A method for analyzing fatigue failure of a blade repaired by additive manufacturing based on a CT scan image

Abstract

The application discloses a kind of based on CT scanning image's additive repair blade fatigue failure analysis method.Combining the way of CT technology and optical microscope is used to carry out defect test analysis and crack imaging to additive repair titanium alloy blade, establish the correlation law between the defect characteristics of titanium alloy blade additive repair area and fatigue performance.First, utilize high-resolution CT technology to carry out nondestructive testing to blade, carry out global defect reconstruction to the leading edge of additive repair titanium alloy blade, obtain the distribution characteristics of defect parameters by statistical analysis;Analysis of defect-related fatigue source and crack propagation behavior, combined with defect-dependent additive repair blade high-cycle fatigue life (limit) model, establish a kind of fatigue failure analysis method based on CT scanning image.Through the method, key defects can be accurately identified and its influence on blade service life is predicted, so as to provide scientific basis and theoretical guidance for repair and reuse of aero-engine blade.

Description

TECHNICAL FIELD

[0001] The present application relates to structural fatigue life prediction, in particular to a method for analyzing fatigue failure of additive repair blade based on CT scan image. BACKGROUND

[0002] In the field of aero-engine, fan and low-pressure compressor blades usually adopt blisk design to improve the stability and efficiency of the structure. However, these blades will bear the double action of vibration stress caused by centrifugal force and airflow excitation during operation. Once the blade is damaged by foreign objects, its fatigue strength will decrease significantly, affecting the safety and reliability of the engine. Traditionally, the repair of damaged blades mainly relies on polishing, welding and other technical means. These methods can reduce local stress concentration or repair the geometric size of the blade to some extent, thereby restoring the fatigue performance of the blade. However, these technologies have limited effect on the treatment of complex damage such as blade corner drop and large gap, and cannot completely restore the performance of the blade.

[0003] In recent years, with the development of additive manufacturing technology, its application in the field of aero-engine blade repair is becoming more and more widespread. Additive manufacturing technology can accurately repair the blade, not only restoring the geometric shape of the blade, but also improving its fatigue performance to some extent. However, the features such as porosity, un-melted defects and residual tensile stress that may be generated during the additive repair process have a non-negligible impact on the fatigue performance of the repaired component. These microscopic defect features, including defect volume, sphericity, aspect ratio, porosity, defect location and type, have an important influence on the fatigue performance and crack propagation behavior of the additive manufacturing component. CT technology is widely used in the detection of internal defects of materials due to its non-contact and non-destructive advantages. CT technology can provide high-resolution three-dimensional images, revealing the microstructure and defect information inside the material. The application of CT technology in defect detection of additive repair blades can accurately identify and characterize various defects introduced during the repair process, providing basic data for subsequent fatigue performance analysis.

[0004] Although researchers have proposed various correction models based on defect-fatigue life test data to improve the accuracy of fatigue life prediction of defect-containing metal materials, the high-cycle fatigue damage mechanism of additive repair blades is still not clear enough, and there is a lack of effective prediction model to accurately evaluate the fatigue performance of the repaired blade. Therefore, based on the CT scan image, it is of great significance to establish a fatigue failure analysis method that can comprehensively consider the influence of additive repair features and defects on improving the performance evaluation and safe use of the repaired blade. SUMMARY

[0005] The present application provides a non-destructive method for analyzing fatigue failure of additive repair blade based on CT scan image.

