A method for evaluating roughness of blade leading and trailing edges

Abstract

The application discloses a kind of roughness evaluation methods of blade leading and trailing edge, belong to compressor and turbine blade processing detection field.The application realizes method is: for the blade surface point cloud data obtained by three-coordinate precision measuring machine data segmentation, alignment, the three-dimensional point cloud data set of blade leading and trailing edge is multiplied by rotation translation matrix and is converted into two-dimensional point cloud data set;Eliminate blade surface profile and obtain blade leading and trailing edge measurement data, and the signal enhancement processing of data characteristics is carried out;Carry out roughness algorithm evaluation and analysis of blade leading and trailing edge, and calculate to obtain Ra, Rz, Rsm and other roughness parameters.The blade leading and trailing edge geometry is approximately a>2b Half elliptical shape.The application can realize roughness evaluation for the leading and trailing edge position of workpiece surface with blade complex curved surface characteristics, optimize blade, improve blade trial and processing technology.The application has the characteristics of high reliability, strong operability, high reliability of detection result, small measurement error and the like.

Description

Technical Field

[0001] This invention belongs to the field of compressor and turbine blade processing and inspection, and relates to a method for evaluating the roughness of the leading and trailing edges of blades. Background Technology

[0002] Blade surface quality parameters account for a significant proportion of the many factors influencing compressor and turbine performance, with surface roughness being a key characteristic parameter. The leading and trailing edges of blades have unique geometries, making it impossible to evaluate their roughness parameters using traditional roughness assessment methods based on standards such as ISO 4287-2009. Furthermore, HB 5647-48 does not explicitly specify blade roughness detection methods, and currently, there is no effective method for measuring and evaluating the roughness of blades with such unique shapes. For surface profiles with excessive curvature, contact roughness measurement equipment cannot measure or evaluate them. Non-contact roughness measurement equipment can measure surface profiles with large curvature, but a mature roughness evaluation method is lacking. If the surface quality parameters of the blade's leading and trailing edges exceed tolerances, and there are no clear detection methods or accurate evaluation methods, it is impossible to determine roughness compliance and to identify the direction of process and design iterations. In mass production of blades, experience and visual inspection are often used to detect the roughness of the blade's leading and trailing edges, lacking accurate evaluation methods. Summary of the Invention

[0003] The main objective of this invention is to provide a method for evaluating the surface roughness of the leading and trailing edges of blades. This method enables the assessment of surface roughness at the leading and trailing edges of workpieces with complex curved surface features, thereby optimizing blades and improving blade prototyping and processing techniques. This invention features high reliability, strong operability, high credibility of test results, and small measurement errors.

[0004] The objective of this invention is achieved through the following technical solution.

[0005] This invention discloses a method for evaluating the roughness of the leading and trailing edges of a blade, comprising the following steps:

[0006] Step 1: Measure the collected blade profile point cloud C i (x ci y ci , z ci Data segmentation was performed to obtain the leading and trailing edge measurement data C of the blade. 1i (x ci y ci , z ci );

[0007] Step 2: Use the rotation and translation matrix [RT] to process the blade leading and trailing edge measurement data C. 1i (x ci yci , z ci Transform the data to a specific spatial coordinate plane to obtain aligned point cloud data, denoted as D. i (x di y di ,1); simplified to D i (x di y di );

[0008] Step 3: Obtain dataset D from Step 2 i (x di y di The shapes of the leading and trailing edges of the blades were removed to obtain the dataset PD. i (x ei y ei );

[0009] Step 4: Process the dataset PD i (x ei y ei Data signal enhancement is performed to obtain the enhanced dataset RD. i (x ri y ri );

[0010] Step 5: Apply roughness standard specifications to the RD dataset. i (x ri y ri The roughness parameters are evaluated to obtain the roughness parameters of the leading and trailing edges of the blade, thus realizing the surface roughness evaluation of the leading and trailing edges of the blade's complex curved surface. These parameters include the arithmetic mean deviation Ra of the profile, the maximum height Rz of the profile, and the average width Rsm of the profile element.

[0011] Furthermore, the geometric shape of the leading and trailing edges of the blade is approximately a semi-elliptical shape with a > 2b.

[0012] Furthermore, the data segmentation method described in step 1 involves connecting the longest chord of the blade profile and extracting 4% from each end of the longest chord as the leading and trailing edges of the blade.

[0013] Furthermore, the method of aligning using the rotation and translation matrix [RT] in step 2 is as follows: Let the Z-direction normal vector k in C1 be... c =1. That is

[0014] D[x di y di ,1,1] T =[RT]·C1(x ci y ci , z ci ,1) T

[0015]

[0016] Where R is the rotation matrix, T is the translation matrix, and θ is the rotation angle.

