Tomographic analysis method

Abstract

A method for tomographic analysis of a part to detect anomalies (30), the method comprising the following steps: - acquisition of at least one three-dimensional image (25) of the part by means of a tomography device, - subdivision of the image (25) into elementary sub-parts (27), - analysis of a distribution of gray levels in each sub-part (27) and obtaining at least one parameter representative of said distribution of gray levels for each sub-part (27), - comparison of the parameter(s) obtained for each sub-part (27) with standard values ​​characteristic of a healthy region and detection of abnormal sub-parts (31) whose parameter(s) differ from the standard values, - determination of risk regions, comprising each abnormal sub-part (31) and each neighboring sub-part (33) of at least one abnormal sub-part, and - analysis of the risk regions to detect anomalies (30) in the part.Figure to be published with the abstract: 6.

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 precisely of a turbomachine, such as a part of a fan casing, a fan blade, or a part of a fixed blade structure. State of the prior art

[0002] It is known to inspect structural mechanical parts by tomography 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 the interior of parts to be inspected, which makes it possible to quickly determine the condition of the part and decide whether it should be put back into operation or replaced.

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

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

[0006] The tomography device 1 comprises at least one emission device 3, configured to emit an incident beam 5 of wave pulses in the direction of the part 20, for example radiofrequency waves, X-rays or even 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 rotating around an axis A, in order to be observed from all directions during the tomographic measurement.

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

[0009] Such a composite material comprises weft fibers 21 and warp fibers 22 woven with each other on a weaving plane P, or several weaving planes P superimposed along a thickness direction Z.

[0010] The fibers 21 and 22 may be carbon fibers, glass fibers or a mixture of the two. 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] In the case of a metallic material, it can be defined on a mesoscopic scale as a set of grains of material 32 linked together by grain boundaries 33, as shown in [Fig.3]. These grains 32 are three-dimensional domains that can have various and varied shapes, with a preferred direction or orientation over the extent of the grain 32, which can lead to an orthotropic metallic material.

[0012] The amplitudes of the waves transmitted from the different observation angles are translated into gray levels in a processing device 13, and a digital analysis makes it possible to reconstruct the volume of the room 20. The processing device 13 comprises a computer.

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

[0014] A human operator can then assess the quality of the part by searching the three-dimensional image for the presence or absence of an anomaly or damage.

[0015] In the following, the terms “non-conformity”, “anomaly”, “damage”, “damage” or equivalent are used interchangeably to designate a part of the part where the mechanical properties of the part are locally degraded compared to those of a part in good condition.

[0016] It may also be the presence of a foreign body in the material, such as a void (porosity) or a material that has arrived by mistake in the part during its shaping. Such an anomaly may justify removal and replacement of the part when its mechanical strength is compromised, or, depending on the case, may not prevent the part from functioning properly.

[0017] In the case of a composite material, it may also be an area where the fibers 21, 22 are stretched or broken.

[0018] In the case of a metallic material, this may again be an area with porosities or a foreign body, joints of lower quality affecting the mechanical characteristics or atypical grain sizes.

[0019] It is important to note that the detection and characterization of the size and shape of the anomaly is still currently left to the human operator. This step generates a significant delay because it is necessary to clearly define the non-compliant zone and an observation of the entire volume of the part is currently necessary to detect all possible anomalies. Presentation of the invention

[0020] The invention aims to remedy these drawbacks, by providing an analysis method allowing rapid detection of potential anomalies without requiring the intervention of an operator, and without risking ignoring a potential anomaly.

[0021] To this end, the invention relates to a method for tomographic analysis of a part to detect anomalies, the method comprising the following steps:

[0022] - acquisition of at least one three-dimensional image of the part by means of a tomography device,

[0023] - subdivision of the image into elementary sub-parts,

[0024] - analysis of a distribution of gray levels in each sub-part and obtaining of at least one parameter representative of said gray level distribution for each sub-part,

[0025] - comparison of the parameter(s) obtained for each sub-part with ranges of standard values ​​characteristic of a healthy region and detection of abnormal sub-parts whose parameter(s) are outside the standard value ranges,

[0026] - determination of risk regions, comprising each abnormal sub-part and each neighboring sub-part of at least one abnormal sub-part, and

[0027] - analysis of risk regions to detect anomalies in the room.

[0028] Such a method makes it possible to detect anomalies in a room reliably and non-invasive, with reduced intervention time for the operator during the analysis stage of the risk regions, without increasing the risk of missing an anomaly.

[0029] The subdivision step may comprise determining at least one standard dimension of the anomalies sought in the part, each sub-part having dimensions between half the standard dimension and double the standard dimension.

[0030] The standard dimension is for example obtained from previous test results stored in a database.

