Blade air film hole cavity injury risk prediction method based on CT (Computed Tomography)

Abstract

The invention belongs to the field of workpiece processing, and relates to a CT-based blade air film hole cavity injury risk prediction method, which comprises the following steps of: 1) obtaining a CT scanning model of a blade, a theoretical model of the blade and an air film hole positioning result; 2) carrying out global consistency optimal registration on the CT scanning model of the blade obtained in the step 1) and the theoretical model of the blade; (3) detecting and segmenting an actual cavity model and features of the CT scanning model of the blade based on a result of the step (2); and 4) predicting the damage risk of the processing side wall of the film hole based on the result obtained in the step 3). According to the CT-based blade air film hole cavity injury risk prediction method provided by the invention, the blade machining quality can be improved, and the blade machining yield can be greatly improved.

Description

Technical Field

[0001] The present invention belongs to the field of workpiece processing, and relates to a method for predicting the machining quality of cavities, and particularly to a method for predicting the risk of impact damage to the channels of blade film cooling holes based on CT. Background Art

[0002] Currently, during the positioning and machining of blade film cooling holes, the main method is to install the blade workpiece on a fixture, and establish the coordinates of the machine tool coordinate system of the blade by positioning the fixture. Assuming that the theoretical blade model and the actual blade model coordinate systems in the machine tool coordinate system coincide, corresponding machining is then carried out in the machine tool coordinate system. However, due to the differences between the real three-dimensional blade workpiece and the theoretical model, when assuming that the real blade model and the theoretical model coordinate systems in the machine tool coordinate system coincide, there will surely be deviations in position and direction during the positioning and machining of the film cooling holes in the machine tool coordinate system, thus affecting the machining quality problems of the inner side wall and the stem of the channel being damaged, resulting in the risks of workpiece rejection and low yield rate.

[0003] For example, in the invention application with the publication number 110640339A, a laser processing technology for special-shaped film cooling holes of a turbine blade is disclosed, including the import and slicing processing of the CAD model of the film cooling hole; wall protection filling and blade clamping; recognition / matching automatic positioning and reference correction of the MARK points on the blade surface; laser processing and on-line monitoring; post-processing; industrial CT multi-angle flaw detection and inspection. This processing technology can achieve the precision machining of complex special-shaped film cooling holes, and further improve the surface integrity of the film cooling hole machining, which is of great significance for improving the quality of turbine engines / gas turbines, and provides technical support for the development of new-generation turbine-type aero-engines and ship gas turbines. However, this film cooling hole processing method relies on process experience and cannot achieve 100% detection of the risk of impact damage to the hole position. Summary of the Invention

[0004] In order to solve the above technical problems in the background art, the present invention provides a method for predicting the risk of impact damage to the channels of blade film cooling holes based on CT, which can improve the quality of blade machining and greatly increase the yield rate of blade machining.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for predicting the risk of impact damage to the channels of blade film cooling holes based on CT, characterized in that: the method for predicting the risk of impact damage to the channels of blade film cooling holes based on CT includes the following steps:

[0007] 1) Obtain the CT scan model of the blade, the theoretical model of the blade, and the film cooling hole positioning result:

[0008] 2) Perform global best registration of the CT scan model of the blade obtained in step 1) with the theoretical model of the blade;

[0009] 3) Detect and segment the CT scan model of the blade based on the registration result of step 2) to obtain the actual cavity model of the CT scan model;

[0010] 4) Predict the risk of damage to the side wall of the film cooling hole machining based on the actual cavity model of the CT scan model of the blade obtained in step 3).