[0006] Technical scheme: To solve the above problems, the application adopts an additive repair blade fatigue failure analysis method based on CT scan image, including the following steps:

[0007] Step 1: Adopt CT scanning technology and optical microscope to test and analyze the defects of the additive repair titanium alloy simulation blade. Perform defect characterization test on the additive repair area of the blade under the inverted optical microscope;

[0008] Step 2: Titanium alloy simulation blade additive defect section and global test analysis, obtain the projection section and three-dimensional view of the defect distribution of the test piece in three directions;

[0009] Step 3: Statistically analyze the defect parameter distribution of the additive repair titanium alloy leading edge simulation blade, including equivalent spherical diameter distribution, sphericity distribution, horizontal section projection area distribution and volume distribution;

[0010] Step 4: Observe the fatigue crack propagation of the additive repair titanium alloy simulation blade by CT nondestructive testing, observe and analyze the high-cycle fatigue fracture of the additive repair TC17 titanium alloy tensile fatigue test piece by SEM, and explore the defect-related fatigue source and fatigue crack propagation behavior;

[0011] Step 5: Establish the correlation between the defect characteristics of the additive repair titanium alloy simulation blade and the fatigue performance, simulate the fatigue crack propagation behavior of the additive repair blade by the defect-dependent high-cycle fatigue life model, and form an additive repair blade fatigue failure analysis method based on CT scan image.

[0012] Further, polish and polish the additive repair simulation component, take samples and CT scan, and obtain the defect characteristics of the repair area, the defect characteristics of the repair area including defect size, defect position and defect morphology.

[0013] Further, the defect size probability distribution, defect position probability distribution and defect morphology probability distribution of the repair area of the additive repair simulation component are fused into the defect-fatigue life model considering the additive repair defects based on the classical fatigue theory:

[0014]

[0015] Wherein, The fatigue damage parameter of the defect is the product of the maximum stress σ max and the defect characteristic value; N f Indicates the fatigue cycle life; α' and β' are the fitting parameters of the defect fatigue life model, and Y represents the defect type, represents the equivalent circle area of the defect, μ represents the sensitivity of the fatigue life of the material to the defect size, α represents the sensitivity of the fatigue life of the material to the defect sphericity, β represents the sensitivity of the fatigue life of the material to the defect position, D L represents the defect position, C d represents the roundness.

[0016] Further, the defect size is characterized by: the defect is equivalent to an ellipsoid, and the square root of the projection area of the ellipsoid is represented as the defect size; the defect position D L is represented by the normalized distance from the surface of the simulated component; the defect morphology is represented by the sphericity C d ;

[0017]

[0018]

[0019] wherein D f is the thickness of the simulated component, D f is the distance of the defect from the surface of the simulated component; V is the defect volume, and S is the defect surface area.

[0020] Further, the defect size probability distribution, the defect position probability distribution and the defect morphology probability distribution of the repair region of the simulated component repaired by the additive repair are obtained by statistical analysis on the distribution of the defect parameters of the simulated component repaired by the additive repair, and the defect parameters of the simulated component repaired by the additive repair include the defect diameter, the defect sphericity, the defect volume and the projection area of the defect on the X-Y plane.

[0021] The application also adopts a computer device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the steps of the above method when executing the computer program.

[0022] The application also provides a computer readable storage medium, which stores a computer program, and the computer program is executed by the processor to implement the steps of the above method.

[0023] Beneficial effects: The application provides an additive repair blade fatigue failure analysis method based on CT scan images, which has significant beneficial effects. By combining computer tomography technology and optical microscopy, this method can accurately detect and characterize the internal defects of additive repair titanium alloy blades, ensuring the comprehensiveness and accuracy of the detection. Detailed statistical defect parameters provide an important basis for fatigue performance analysis, enabling effective evaluation of the impact of defects introduced during the additive repair process on blade performance. Combined with high-cycle fatigue testing and CT non-destructive testing, the application can accurately evaluate the fatigue performance of the repaired blade, reveal crack propagation and defect-related fatigue sources and behaviors, and establish a correlation between defect characteristics and fatigue performance. This provides a scientific basis for predicting the service life of the repaired blade, improves repair effectiveness and resource utilization, and ensures that the repaired blade meets safety requirements. In particular, for blades damaged by external objects, the fatigue strength can be restored after additive repair, avoiding direct scrapping and saving material resources. Overall, the application provides an efficient and accurate analysis method that provides reliable technical support for blade repair and reuse. BRIEF DESCRIPTION OF DRAWINGS

[0024] Figure 1 The flowchart of the application.

