Additive repair blade fatigue failure analysis method based on CT scanning image

Abstract

The invention discloses an additive repair blade fatigue failure analysis method based on a CT scanning image. A mode of combining a CT technology and an optical microscope is adopted to carry out defect test analysis and crack imaging on the titanium alloy blade subjected to additive repair, and a correlation rule between defect characteristics and fatigue performance of an additive repair area of the titanium alloy blade is established. The method comprises the following steps: firstly, carrying out nondestructive testing on the blade by utilizing a high-resolution CT technology, carrying out global defect reconstruction on the front edge of the titanium alloy blade subjected to additive repair, and obtaining distribution characteristics of defect parameters through statistical analysis; the fatigue failure analysis method based on the CT scanning image is established by analyzing a defect-related fatigue source and crack propagation behaviors and combining a defect-dependent additive repair blade high-cycle fatigue life (limit) model. Through the method, the key defect can be accurately identified and the influence of the key defect on the service life of the blade can be predicted, so that a scientific basis and theoretical guidance are provided for repairing and reusing the aero-engine blade.

Description

Technical Field

[0001] The present invention relates to structural fatigue life prediction, and in particular to a fatigue failure analysis method for blades repaired by additive manufacturing based on CT scanning images. Background Art

[0002] In the field of aero engines, fan and low-pressure compressor blades usually adopt an integral blade disc design to improve structural stability and efficiency. However, these blades are subjected to the dual effects of centrifugal force caused by high speed and vibration stress caused by airflow excitation during operation. Once the blades are damaged by foreign objects, their fatigue strength will drop significantly, affecting the safety and reliability of the engine. Traditionally, the repair of damaged blades mainly relies on technical means such as grinding and welding. These methods can reduce local stress concentration or repair the geometric dimensions of the blades to a certain extent, thereby restoring the fatigue performance of the blades. However, these technologies have limited effects on complex damage conditions such as blade corner loss and large notches, and cannot completely restore the performance of the blades.

[0003] In recent years, with the development of additive manufacturing technology, its application in the field of aircraft engine blade repair has become increasingly widespread. Additive manufacturing technology can accurately repair blades, not only to restore the geometric shape of the blades, but also to improve their fatigue performance to a certain extent. However, the characteristics of porosity, unfused defects and residual tensile stress that may be generated during the additive repair process have an important impact on the fatigue performance of the repaired components. These microscopic defect characteristics, including defect volume, sphericity, aspect ratio, porosity, defect location and type, have an important influence on the fatigue performance and crack propagation behavior of additively manufactured components. CT technology is widely used in the detection of internal defects in materials due to its non-contact and non-destructive advantages. CT technology can provide high-resolution three-dimensional images to reveal the microstructure and defect information inside the material. Applying CT technology to defect detection of additively repaired 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 a variety of correction models to improve the accuracy of fatigue life prediction of defective metal materials based on defect-fatigue life test data, the high-cycle fatigue damage mechanism of additively repaired blades is still not clear enough, and there is a lack of effective prediction models to accurately evaluate the fatigue performance of repaired blades. Therefore, based on CT scan images, it is of great significance to establish a fatigue failure analysis method that can comprehensively consider the characteristics of additive repair and the influence of defects, in order to improve the performance evaluation and safe use of repaired blades. Summary of the invention

[0005] Purpose of the invention: In view of the above shortcomings, the present invention provides a non-destructive fatigue failure analysis method for blades repaired by additive manufacturing based on CT scanning images.

[0006] Technical solution: To solve the above problems, the present invention adopts a fatigue failure analysis method of blades repaired by additive repair based on CT scanning images, comprising the following steps:

[0007] Step 1: Use CT scanning technology combined with optical microscopy to conduct defect testing and analysis on the additively repaired titanium alloy simulated blade. Conduct defect characterization testing on the additively repaired area of ​​the blade on an inverted optical microscope;

[0008] Step 2: Analysis of defect cross-section and global test of titanium alloy simulated blade additive, obtaining the projection cross-section of the specimen in three directions and the defect distribution of the three-dimensional view;

[0009] Step 3: Statistical distribution of defect parameters of the simulated blade with additively repaired titanium alloy leading edge, including equivalent spherical diameter distribution, sphericity distribution, horizontal cross-sectional projection area distribution, and volume distribution;

[0010] Step 4: Observe the fatigue crack propagation of the additively repaired titanium alloy simulated blade by CT nondestructive testing, and observe and analyze the high-cycle fatigue fracture of the additively repaired TC17 titanium alloy tensile fatigue specimen by SEM to explore its defect-related fatigue source and fatigue crack propagation behavior;

[0011] Step 5: Establish the correlation between the defect characteristics and fatigue performance of the additively repaired titanium alloy simulated blade, simulate the fatigue crack propagation behavior of the additively repaired blade through the defect-dependent high-cycle fatigue life model, and form a fatigue failure analysis method for additively repaired blades based on CT scanning images.