[0011] The CT scan model of the blade in the above step 1) is an STL model collected by a high-precision CT device, which contains all the information of the outer wall and inner wall of the blade; the CT scan model of the blade includes the complete outer wall scan model of the blade, the complete inner wall scan model, the blade cavity scan model, the blade body scan model, the blade rib scan model, the blade rib scan model, and the blade side wall scan model; the theoretical model of the blade includes the complete outer wall theoretical model of the blade, the complete inner wall theoretical model, the blade cavity theoretical model, the blade body theoretical model, the blade rib theoretical model, the blade rib theoretical model, and the blade side wall theoretical model; the theoretical model of the blade is an IGS model;

[0012] The film cooling hole positioning result is the position and axial information of the theoretical film cooling hole in the machine tool processing space coordinate system according to the production process.

[0013] The specific implementation method of the above step 2) is:

[0014] 2.1) Register the blade body scan model and the blade body theoretical model;

[0015] 2.2) Register the blade base based on the registration result of step 2.1);

[0016] 2.3) Perform overall best registration of the point clouds of the blade body part and the base part based on the registration result of step 2.2).

[0017] The specific implementation method of the above step 2.1) is: According to the height dimension H_top of the blade body, intercept the blade body parts of the blade body theoretical model and the blade body scan model, perform ICP registration based on multi-angle initial values and multi-scale point clouds, sort the registration results at different angles and different scales according to the registration accuracy error value from small to large, and take the registration result with the smallest error value as the optimal registration result M1.

[0018] The specific implementation method of the above step 2.2) is as follows: According to the size H_bottom of the blade base, intercept the base parts of the blade scanning model and the blade theoretical model. Using the registration result M1 of step 2.1) as the initial value, perform registration on the blade base. Sort the registration results at different angles and scales in ascending order according to the registration accuracy error value, and use the registration result with the smallest error value as the registration result M2.

[0019] The specific implementation method of the above step 2.3) is as follows: Intercept the scanning model including the blade body and the base and the theoretical model including the blade body and the base. Using the registration result M2 of step 2.2) as the initial value, calculate the globally consistent optimal matching result to obtain the registration result M3.

[0020] The specific implementation method of the above step 3) is as follows:

[0021] 3.1) Using the registration result M3 obtained in step 2.3) as the initial value, perform ICP registration on the blade channel theoretical model and the blade channel scanning model to obtain the registration result Q1;

[0022] 3.2) According to the registration result Q1 obtained in step 3.1), map the blade channel theoretical model S_theo1 to the blade channel scanning model coordinate system S_theo1_scan, and use the nearest neighbor search method to find the corresponding channel model S_scan1 of the scanning model;

[0023] 3.3) Repeat step 3.1) and step 3.2) until each channel theoretical model is processed, and successively obtain the actual channel models S_scan1, S_scan2,..., S_scan’n including ribs and flanges.

[0024] The specific implementation method of the above step 4) is as follows:

[0025] 4.1) Using the method of fixture positioning, convert the scanning models of the channels, ribs, and flanges of the blade workpiece to the machine tool processing coordinate system;

[0026] 4.2) Using the air film hole positioning result obtained in step 1) and the actual channel models S_scan1, S_scan2,..., S_scan’n including ribs and flanges obtained in step 3.3) to perform spatial topology calculation, and judge whether there are intersection and inclusion relationships. If there are, it proves that there is a risk of impact damage; if not, there is no risk of impact damage.

[0027] The specific judgment method in the above step 4.2) is that after the positioning of the film cooling holes is completed, the contour of the film cooling holes is discretized into a series of spatial straight lines according to the circumferential contour line and the axial direction of the film cooling holes. The spatial intersection calculation is carried out between each straight line and the models of ribs, stiffeners and side walls, and it is judged whether there is an intersection point respectively. If there is an intersection point, it proves that there is a corresponding risk of impact damage. If there is no intersection point, there is no corresponding risk of impact damage.

[0028] The accuracy of the CT equipment acquisition above is at the 10μm level.