[0031] The dimensions of the sub-parts may include a height, a width, a length of the sub-part.

[0032] Alternatively, each sub-part may have dimensions greater than twice the standard dimension.

[0033] Such a characteristic makes it possible to optimize the size of the risk region studied according to the anomalies to be considered.

[0034] At least one of the parameters representative of the gray level distribution can be chosen from the average, the maximum and the minimum of the gray level distribution on the sub-part.

[0035] At least one of the parameters representative of the gray level distribution can be chosen from the gradient, the rotational and the divergence of the gray level distribution on the sub-part.

[0036] The comparison step may implement at least one digital processing tool chosen from a nearest neighbor analysis, a classification tree analysis, a support vector machine analysis and a network analysis. neurons.

[0037] Alternatively, the comparison step may consist of comparing the measured value with a range of standard values ​​characteristic of a healthy region, to determine whether the measured value is within said range or not.

[0038] The range of standard values ​​characteristic of a healthy value may be a range centered on a standard average value and of an amplitude for example equal to 10% of this standard average value.

[0039] The part may comprise a woven composite material.

[0040] The composite material may be woven in a two-dimensional pattern. Such a material comprises warp fibers and weft fibers woven together to form at least one weaving plane, said fibers being embedded in a matrix.

[0041] The fibers may be in the form of independent weaving planes pre-impregnated with the uncured matrix, and deposited on top of each other.

[0042] Alternatively, all weaving planes may be placed, for example in a mold, before injection of the uncured matrix.

[0043] Alternatively, the composite material may be woven in a three-dimensional pattern, with fibers extending in at least three non-coplanar directions, embedded in a matrix as above. Such a material is also known as "interlock."

[0044] The fibers of the woven composite material may comprise glass fibers and / or carbon fibers.

[0045] The matrix may comprise at least one polymer, in particular a thermosetting polymer, and / or at least one resin.

[0046] The part may comprise a metallic material.

[0047] The part may be a turbomachine casing part, or a compressor rotor, stator or fan blade of a turbomachine. Brief description of the figures

[0048] [Fig-1] [Fig.l] is a schematic side view of a tomography device at during the implementation of a method according to the invention,

[0049] [Fig.2] [Fig.2] is a schematic detail view of a part made of material woven composite,

[0050] [Fig.3] [Fig.3] is a schematic detail view of a part made of metallic material,

[0051] [Fig.4] [Fig.4] is a schematic view of a three-dimensional image of a part including an anomaly,

[0052] [Fig.5] [Fig.5] is a schematic view of a step of subdividing the three-dimensional image of [Fig.4], and

[0053] [Fig.6] [Fig.6] is a schematic view of a step of analysis of the subdivisions of [Fig.5]. Detailed description of the invention

[0054] 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 from composite material or metallic material.

[0055] The method comprises a first step of acquiring at least one three-dimensional image of the part 20 by means of the tomography device 1.

[0056] An incident beam 5 of wave pulses is emitted by the emission device 3, in the direction of the part 20. The incident beam 5 is for example a beam of radiofrequency waves.

[0057] The waves pass through the room 20, and a transmitted beam 9 coming from the room 20 is picked up by the receiver 7. The intensity distribution of the transmitted beam 9 obtained is converted into a two-dimensional grayscale image of the room 20 by the processing device 13.

[0058] The part 20 is rotated by means of the support 11, and two-dimensional images of the part 20 are acquired from all directions.

[0059] A three-dimensional image of the part 20 in gray levels, or tomographic image, is then reconstructed by image processing using the processing device 13.

[0060] In the example described, the part 20 is a part made of composite material, as shown in [Fig.2].

[0061] The composite material comprises weft fibers 21 extending in a weft direction X and warp fibers 22 extending in a warp direction Y, embedded in a matrix 23. The average distance between two neighboring weft fibers 21 or warp fibers 22 is of the order of 2 mm.

[0062] A thickness of the part 20, measured along a thickness direction Z, is for example between 5 and 25 mm, in particular close to 10 mm for a part 20 comprising four to eight planes of superimposed weaving planes.

[0063] The weft 21 and warp 22 fibers may be of identical or different materials (glass or carbon).

[0064] The matrix 23 comprises at least one organic polymer and / or at least one resin.

[0065] Woven composite materials can be considered as gold materials thotropic, that is to say materials having three planes of symmetry at the level of their internal microstructure.

[0066] Alternatively, the part 20 is composed of a metallic material consisting of metallic grains 32 and comprising joints 33 between these grains 32. A thickness of the part measured in the Z direction is for example between 2 and 25 mm, in particular close to 7.5 mm. The metallic material can be isotropic or orthotropic depending on its internal micro structure.