[0012] Furthermore, the additive repair simulation component is ground and polished, sliced ​​and sampled, and CT scanned to obtain defect characteristics of the repaired area, wherein the defect characteristics of the repaired area include defect size, defect location, and defect morphology.

[0013] Furthermore, the statistical probability distribution of defect size, defect location and defect morphology of the repaired area of ​​the additive repair simulation component is integrated into the classical fatigue theory to establish a defect-fatigue life model considering the defects of additive repair:

[0014]

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

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

[0017]

[0018]

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

[0020] Furthermore, 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 obtained by statistically analyzing the distribution of defect parameters of the additive repair simulation component. The defect parameters of the additive repair simulation component include defect diameter, defect sphericity, defect volume and the projection area of ​​the defect on the XY plane.

[0021] The present invention also adopts a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the computer program.

[0022] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0023] Beneficial effects: The present invention provides a fatigue failure analysis method for additively repaired blades based on CT scan images, which has significant beneficial effects. By combining computer tomography technology and optical microscopy, the method can detect and characterize the internal defects of additively repaired titanium alloy blades with high precision, ensuring the comprehensiveness and accuracy of the detection. The detailed statistical defect parameters provide an important basis for fatigue performance analysis, making it possible to effectively evaluate 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 present invention can accurately evaluate the fatigue performance of repaired blades, reveal the fatigue sources and behaviors related to crack propagation and defects, and establish the correlation between defect characteristics and fatigue performance. This provides a scientific basis for predicting the service life of repaired blades, improves the repair effect and resource utilization, and enables the repaired blades to meet the safety requirements. In particular, for blades damaged by external objects, their fatigue strength can be restored after additive repair, avoiding direct scrapping and saving material resources. In general, the present invention provides an efficient and accurate analysis method, which provides reliable technical support for blade repair and reuse. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a schematic diagram of the process of the present invention.

[0025] Figure 2 This is the CT imaging area of ​​the test scheme for simulating blade defects in the additive repair of TC17 titanium alloy leading edge in the present invention.

[0026] Figure 3 Simulated blade defect distribution for additively repairing the leading edge of TC17 titanium alloy in the present invention (a) global view; (b) top view; (c) front view.

[0027] Figure 4 Characterization of fatigue crack of simulated blade of TC17 titanium alloy leading edge repaired by additive manufacturing in the present invention (a) right view; (b) front view; (c) left view.

[0028] Figure 5 The global defect characterization of the blade simulated by additively repairing the leading edge of TC17 titanium alloy in the present invention (a) front view; (b) right view.

[0029] Figure 6 Distribution statistics of defect parameters of the simulated blade for additively repairing the leading edge of TC17 titanium alloy in the present invention: (a) equivalent spherical diameter distribution; (b) sphericity distribution; (c) horizontal cross-sectional projection area distribution; (d) volume distribution.

[0030] Figure 7 Distribution statistics of blade defect parameters for the additively repaired TC17 titanium alloy leading edge simulation in the present invention (a) global view; (b) top view; (c) front view; (d) left view. DETAILED DESCRIPTION

[0031] like Figure 1 As shown, the fatigue failure analysis method of blades repaired by additive manufacturing based on CT scanning images and defect statistics in this embodiment includes the following steps:

[0032] Step 1: Add a semicircular groove to the titanium alloy simulated blade with a radius of 3mm and a groove center distance of 10mm from the clamping end. Figure 2 After the groove was repaired by laser cladding, the surface was restored by manual grinding with a grinder and sandpaper, and finally manual polishing was performed to ensure that the surface of the repaired area of ​​the specimen was consistent with the original state, reaching level nine roughness, to avoid stress concentration and fatigue failure caused by the roughness of the repaired surface.