[0025] Figure 2 The CT imaging area of the defect testing scheme of the additive repair TC17 titanium alloy leading edge simulation blade in the application.

[0026] Figure 3 The defect distribution of the additive repair TC17 titanium alloy leading edge simulation blade in the application (a) global view; (b) top view; (c) front view.

[0027] Figure 4 The fatigue crack characterization of the additive repair TC17 titanium alloy leading edge simulation blade in the application (a) right view; (b) front view; (c) left view.

[0028] Figure 5 The global defect characterization of the additive repair TC17 titanium alloy leading edge simulation blade in the application (a) front view; (b) right view.

[0029] Figure 6 The defect parameter distribution statistics of the additive repair TC17 titanium alloy leading edge simulation blade in the application (a) equivalent spherical diameter distribution; (b) sphericity distribution; (c) horizontal cross-sectional projection area distribution; (d) volume distribution.

[0030] Figure 7 The defect parameter distribution statistics of the additive repair TC17 titanium alloy leading edge simulation blade in the application (a) global view; (b) top view; (c) front view; (d) left view. DETAILED DESCRIPTION

[0031] As Figure 1 shown, the additive repair blade fatigue failure analysis method based on CT scan images and defect statistics in the present embodiment includes the following steps:

[0032] Step 1: Artificially machine a semicircular groove with a radius of 3 mm on the titanium alloy simulation blade, and the center of the groove is 10 mm away from the clamping end, as shown in Figure 2 After repairing the groove using the laser cladding process, the surface state is restored by using a grinder and sandpaper for manual polishing in sequence, and finally manual polishing is performed to ensure that the surface of the repaired area of the sample is consistent with the original state, achieving a roughness of nine levels, thereby avoiding stress concentration and fatigue failure problems caused by the roughness of the repaired surface.

[0033] Step 2: Perform global defect characterization testing on the additive repair TC17 titanium alloy leading edge simulation blade on the Phoenix v|tome|x M CT digital tomography system, and the test area is as shown in Figure 2 The high-resolution CT test parameters are as follows: photon energy is 60 keV, voltage is 125 kV, current is 105 μA, exposure time is 500 ms, CT voxel size is 6.47 μm, focus size is 13.125 μm, and filter selection is 0.4 mm copper sheet. The imaging range is 20 mm, and the imaging interval is 5.8 μm.

[0034] Step 3: Further taking the additive repair TC17 titanium alloy leading edge simulation blade after completing the high-cycle fatigue test as an example, the additive defect distribution rule and its relationship with the fatigue crack are analyzed. The 3×10 7 cycle high-cycle fatigue strength test is carried out by using the step-by-step loading method, and the high-cycle fatigue strength of the forged and additive repair TC17 titanium alloy leading edge simulation blade is obtained. The initial load of the forged material blade is set to the maximum stress of the leading edge of 500 MPa, and the load level increment is 25 MPa. The fatigue dispersion of the additive repair blade is large, the initial load is set to the maximum stress of the leading edge of 150 MPa, the load level increment of the first six levels is 15 MPa, the seventh level is 240 MPa, and the load level increment is 25 MPa. The high-cycle fatigue test results of the leading edge simulation blade are shown in Table 1.

[0035] Table 1 High-cycle fatigue test results of the leading edge simulation blade

[0036]

[0037] Step 4: Defect statistical analysis is performed, and the defect statistical analysis of the blade after additive repair is performed to obtain the equivalent spherical diameter and spherical degree probability distribution. Combined with the crack-defect CT scan results, the fatigue crack propagation failure analysis of the additive repair simulation blade is performed.

[0038] Step 5: Combine the high-cycle fatigue test results of the leading edge simulation blade in Step 3 with the defect statistical analysis of the blade after additive repair in Step 4, establish a high-cycle fatigue life prediction model for additive repair, combine the CT defect statistical results with the fatigue limit value, see Table 2. At the same time, based on the CT scanning defect results and the defect analysis near the fatigue source of the SEM fracture, the fatigue failure method of the additive repair blade based on the CT scanning image is formed.