[0017] Furthermore, in step 3, the B-spline curve f(x, y) is fitted using theoretical values ​​of the leading and trailing edges of the blade. The specific implementation method is as follows:

[0018] For discrete point data D at the leading and trailing edges i (x di y di Calculate the chord length at each point, and add control points V0, V1, V2, ..., V n Perform N B-spline interpolations, where N = 3, then we have:

[0019] x i =(1 / 6)[t 3 V i+2 (x)+(-3t 3 +3t 2 +3t+1)V i+1 (x)+(3t 3 -6t 2 +4)V i (x)+(-t 3 +3t 2 -3t+1)V i-1 (x)

[0020] y i =(1 / 6)[t 3 V i+2 (y)+(-3t 3 +3t 2 +3t+1)V i+1 (y)+(3t 3 -6t 2 +4)V i (y)+(-t 3 +3t 2 -3t+1)V i-1 (y)

[0021] Where V i (x), V i (y) is the x of the point i y i Coordinates, where t is the arc length parameter.

[0022] Then, the deviation relative to the fitted curve is calculated, where, the deviation PD relative to the fitted curve is then calculated. i (x ei y ei ) = D i (xdi y di )-f(x,y), to get xx ei To fix the sampling step size (0.5μm or 0.25μm), y di This represents the error value. Therefore, the discrete dataset PD i (x ei y ei It can be abbreviated as D(n);

[0023] Furthermore, in step 4, a transformation function is designed and a discrete dataset D(n) is subjected to a convolution operation to obtain the enhanced dataset RD(n).

[0024]

[0025] Furthermore, in step 5, λ is selected using the relationship between Rsm and the roughness evaluation length. c Contour filter and λ s Contour filter, with λ c The surface profile obtained by filtering is the roughness evaluation centerline.

[0026] Beneficial effects:

[0027] 1. This invention discloses a method for evaluating the surface roughness of a blade's leading and trailing edges. The method involves segmenting and aligning the blade profile point cloud data obtained from a coordinate measuring machine (CMM). The three-dimensional point cloud dataset of the blade's leading and trailing edges is multiplied by a rotation and translation matrix to transform it into a two-dimensional point cloud dataset. Next, the blade profile contour is removed to obtain the measured data of the blade's leading and trailing edges. Signal enhancement processing is then performed on this data feature set. Finally, an algorithmic evaluation and analysis of the blade's leading and trailing edge roughness is conducted to calculate roughness parameters such as Ra, Rz, and Rsm. This enables the evaluation of surface roughness parameters for workpieces with special complex curved surfaces.

[0028] 2. This invention discloses a method for evaluating the roughness of the leading and trailing edges of a blade. Currently, contact roughness measuring devices cannot measure and evaluate surface profiles with excessive curvature. Non-contact roughness measuring devices can measure surface profiles with large curvature, but there is no mature roughness evaluation method. If the surface quality parameters of the leading and trailing edges of a blade exceed the tolerance, and there is no clear detection method or accurate evaluation method, this invention can realize the roughness evaluation of special blade profiles, filling the gap in the evaluation method of surface roughness at the leading and trailing edges of special geometric features of blades.

[0029] 3. This invention discloses a method for evaluating the roughness of the leading and trailing edges of blades. Currently, in the mass production of blades, the roughness of the leading and trailing edges is often detected by experience and visual inspection, which lacks an accurate evaluation method. This invention provides a roughness evaluation method that is highly reliable, easy to operate, has high credibility of the test results, and has small measurement errors. This method can guide, optimize, and improve blade design and promote the iterative update of blade trial production and processing technology. Attached Figure Description

[0030] Figure 1 Flowchart of a method for evaluating the roughness of the leading and trailing edges of a blade;

[0031] Figure 2 : Schematic diagram of the leading and trailing edges of the blade;

[0032] Figure 3 : Measured point cloud data of the blade. Detailed implementation method:

[0033] The technical solution of the present invention will now be described in conjunction with the accompanying drawings.

[0034] To better illustrate the purpose and advantages of the present invention, the invention will be further described below in conjunction with the accompanying drawings and examples.

[0035] Blade profile measurement point cloud C i (x ci y ci , z ci Data acquired by a coordinate measuring machine, such as... Figure 1 As shown in the figure, the specific implementation steps of the blade leading and trailing edge roughness evaluation method disclosed in this embodiment are as follows:

[0036] Step 1: Measure the collected blade profile point cloud C i (x ci y ci , z ci Data segmentation was performed to obtain the leading and trailing edge measurement data C of the blade. 1i (x ci y ci , z ci );

[0037] Among them, the longest chord of the blade profile is connected, and 4% of each end of the longest chord is taken as the leading and trailing edges of the blade to complete the data segmentation.

[0038] Step 2: Use the rotation and translation matrix [RT] to transfer the blade leading and trailing edge measurement data C 1i (x ci y ci , z ci Transform the data to a specific spatial coordinate plane to obtain aligned point cloud data, denoted as D.i (x di y di ,1); can be simplified to D i (x di y di ),in

[0039] The method of alignment using the rotation and translation matrix [RT] is as follows: Let the Z-direction normal vector k in C1 be... c =1. That is

[0040] D[x di ,y di ,1,1] T =[RT]·C1(x ci ,y ci ,z ci ,1) T

[0041]

[0042] Where R is the rotation matrix, T is the translation matrix, and θ is the rotation angle.