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

[0034] Step 3: Taking the additive repaired TC17 titanium alloy leading edge simulation blade after high cycle fatigue test as an example, the distribution law of additive defects and its relationship with fatigue cracks are analyzed. 7 Cyclic high-cycle fatigue strength test was conducted to obtain the high-cycle fatigue strength of the forged and additively repaired TC17 titanium alloy leading edge simulation blades. The initial load of the forged material blade was set to the maximum leading edge stress of 500MPa, and the load level increase was 25MPa. The fatigue dispersion of the additively repaired blade was large, and the initial load was set to the maximum leading edge stress of 150MPa. The first six load levels increased by 15MPa, and the seventh level started at 240MPa, and the load level increased by 25MPa. The high-cycle fatigue test results of the leading edge simulation blade are summarized in Table 1.

[0035] Table 1 High cycle fatigue test results of leading edge simulated blade

[0036]

[0037] Step 4: Conduct defect statistical analysis on the blade after additive repair to obtain the probability distribution of equivalent spherical diameter and sphericity. Combined with the crack-defect CT scanning results, perform fatigue crack propagation failure analysis of the additively repaired simulated blade.

[0038] Step 5: Combine the high-cycle fatigue test results of the leading edge simulation blade in step 3 with the statistical analysis of blade defects after additive repair in step 4 to establish a high-cycle fatigue life prediction model for additive repair. Combine the CT defect statistical results and fatigue limit values ​​to give the model parameters, 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, a fatigue failure method for blades repaired by additive repair based on CT scanning images is formed.

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

[0040]

[0041] The specific implementation steps are as follows: first, perform global defect characterization of the leading edge of the TC17 titanium alloy simulated blade with additive repair, perform statistical analysis on their equivalent spherical diameter and sphericity, and obtain their probability density distribution. Then, perform high-cycle fatigue test on the leading edge of the TC17 titanium alloy simulated blade with additive repair, obtain the fatigue limit before and after repair, and study the life extension effect and mechanism of additive repair. Based on the crack-defect CT scanning image, establish an additive repair blade fatigue failure method.

[0042] Figure 3 The global defect characterization test results of the leading edge simulation blade of TC17 titanium alloy for additive repair. During the CT test, the specimen was not subjected to high cycle fatigue test and the defect distribution was in the initial state. From the top view ( Figure 3 It can be seen from Figure b) that the additive defects are in the form of points and strips, and the larger defects are located at the bottom of the additive area, that is, the additive repair interface, which is consistent with the test results of the additive repaired tensile fatigue specimens. Figure 3 c is the front view of the simulated blade, and it can be seen that the defect is located at the boundary of the additive area. This is because the semicircular groove is easily heated unevenly at the boundary at the beginning of laser cladding, resulting in over-melting or under-melting of powder at the additive repair interface, thus forming defects.

[0043] Unlike forged titanium alloy specimens, cracks in additively repaired specimens tend to nucleate at additive defects and then expand to the surrounding areas. This shows that manufacturing defects are the biggest cause of fatigue crack initiation in additively repaired specimens. The fatigue striation spacing in the additive area is larger, while the fatigue striation spacing in the matrix area is smaller, indicating the difference in crack growth rates in different regions, with crack growth in the additive area being faster. There are obvious differences in the dimple characteristics of the instantaneous fracture zone between the additive area and the matrix area. The dimples in the additive area are shallow and small, while those in the matrix area are deep and large, indicating that additive repair has led to a decrease in the plasticity of TC17 titanium alloy.

[0044] A total of 8 defects were identified in the additively repaired TC17 titanium alloy leading edge simulation blade. Due to the low test resolution used, small-sized pore defects could not be identified. A statistical analysis of their equivalent spherical diameters and sphericity showed that the largest defect diameter reached 220μm, and there were 4 defects with a diameter of more than 100μm. The sphericity of the defect and its equivalent spherical diameter roughly showed a negative correlation. The largest defect had the lowest sphericity and the sharpest shape; while the small-sized defects had a higher sphericity, which was roughly ellipsoidal. Further, taking the additively repaired TC17 titanium alloy leading edge simulation blade after high-cycle fatigue testing as an example, the distribution law of additive defects and its relationship with fatigue cracks were analyzed. The initial load level of the leading edge simulation blade was 150MPa, and a 0.22×10 7 Failed during looping.

[0045] The CT test results of fatigue cracks in the leading edge of the TC17 titanium alloy simulated blade repaired by additive manufacturing are shown in the figure. Figure 4 As shown in the figure, it can be seen that there are cracks on both the blade base and the blade back, and they run through the entire front edge of the specimen. The crack propagation direction is perpendicular to the front edge of the specimen. Figure 5 The global defect distribution diagram of the leading edge simulated blade. There are a large number of pore defects along the crack propagation path. The size and morphology are similar. They are all blue in the size cloud map, indicating that the volume is 200000μm 3 There are two obvious LOF defects in the picture, one is strip-shaped and has a volume of 686899μm 3 The other is irregular in shape, with a triangular projection surface and a volume of 1855032μm 3 Apart from this, no large volume defects were observed.