[0039] Table 2: Additive repair titanium alloy defect-high cycle fatigue model parameters

[0040]

[0041] The specific implementation steps are as follows, first, the global defect characterization of the additive repair TC17 titanium alloy leading edge simulation blade is carried out, the equivalent spherical diameter and sphericity of them are statistically analyzed, and the probability density distribution is obtained. Then the high-cycle fatigue test of the additive repair TC17 titanium alloy leading edge simulation blade is carried out, the fatigue limit before and after repair is obtained, and the life extension effect and mechanism of additive repair are studied. Based on the crack-defect CT scanning image, the fatigue failure method of the additive repair blade is established.

[0042] Figure 3 The global defect characterization test results of the additive repair TC17 titanium alloy leading edge simulation blade. During CT testing, the specimen did not undergo high-cycle fatigue testing, and the defect distribution was in the initial state. From the top view ( Figure 3 b) can be seen, the additive defects are point-like and strip-like, and the defects with larger size are located at the bottom of the additive area, i.e. at the additive repair interface position, which is consistent with the test results of the additive repair tensile fatigue specimen. Figure 3 c is the front view of the simulation blade, and defects can also be seen at the boundary of the additive area. This is because the semicircular groove is easy to be unevenly heated at the boundary when laser cladding starts, resulting in over-melting or non-melting of the powder at the additive repair interface, thereby forming defects.

[0043] Unlike forged titanium alloy specimens, cracks in additive repair specimens tend to nucleate at additive defects and then expand in all directions. As can be seen, manufacturing defects are the biggest inducement for fatigue crack initiation of additive repair specimens. The fatigue striation spacing in the additive area is larger, and the fatigue striation spacing in the matrix area is smaller, indicating the difference in crack propagation rate in different areas, and the crack propagation in the additive area is faster. There is a significant difference in the characteristics of the dimple in the transient fracture zone between the additive area and the matrix area, the dimple in the additive area is shallow and small, and the dimple in the matrix area is deep and large, indicating that additive repair leads to a decrease in the plasticity of TC17 titanium alloy.

[0044] Eight defects were identified in the additively repaired TC17 titanium alloy leading edge simulation blade, because the test resolution was low, small size pore-like defects could not be identified. Statistical analysis of their equivalent spherical diameter and sphericity was carried out, the largest defect diameter reached 220 μm, and there were four defects with a diameter of more than 100 μm. The sphericity of the defects roughly presented a negative correlation with their equivalent spherical diameter, the sphericity of the largest defect was the lowest, and the shape was the sharpest; while the sphericity of the small defects was higher, and roughly showed an ellipsoidal shape. Further taking the additively repaired TC17 titanium alloy leading edge simulation blade after completing the high cycle fatigue test as an example, the distribution law of the additive defects and its relationship with the fatigue cracks were analyzed. The initial load level of the leading edge simulation blade was 150 MPa, and the twelfth level was 355 MPa. The test was stopped at 0.22×10 7 cycles, and the test result was failure.

[0045] The CT test results of the fatigue cracks of the additively repaired TC17 titanium alloy leading edge simulation blade are shown in Fig. 2. Figure 4 From the figure, it can be observed that there are cracks in the blade basin and the blade back, and they penetrate through the entire front edge of the test piece. The crack propagation direction is perpendicular to the front edge of the test piece. Figure 5 The global defect distribution diagram of the leading edge simulation blade is shown in Fig. 3. There are a large number of pore-like defects along the crack propagation path, which are similar in size and morphology, and all show blue in the size cloud diagram, indicating that the volume is within 200000 μm 3 . Two obvious LOF defects can be seen in the figure, one is strip-shaped, with a volume of 686899 μm 3 ; the other is irregular, with a triangular projection plane, and the volume is 1855032 μm 3 . In addition, no large volume defects were observed.