[0029] The advantages of the present invention are as follows:

[0030] The present invention provides a method for predicting the risk of impact damage to the film cooling hole cavity of a blade based on CT, including: 1) obtaining the CT scan model of the blade, the theoretical model of the blade and the film cooling hole positioning result; 2) performing global best registration on the CT scan model of the blade obtained in step 1) and the theoretical model of the blade; 3) detecting and segmenting the CT scan model of the blade based on the registration result of step 2) to obtain the actual cavity model of the CT scan model; 4) predicting the risk of impact damage to the side wall of the film cooling hole during processing based on the actual cavity model of the CT scan model obtained in step 3). The present invention utilizes the blade point cloud information collected by CT, through precise registration of the outer wall and inner wall of the blade, arranges the processing hole position information on the actual model, predicts the risk of impact damage to the outer wall, inner wall, cavity and stem by the hole position information, and gives the best adjustment scheme for the position and direction of the film cooling holes for the hole positions with the risk of impact damage, solves the problem of predicting the risk of impact damage to the side wall, ribs and stiffeners of the film cooling holes, improves the quality of blade processing, and greatly improves the yield rate of blade processing. The present invention adopts the full model scan of CT, based on data drive, can take into account the measurement information of the outer wall and the inner cavity, and conduct scientific analysis and early warning of the risk of impact damage to the processing hole position. The present invention adopts the global best registration and local cavity precise registration methods, accurately identifies the spatial information of the actual cavity, and solves the positioning and identification problems caused by the deformation of the actual cavity; the present invention can simulate the risk of impact damage to the side wall, stem and ribs during the actual processing of the film cooling holes by calculating the actual processing hole position information and the actual workpiece cavity information. Description of the Drawings

[0031] Figure 1 is the theoretical model diagram of a certain engine blade;

[0032] Figure 2 is the blade scan model;

[0033] Figure 3 is the global registration result of the blade theoretical model and the scan model;

[0034] Figure 4 is the internal cavity model of the blade theoretical model;

[0035] Figure 5 It is the risk judgment of the blade rib and inner wall being damaged;

[0036] Figure 6 It is the risk judgment of the side wall of the blade film cooling hole being damaged;

[0037] Figure 7 It is the calculation trajectory of the risk of damage to the film cooling hole in the axial direction;

[0038] Figure 8 It is the flow chart of the method for predicting the risk of damage to the cavity of the blade film cooling hole based on CT provided by the present invention. Specific implementation mode

[0039] The purpose of the present invention is to provide the ability to detect the risk of damage to the inner cavity during the processing of the blade film cooling hole, aiming to solve the problems of possible damage to the inner cavity side wall and stem during the positioning and processing of the film cooling hole due to the deformation generated by the actual blade casting.

[0040] The present invention provides a method for predicting the risk of damage to the cavity of the blade film cooling hole based on CT, including:

[0041] 1) Obtain the CT scan model of the blade, the theoretical model of the blade, and the film cooling hole positioning result:

[0042] The CT scan model of the blade refers to the STL model containing all the information of the outer wall and inner wall of the blade collected by a high-precision CT device; exemplarily, the accuracy of the CT device can be 10μm level or higher, and the present invention does not make a limitation.

[0043] The theoretical model of the blade includes the complete outer wall and inner wall models of the blade, the blade cavity model, and the IGS models of the blade ribs, ribs, and side walls;

[0044] The film cooling hole positioning result refers to the position and axial information of the theoretical film cooling hole located in the machine tool processing space coordinate system according to the production process;

[0045] 2. Align the CT scan model of the blade obtained in step 1) with the theoretical model of the blade with the best global consistency;

[0046] 2.1) Align the scanned model of the blade body and the theoretical model of the blade body. Specifically: According to the height dimension H_top of the blade body ( Figure 1 and Figure 2 the red box part), intercept the blade body parts of the theoretical model and the scanned model of the blade, and then perform ICP alignment based on multi-angle initial values and multi-scale point clouds. Sort the alignment results at different angles and different scales according to the alignment accuracy error value from small to large, and take the alignment result with the smallest error value as the optimal alignment result M1;