[0046] The distribution of leading edge simulated blade defect parameters is statistically analyzed, including defect diameter, defect sphericity, defect volume, and defect projection area on the XY plane, to measure the size and shape distribution of defects, such as Figure 6 shown. Figure 6 It can be observed in a that the defect diameters are almost all within 100 μm, of which defects smaller than 50 μm account for more than 90%. Figure 6 b shows the sphericity distribution of the leading edge simulated blade. The number of defects with a sphericity greater than 0.6 accounts for 80%, indicating that the defect shapes are mainly spherical and ellipsoidal, and the number of irregular sharp defects is rare. Figure 6 Figures c and d show the distribution of the projected area and volume of the defects on the XY plane, respectively. Both are concentrated in a small range of values. Two larger data can be seen on the axis of the figure, representing the two LOF defects mentioned above. Most of the projected area on the XY plane is within 2500μm 2 The defect volume is less than 50000μm3 . Figure 6 The four statistical data in the figure all show that the additive defects of the leading edge simulated blade are mainly pores, with small defect sizes and regular shapes.

[0047] The fatigue cracks and defects of the leading edge simulated blade are distributed as follows: Figure 7 As shown in the figure, the fatigue cracks of the simulated blade specimens all pass through the entire leading edge area of ​​the specimen and extend inward. There are also specimens with fatigue cracks in the inner area of ​​the leading edge that do not extend to the outside of the leading edge. This shows that the fatigue cracks initiate inside the specimen, rather than at the maximum stress point of the leading edge, because the additive defects lead to greater stress concentration. Figure 7 It can be seen that at the boundary of the additive area, there are a large number of defect groups consisting of pores, which is the same as the CT results of the samples without fatigue testing.

[0048] The high cycle fatigue test results of the leading edge simulated blade are summarized in Table 1. It can be seen that the average high cycle fatigue strength of the forged material leading edge simulated blade under stress ratio R = -1 and single-stage cycle number 3×107 is 567.13MPa, and the fatigue strength of the simulated blade after additive repair is significantly lower than that of the base material. The fatigue strength of the simulated blade repaired with a 3mm deep semicircular notch is in the range of 180 to 360MPa, and the results are highly dispersed, most of which are above 260MPa, with an average of 268.90MPa. The fatigue strength of this type of repaired specimen is about 52.4% lower than that of the simulated blade of forged material. The fatigue strength of the simulated blade repaired with a large-size notch (3mm deep semicircle) is almost all above 200MPa, which can meet the requirements for safe use. The high cycle fatigue strength of the simulated blade with a large-size notch additive repair (average 270.67MPa) is 113.5% higher than that of the simulated blade with foreign object loss (FOD) of the skewed notch (average 126.59MPa), and the repair effect is significant. It can be seen that additive repair is very necessary and effective for the continued use of FOD blades. For large-sized notched blades after FOD, the remaining fatigue strength does not meet the safe use requirements stipulated by the fatigue strength reserve, and can only be scrapped as required. After additive repair, the fatigue strength can be restored to a certain extent, so that it meets the safe use requirements of the fatigue strength reserve, greatly saving blade resources.

Claims

1. A fatigue failure analysis method for blades repaired by additive manufacturing based on CT scanning images, characterized in that: The following steps are involved: Step 1: Use CT technology combined with an optical microscope to perform defect testing and analysis on the additively repaired titanium alloy simulated blade, and conduct defect characterization testing on the additively repaired area of ​​the blade on an inverted optical microscope; Step 2: Analysis of defect cross-section and global test of titanium alloy simulated blade additive, obtaining the projection cross-section of the specimen in three directions and the defect distribution of the three-dimensional view; Step 3: Statistical distribution of defect parameters of the simulated blade with additively repaired titanium alloy leading edge, including equivalent spherical diameter distribution, sphericity distribution, horizontal cross-sectional projection area distribution, and volume distribution; Step 4: Observe the fatigue crack propagation of the additively repaired titanium alloy simulated blade by CT nondestructive testing, and observe and analyze the high-cycle fatigue fracture of the additively repaired TC17 titanium alloy tensile fatigue specimen by SEM to explore its defect-related fatigue source and fatigue crack propagation behavior; Step 5: Establish the correlation between the defect characteristics and fatigue performance of the additively repaired titanium alloy simulated blade, simulate the fatigue crack propagation behavior of the additively repaired blade through the defect-dependent high-cycle fatigue life model, and form a fatigue failure analysis method for additively repaired blades based on CT scanning images.