[0046] Statistical analysis was carried out on the distribution of the defect parameters of the leading edge simulation blade, including the defect diameter, the defect sphericity, the defect volume and the projection area of the defect on the X-Y plane, which were used to measure the size and shape distribution law of the defects, as shown in Fig. 4. Figure 6 Figure 6 It can be observed in a that the defect diameter is almost within 100 μm, and the defects with a size of less than 50 μm account for more than 90%. Figure 6 b shows the sphericity distribution of the leading edge simulation blade, and the number of defects with a sphericity greater than 0.6 accounts for 80%, indicating that the defect shape is mainly spherical and ellipsoidal, and the number of sharp defects is small. Figure 6 c and d respectively show the projection area and volume distribution of the defects on the X-Y plane of the test piece, both of which are concentrated in a small numerical range. Two larger data in the axis can represent the two LOF defects described above. Most of the X-Y plane projection area is within 2500 μm 2 , and the defect volume is almost less than 50000 μm​3 . Figure 6 The four statistical data in Table 4 show that the additive defects of the leading edge simulation blade are mainly pores, and the defect size is small and the shape is regular.

[0047] The fatigue crack and defect distribution of the leading edge simulation blade are shown in Figs. 6 and 7. Figure 7 The fatigue cracks of the simulation blade samples all pass through the entire leading edge region of the sample and expand inward. There are also fatigue cracks of the sample in the internal region of the leading edge, which do not expand to the outside of the leading edge. This shows that the fatigue cracks are generated in the internal region of the sample, rather than at the maximum stress point of the leading edge, because the additive defects cause greater stress concentration. Figure 7 As can be seen from Table 4, there are a large number of defect groups composed of pores at the boundary of the additive region, which is the same as the CT results of the samples without fatigue test.

[0048] The high-cycle fatigue test results of the leading edge simulation blade are shown in Table 1. As can be seen from Table 1, the average high-cycle fatigue strength of the forged material leading edge simulation blade at a stress ratio R = -1 and a single cycle number of 3 x 107 is 567.13 MPa, and the fatigue strength of the simulation blade after additive repair is significantly lower than that of the base material. The fatigue strength of the simulation blade repaired by a 3 mm deep semicircular notch is in the range of 180-360 MPa, and the results have large dispersion, most of which are above 260 MPa, and the average is 268.90 MPa. The fatigue strength of this kind of repaired sample is about 52.4% lower than that of the forged material simulation blade. The fatigue strength of the simulation blade repaired by a large-size notch (3 mm deep semicircular) is almost all above 200 MPa, which can meet the safety use requirements. The high-cycle fatigue strength (average 270.67 MPa) of the simulation blade repaired by a large-size notch is increased by 113.5% compared with that of the foreign object damage (FOD) simulation blade (average 126.59 MPa), and the repair effect is significant. It can be seen that additive repair is very necessary and effective for the continued use of the FOD blade. For the large-size notch blade after FOD, the residual fatigue strength does not meet the safety use requirements of the fatigue strength reserve regulation, and can only be scrapped according to the requirements. After additive repair, the fatigue strength can be restored to a certain extent to meet the safety use requirements of the fatigue strength reserve, which greatly saves the blade resources.