[0047] 2.2) Perform registration on the blade base based on the registration result in step 2.1): Specifically, according to the size H_bottom of the blade base ( Figure 1 and Figure 2 the blue box part), intercept the base parts of the blade scan model and the theoretical model, and then use the registration result M1 in 2.1) as the initial value to perform fine registration of the blade base. Sort the registration results at different angles and scales in ascending order according to the registration accuracy error value, and use the registration result with the smallest error value as the registration result M2;

[0048] 2.3) Perform overall optimal registration on the point clouds of the blade body part and the base part: Specifically, intercept the scan model and the theoretical model including the blade body and the base, and use the registration result M2 in 2.2) as the initial value to calculate the globally consistent optimal matching result, and obtain the registration result M3 as the current calculation result;

[0049] 3. Based on the registration result in step 2), detect and segment the CT scan model of the blade to obtain the actual cavity model of the CT scan model, which specifically includes the following steps:

[0050] 3.1) Use the registration result M3 in step 2.3) as the initial value to perform ICP registration on the theoretical cavity model and the actual scan model to obtain the registration result Q1;

[0051] 3.2) Map the cavity theoretical model S_theo1 to the scan model coordinate system S_theo1_scan according to the registration result Q1, and then use the nearest neighbor search to find the corresponding cavity model S_scan1 of the scan model;

[0052] 3.3) Repeat step 3.1) and step 3.2) until each theoretical cavity model is processed, and successively obtain the respective actual cavity models S_scan1, S_scan2,..., S_scan’n including ribs and flanges;

[0053] 4. Predict the risk of damage to the side wall of the air film hole processing based on the actual cavity model of the CT scan model obtained in step 3), which specifically includes:

[0054] 4.1) Use the method of fixture positioning ( Figure 2 the standard block in) to convert the scan models of the cavities, ribs, and flanges of the blade workpiece to the machine tool processing coordinate system;

[0055] 4.2) Use the hole position and axial positioning results in step 1.3) and the actual rib, riblet, and sidewall information in step 3.3) to perform spatial topology calculations to determine whether there are intersection and inclusion relationships. If they exist, it proves that there is a risk of impact damage. Specifically, when making the determination, the method is as follows: After the air film hole positioning is completed, discretize the contour of the air film hole into a series of spatial lines according to the circumferential contour line and the axial direction of the air film hole axis. Perform spatial intersection calculations between each line and the models of ribs, riblets, and sidewalls, and respectively determine whether there are intersection points. If there are intersection points, it proves that there is a corresponding risk of impact damage. If there are no intersection points, there is no corresponding risk of impact damage.

Claims

1. A CT-based blade air film hole cavity injury risk prediction method, characterized by: The CT-based blade air film hole cavity injury risk prediction method comprises the following steps: 1) Obtain the CT scanning model of the blade, the theoretical model of the blade, and the film hole positioning results: 2) performing global consistency optimal registration of the CT scan model of the blade obtained in step 1) with the theoretical model of the blade; 3) Based on the registration result of step 2), the CT scan model of the blade is detected and segmented to obtain the actual cavity model of the CT scan model; 4) Based on the actual cavity model of the CT scanning model obtained in step 3), the risk of side wall damage during air film hole processing is predicted.

2. The CT-based blade air film hole cavity injury risk prediction method according to claim 1 is characterized by: The CT scanning model of the blade in step 1) is an STL model containing all information of the outer wall and inner wall of the blade collected by high-precision CT equipment; the CT scanning model of the blade includes a complete outer wall scanning model of the blade, a complete inner wall scanning model, a blade cavity scanning model, a blade body scanning model, a blade tendon scanning model, a blade rib scanning model and a blade side wall scanning model; the theoretical model of the blade includes a complete outer wall theoretical model of the blade, a complete inner wall theoretical model of the blade, a blade cavity theoretical model, a blade body theoretical model, a blade tendon theoretical model, a blade rib theoretical model and a blade side wall theoretical model; the theoretical model of the blade is an IGS model; The air film hole positioning result is the air film hole position and axial information of positioning the theoretical air film hole to the air film hole position in the machine tool processing space coordinate system according to the production process.

