Z analysis directions, a curve of evolution of the parameter values ​​is drawn as a function of the positions of the images in subpart 31 according to the direction.

[0099] The method then includes the detection of non-conforming values ​​of these parameters, which are outside predetermined conformity ranges.

[0100] The conformity ranges are obtained, for each parameter, from healthy test specimens from production, each conformity range including for example the lower 99 percentiles of the values ​​of the associated parameter measured on said healthy test specimens.

[0101] This step makes it possible to detect and quantify the so-called "non-conforming" values ​​for each parameter and according to each direction of space, and to determine a partial conformity score corresponding to the quantity of non-conformities detected.

[0102] The partial compliance scores are for example classified in a matrix with three rows and three columns, for each of the three digital processing tools and each of the three directions.

[0103] An overall conformity score of subpart 31 is obtained by summing the partial scores (for example the nine partial scores in the case of a matrix with three rows and three columns), and makes it possible to characterize the state of conformity of subpart 31, in particular of the weaving of the fibers 21.

[0104] This conformity score is compared to a predetermined conformity threshold value, which depends on the level of requirements relating to part 20, in order to decide whether part 20 has one or more potential anomalies.

[0105] The described method, executed entirely by a computer connected to the system of Tomographic measurement allows for a diagnosis in a rapid time, reduced by a factor of 5, and without requiring the intervention of a qualified inspector.

[0106] If necessary, the inspector can then re-examine the part. In this case, the conformity scores of each sub-part 31 and the curves showing the evolution of the characteristic parameters make it possible to quickly locate the relevant regions of the part 20 to be inspected, and facilitate this additional observation.

Claims

Demands

1. A method for tomographic analysis of a part (20), the method comprising the following steps: - acquiring at least one tomographic image (30) of the part (20) by means of a tomography device (1), - dividing the tomographic image (30) into elementary sub-parts (31) and obtaining a plurality of two-dimensional images (32) for each sub-part (31), the two-dimensional images being cross-sections of the tomographic image in planes perpendicular to three characteristic directions (X, Y, and Z), each image (32) comprising a plurality of pixels, each pixel having a respective gray level, - describing the images (32) by applying at least one digital processing tool to each image (31) of the sub-part (31) and obtaining, for each tool applied, a value of at least one representative parameter for each image (32), - determining spatial variations in each sub-part (31),according to the three characteristic directions (X, Y, Z), of the values ​​of each parameter and detection of parameter values ​​outside predetermined compliance ranges, - calculation of a compliance score for each sub-part (31), from the parameter values ​​outside the compliance ranges, and - comparison of the compliance score of each sub-part (31) to a predetermined compliance threshold to determine the compliance of the sub-part (31).

2. A method according to the preceding claim, wherein the method comprises a step of processing two-dimensional images (31), carried out before the step of applying at least one digital processing tool, during which each pixel whose grey level is outside a predetermined median range is replaced by a corrected pixel having a grey level within said median range.

3. A method according to the preceding claim, wherein the corrected grey level is determined randomly from a distribution of grey levels of the pixels of the image (31) whose grey level is in the median range.

4. A method according to any one of the preceding claims, wherein the at least one digital processing tool includes an autocorrelation calculation, including the generation of an autocorrelation image (33) from the image (31), the detection of a central peak (34) of the autocorrelation image (33) and the determination of an isotropy ratio of the central peak (34), the isotropy ratio being chosen as the representative parameter value.

5. A method according to any one of the preceding claims, wherein at least one digital processing tool comprises a filtering calculation, including the application to the image (31) of at least one Gabor filter and obtaining a filtered image, the determination, for each pixel of the image (31) of a sum of the corresponding values ​​of the images filtered with each of the Gabor filters, the maximum on the image (31) of the sums being chosen as the representative parameter value.

6. Method according to the preceding claim, wherein four Gabor filters are applied, with respective bases obtained from the (X, Y, Z) directions characteristic of the image (31).

7. A method according to any one of the preceding claims, wherein at least one digital processing tool includes a calculation of a standard deviation of the grey levels over all the pixels of the image (31), said standard deviation being chosen as the representative parameter value.

8. A method according to any one of the preceding claims, wherein three digital processing tools are applied to each image (31), the step of calculating the conformity score comprising calculating a partial score for each digital processing tool and for each direction (X, Y, Z), from the quantity of parameter values ​​out of conformity ranges obtained for said digital processing tool and said direction, the conformity score being the sum of the partial scores.

9. A method according to any one of the preceding claims, wherein the parameter compliance ranges are predetermined from sound specimens, each compliance range comprising the lower 99 percentiles of the values ​​of the associated parameter measured on said sound specimens.

10. Product computer program comprising instructions enabling, when executed on a computer controlling a tomographic analysis device (1), the implementation of the method according to one of the preceding claims.

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