2. The fatigue failure analysis method of blades repaired by additive manufacturing based on CT scanning images according to claim 1 is characterized in that: High-resolution CT was used to carry out global defect characterization test on the additively repaired TC17 titanium alloy leading edge simulated blade on the Phoenix v|tome|x M CT digital tomography system; the high-resolution CT test parameters are as follows: photon energy 60keV, voltage 125kV, current 105μA, exposure time 500ms, CT voxel size 6.47μm, focal size 13.125μm, filter selected 0.4mm copper sheet, imaging range 20mm, imaging interval 5.8μm.

3. The fatigue failure analysis method of blades repaired by additive manufacturing based on CT scanning images according to claim 2 is characterized in that: The probability distribution of defect size, defect location and defect morphology in the repaired area of ​​the blade in the statistical additive repair simulation is as follows; additive defects are not perfect spheres. Most pore defects are quasi-spherical or quasi-ellipsoidal, while fatal unmelted defects have different shapes. The measurement of defect shape can be expressed by the parameter of sphericity. The calculation formula of defect sphericity is as follows: In the formula, C d is the sphericity, V is the defect volume, S is the defect surface area, the equivalent sphere diameter expresses the actual size of the defect, and the circumscribed sphere diameter expresses the spherical range volume occupied by the defect in space. At the same time, the defect position will also affect the high-cycle fatigue failure mechanism of the additively repaired blade, and the defect position feature D needs to be introduced. L , where the distance D between the defect and the sample surface needs to be measured d , D f is the sample thickness, D L After normalization, the fatal defect position is expressed as follows: D L is the normalized defect position, D d When D is 0, L 1 means the defect is on the surface of the sample, D L When it is 0.5, it means that the defect is in the center of the sample. Similarly, the internal defects of the sample are accurately characterized in three dimensions by X-CT, and the ellipsoid is equivalent to the square root of the projected area. As the defect characteristic size; Its fatigue dispersion and size effect can be evaluated by statistical methods.

4. The fatigue failure analysis method of blades repaired by additive manufacturing based on CT scanning images according to claim 3 is characterized in that: The fatigue crack propagation of the simulated blade of the additively repaired titanium alloy was observed by CT non-destructive testing, and the fatigue source and fatigue crack propagation behavior related to the defect were studied. First, the fatigue source was found by fracture analysis, and the high-cycle fatigue fracture of the additively repaired TC17 titanium alloy tensile fatigue specimen was observed and analyzed by SEM to confirm the cracking mode of the specimen and explore the fatigue initiation and propagation mechanism after additive repair. The dimple size on the fatigue fracture SEM image was quantitatively analyzed using the image analysis software Image Pro Plus. Before the test, the fractured part of the specimen was cut off by wire cutting, and the specimen was naturally split into two pieces from the cross section. One of the pieces was randomly selected to clean the specimen with anhydrous ethanol in an ultrasonic cleaner, and the fatigue fracture was observed using SEM after drying. Then, CT scanning was used to detect defects, and the defect size probability distribution, defect position probability distribution and defect morphology probability distribution of the repaired area of ​​the additively repaired simulation component were obtained by statistically analyzing the distribution of defect parameters of the additively repaired simulation component, and the defect parameters of the additively repaired simulation component included defect diameter, defect sphericity, defect volume and the projection area of ​​the defect on the XY plane.

5. The fatigue failure analysis method of blades repaired by additive manufacturing based on CT scanning images according to claim 4 is characterized in that: Establish defect-dependent high cycle fatigue life model: Where σ max is the maximum applied stress, Y is the shape factor, in the three-dimensional case, Y = 1, in the two-dimensional case, Y = 0.9; α, β, μ are material parameters related to the sphericity, position, and size of the defect, respectively, α′, β′ are fitting parameters, which can be fitted from the defect-fatigue life curve; A high-cycle fatigue life prediction model for additively repaired blades was established, and a fatigue failure analysis method for additively repaired blades based on CT scanning images was formed.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to claim 1 to claim 5 are implemented.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to claim 1 to claim 5 are implemented.

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