[0067] The analysis method comprises a step of determining the standard dimensions of the anomalies sought in the part 20.

[0068] The standard dimensions depend on the nature of the material, as well as the acceptable degradation threshold in the part 20.

[0069] For example, in a part 20 intended for the aeronautical field, anomalies that are too small do not cause sufficient degradation of the mechanical properties of the part 20 to be taken into account.

[0070] An example of standard dimensions of anomalies is 1 mm in the three directions X, Y, Z.

[0071] The method then comprises a step of subdividing the three-dimensional image into elementary sub-parts.

[0072] Such a three-dimensional image 25 subdivided into elementary sub-parts 27 is represented schematically in [Fig.4], in which the part 20 comprises an anomaly 30.

[0073] The elementary sub-parts 27 are for example cubic, with a side size D equal to the standard dimension of the anomalies sought.

[0074] Alternatively, the side size D of the sub-parts 27 is greater than the standard dimension of the anomalies sought, in particular twice as large.

[0075] The method comprises a step of analyzing the distribution of gray levels in each sub-part 27 and determining a value of at least one parameter representative of this distribution for each sub-part 27.

[0076] Examples of representative parameters are: the average of the gray levels, the maximum level, the minimum level but also more complete mathematical operators such as the average gray level gradient on the sub-part 27, or even the rotation and the divergence of the gray level on the sub-part 27. These parameters allow a rapid description of the gray level distributions in each sub-part 27.

[0077] The method then comprises a comparison step, during which the values ​​of each representative parameter obtained for each sub-part 27 are compared with the known values ​​corresponding to sub-parts known to be without anomalies.

[0078] This comparison step may consist of a simple verification of the values ​​of the parameter studied in relation to a range of values ​​identified on the healthy areas of the material, or in a more in-depth classification analysis.

[0079] In the case of classification analysis, dedicated digital processing tools such as nearest neighbor analyses, classification trees, can be used. If the data volume is sufficient, analysis by support vector machine or neural networks can be implemented.

[0080] The comparison step then allows the determination of abnormal sub-parts 31, for which the values ​​of the representative parameters differ from the values ​​identified on the healthy areas. The abnormal sub-parts 31 are represented in [Fig.6] with cross hatching.

[0081] The method then comprises a step of determining risk regions, comprising all the abnormal sub-parts 31, as well as each sub-part 33 directly neighboring at least one abnormal sub-part 31.

[0082] The sub-parts 33 directly neighboring at least one abnormal sub-part 31 are marked with simple hatching in [Fig.6].

[0083] In this way, the risk region is identified with a margin around the identified abnormal sub-parts, so as not to miss any anomaly.

[0084] Sub-parts 27 not forming part of a risk region are excluded and classified as a healthy zone.

[0085] The risky regions are then manually inspected by an operator, to detect anomalies in the part, while the healthy areas are not inspected, thus reducing the intervention time of a qualified operator.

Claims

Claims

1. Method for tomographic analysis of a part (20) to detect anomalies (30), the method comprising the following steps: - acquisition of at least one three-dimensional image (25) of the part (20) by means of a tomography device (1), - subdivision of the image (25) into elementary sub-parts (27), - analysis of a distribution of gray levels in each sub-part (27) and obtaining at least one parameter representative of said distribution of gray levels for each sub-part (27), - comparison of the parameter(s) obtained for each sub-part (27) with standard values ​​characteristic of a healthy region and detection of abnormal sub-parts (31) whose parameter(s) differ from the standard values, - determination of risk regions, comprising each abnormal sub-part (31) and each neighboring sub-part (33) of at least one abnormal sub-part,and - analysis of risk regions to detect anomalies (30) in the room (20).,

2. The method of claim 1, wherein the subdividing step comprises determining at least one standard dimension of the anomalies (30) sought in the part (20), each sub-part (27) having dimensions between half the standard dimension and twice the standard dimension.

3. Method according to one of the preceding claims, in which at least one of the parameters representative of the gray level distribution is chosen from the average, the maximum and the minimum of the gray level distribution on the sub-part (27).

4. Method according to one of the preceding claims, in which at least one of the parameters representative of the gray level distribution is chosen from the gradient, the rotational and the divergence of the gray level distribution on the sub-part (27).

5. Method according to one of the preceding claims, in which the comparison step implements at least one digital processing tool chosen from a nearest neighbor analysis, a classification tree analysis, a support vector machine analysis and a neural network analysis.

6. Method according to one of the preceding claims, in which the part (20) comprises a woven composite material.

7. Method according to one of the preceding claims, in which the part (20) comprises a metallic material.

8. Method according to one of the preceding claims, in which the part (20) is a part of a turbomachine casing, or a compressor rotor, stator or fan blade of a turbomachine.

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