3. The CT-based blade air film hole cavity injury risk prediction method according to claim 2 is characterized by: The specific implementation method of step 2) is: 2.1) Align the blade body scanning model with the blade body theoretical model; 2.2) aligning the blade base based on the alignment result of step 2.1); 2.3) Based on the registration result of step 2.2), the point clouds of the blade body and base are optimally registered as a whole.

4. The CT-based blade air film hole cavity injury risk prediction method according to claim 3 is characterized by: The specific implementation method of step 2.1) is: according to the height size H_top of the blade body, the blade body part of the blade body theoretical model and the blade body scanning model is intercepted, and ICP alignment is performed based on multi-angle initial values ​​and multi-scale point clouds. The alignment results at different angles and different scales are sorted from small to large according to the alignment accuracy error value, and the alignment result with the smallest error value is taken as the optimal alignment result M1.

5. The CT-based blade air film hole cavity injury risk prediction method according to claim 4 is characterized by: The specific implementation method of step 2.2) is: according to the size H_bottom of the blade base, the base part of the blade scanning model and the blade theoretical model is intercepted, and the alignment result M1 of step 2.1) is used as the initial value to align the blade base, and the alignment results at different angles and scales are sorted from small to large according to the alignment accuracy error value, and the alignment result with the smallest error value is used as the alignment result M2.

6. The CT-based blade air film hole cavity injury risk prediction method according to claim 5 is characterized by: The specific implementation method of step 2.3) is: intercepting the scanning model including the blade body and base and the theoretical model including the blade body and base, using the alignment result M2 of step 2.2) as the initial value, calculating the global consistency optimal matching result, and obtaining the alignment result M3.

7. The CT-based blade air film hole cavity injury risk prediction method according to claim 6 is characterized by: The specific implementation method of step 3) is: 3.1) Using the registration result M3 obtained in step 2.3) as the initial value, ICP registration is performed on the blade cavity theoretical model and the blade cavity scanning model to obtain the registration result Q1; 3.2) According to the registration result Q1 obtained in step 3.1), the blade cavity theoretical model S_theo1 is mapped to the blade cavity scanning model coordinate system S_theo1_scan, and the cavity model S_scan1 of the corresponding scanning model is found by using the nearest neighbor search method; 3.3) Repeat steps 3.1) and 3.2) until each cavity theoretical model is processed, and actual cavity models S_scan1, S_scan2, ..., S_scan'n including ribs and tendons are obtained in turn.

8. The CT-based blade air film hole cavity injury risk prediction method according to claim 7 is characterized by: The specific implementation method of step 4) is: 4.1) Using the fixture positioning method, the scanning model of the cavity, ribs and ribs of the blade workpiece is converted to the machine tool processing coordinate system; 4.2) Using the air film hole positioning result obtained in step 1) and the actual cavity models S_scan1, S_scan2, ..., S_scan'n including tendons and ribs obtained in step 3.3), perform spatial topological calculation to determine whether there is an intersection and inclusion relationship. If so, it proves that there is a risk of injury; if not, there is no risk of injury.

9. The CT-based blade air film hole cavity injury risk prediction method according to claim 8, characterized in that: The specific judgment method in step 4.2) is that after the positioning of the air film hole is completed, the contour of the air film hole is discretized into a series of spatial straight lines according to the axial circular contour line and axial direction of the air film hole, and each straight line is spatially intersected with the models of the tendons, ribs and side walls to determine whether there are intersections. If there are intersections, it proves that there is a corresponding injury risk. If there are no intersections, there is no corresponding injury risk.

10. The CT-based blade air film hole cavity injury risk prediction method according to any one of claims 2 to 9, characterized in that: The accuracy of the CT device is at the 10 μm level.

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