Claims

1. A method for additive repair of a blade fatigue failure analysis based on CT scan images, characterized by, The method comprises the following steps: Step 1: Defect testing and analysis of the additive repair titanium alloy simulation blade are carried out by combining CT technology with an optical microscope; defect characterization testing of the additive repair region of the blade is carried out on an inverted optical microscope; Step 2: Cross-section and global testing analysis of the additive defect of the titanium alloy simulation blade are carried out to obtain the defect distribution of the projection cross-section and three-dimensional view of the specimen in three directions; Step 3: Defect parameter distribution of the additive repair titanium alloy leading edge simulation blade is counted, including equivalent spherical diameter distribution, sphericity distribution, horizontal cross-section projection area distribution and volume distribution; Step 4: The fatigue crack propagation of the additive repair titanium alloy simulation blade is observed by CT nondestructive testing; the high-cycle fatigue fracture of the additive repair TC17 titanium alloy tensile fatigue specimen is observed and analyzed by SEM; the defect-related fatigue source and fatigue crack propagation behavior are explored; firstly, the fatigue source is found through fracture analysis; the high-cycle fatigue fracture of the additive repair TC17 titanium alloy tensile fatigue specimen is observed and analyzed by SEM; the crack initiation mode of the specimen is confirmed; the fatigue initiation and propagation mechanism after additive repair is explored; the dimple size on the SEM image of the fatigue fracture is quantitatively analyzed by using the image analysis software Image Pro Plus; before testing, the specimen is cut off at the fracture site by using wire cutting; the specimen is naturally divided into two parts from the cross-section; one of the two parts is cleaned by using anhydrous ethanol in an ultrasonic cleaner; after drying, the fatigue fracture is observed by using SEM; then, defects are detected by CT scanning; the defect size probability distribution, defect position probability distribution and defect morphology probability distribution of the repair region of the additive repair simulation component are obtained by statistical analysis of the defect parameter distribution of the additive repair simulation component; the defect parameters of the additive repair simulation component include defect diameter, defect sphericity, defect volume and defect projection area in the X-Y plane; Step 5: The correlation between the defect characteristics of the additive repair titanium alloy simulation blade and the fatigue performance is established; the fatigue crack propagation behavior of the additive repair blade is simulated by using the defect-dependent high-cycle fatigue life model; and a fatigue failure analysis method for the additive repair blade based on CT scanning images is formed.

2. The method of claim 1, wherein, High-resolution CT is used to carry out global defect characterization testing of the additive repair TC17 titanium alloy simulation blade on the Phoenix v|tome|x M type CT digital tomography system; the high-resolution CT testing parameters are as follows: photon energy is 60 keV, voltage is 125 kV, current is 105 mu A, exposure time is 500 ms, CT voxel size is 6.47 mu m, focus size is 13.125 mu m, filter piece is 0.4 mm copper piece, imaging range is 20 mm, and imaging interval is 5.8 mu m.

3. The method of claim 2, wherein, The defect size probability distribution, defect position probability distribution and defect morphology probability distribution of the repair region of the additive repair simulation blade are counted; the sphericity is used as a parameter to measure the shape of the defect; the calculation formula of the sphericity of the defect is as follows: In the formula, C d Let V be the sphericity, V be the defect volume, and S be the defect surface area. The equivalent sphere diameter expresses the actual size of the defect, while the circumscribed sphere diameter expresses the spherical volume occupied by the defect in space. Furthermore, the defect location also affects the high-cycle fatigue failure mechanism of additively repaired blades, necessitating the introduction of the defect location feature D. L This also requires measuring the distance D from the defect to the sample surface. d D f For the sample thickness, use D L After normalization, the location of the fatal defect is represented by the following expression: D L For normalized defect position, D d is 0, D L is 1, indicating the defect is on the surface of the sample, D L is 0.5, indicating the defect is at the center of the sample. Similarly, the internal defects of the sample are precisely characterized by X-CT, and are equivalent to ellipsoids, which are projected to the square root of the projected area as the characteristic size of the defect; The fatigue dispersion and size effect are evaluated by a statistical method.

4. The method of claim 3, wherein, A defect-dependent high-cycle fatigue life model is established: wherein is the maximum stress applied, Y is the shape factor, in the three-dimensional case, , in the two-dimensional case, ; are material parameters related to the sphericality, position, size of the defect, respectively, is a fitting parameter, resulting from the fitting of the defect-fatigue life curve; A high-cycle fatigue life prediction model of the additive repair blade is established, and a fatigue failure analysis method of the additive repair blade based on a CT scan image is formed.

5. A computer device comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, The processor implements the steps of the method of any one of claims 1 to 4 when executing the computer program.

6. A computer-readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the method of any one of claims 1 to 4.

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