[0043] Step 3: Considering the special geometry (large curvature) of the leading and trailing edges of the blade, this surface profile has a significant impact on the accuracy of the roughness evaluation results. Using dataset D... i (x di y di Since roughness parameters cannot be directly evaluated, the shapes of the leading and trailing edges of the blade are removed to obtain the dataset PD. i (x ei y ei The specific implementation method is as follows:

[0044] Using theoretical values ​​of the leading and trailing edges of the blade, a B-spline curve f(x, y) is fitted, targeting discrete point data D at the leading and trailing edges. i (x di y di Calculate the chord length at each point, and add control points V0, V1, V2, ..., V n Perform N B-spline interpolations, where N = 3, then we have:

[0045] x i =(1 / 6)[t 3 V i+2 (x)+(-3t 3 +3t 2 +3t+1)V i+1 (x)+(3t 3 -6t 2 +4)V i (x)+(-t 3 +3t2 -3t+1)V i-1 (x)

[0046] y i =(1 / 6)[t 3 V i+2 (y)+(-3t 3 +3t 2 +3t+1)V i+1 (y)+(3t 3 -6t 2 +4)V i (y)+(-t 3 +3t 2 -3t+1)V i-1 (y)

[0047] Where V i (x), V i (y) is the x of the point i y i Coordinates, where t is the arc length parameter.

[0048] Then, the deviation relative to the fitted curve is calculated, where, the deviation PD relative to the fitted curve is then calculated. i (x ei y ei ) = D i (x di y di )-f(x,y), to get xx ei To fix the sampling step size (0.5μm or 0.25μm), y di This represents the error value. Therefore, the discrete dataset PD i (x ei y ei It can be abbreviated as D(n);

[0049] Step 4: Considering the influence of measurement methods during point cloud data acquisition, the measured point cloud data is distorted in reflecting the true surface contour of the workpiece. Therefore, for the PD dataset... i (x ei y ei Data signal enhancement is performed to obtain the enhanced dataset RD. i (x ri y ri );

[0050] Design a transformation function and a discrete dataset D(n); perform a convolution operation to obtain the enhanced dataset RD(n).

[0051]

[0052] Step 5: Apply roughness standard specifications to the RD dataset. i (x ri y ri Roughness parameters are evaluated. The roughness parameters of the leading and trailing edges of the blade are obtained; these parameters include Ra (arithmetic mean deviation of the profile), Rz (maximum height of the profile), and Rsm (average width of the profile element).

[0053] Based on relevant surface roughness standards and specifications, λ is selected using the relationship between Rsm and the surface roughness evaluation length. c Contour filter and λ s Contour filter, with λ c The surface profile obtained by filtering is the roughness evaluation centerline.

[0054] The aforementioned method for evaluating the roughness of the leading and trailing edges of blades is applied to assess the roughness of the leading and trailing edges of special profiles of blades with complex curved surface features. This method meets the technical requirements for surface roughness detection at the leading and trailing edges of blades with special geometric features and solves related engineering problems.

[0055] The above detailed description further illustrates the purpose, technical solution, and beneficial effects of the invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for evaluating the roughness of the leading and trailing edges of a blade, characterized in that: Includes the following steps, Step 1: Measure the collected blade profile point cloud C i (x ci y ci , z ci Data segmentation was performed to obtain the leading and trailing edge measurement data C of the blade. 1i (x ci y ci , z ci ); Step 2: Use the rotation and translation matrix [RT] to process the blade leading and trailing edge measurement data C. 1i (x ci y ci , z ci Transform the data to a specific spatial coordinate plane to obtain aligned point cloud data, denoted as D. i (x di y di ,1); simplified to D i (x di y di ); Step 3: Obtain dataset D from Step 2 i (x di y di The shapes of the leading and trailing edges of the blades were removed to obtain the dataset PD. i (x ei y e i); Step 4: Process the dataset PD i (x ei y ei Data signal enhancement is performed to obtain the enhanced dataset RD. i (x ri y ri ); Step 5: Apply roughness standard specifications to the RD dataset. i (x ri y ri The roughness parameters are evaluated to obtain the roughness parameters of the leading and trailing edges of the blade, thus realizing the surface roughness evaluation of the leading and trailing edge positions of the blade's complex curved surface feature workpiece. The parameters include the arithmetic mean deviation Ra of the profile, the maximum height Rz of the profile, and the average width Rsm of the profile element.

2. The blade leading and trailing edge roughness evaluation method as described in claim 1, characterized in that: The leading and trailing edges of the blade are approximately semi-elliptical in shape with a > 2b.

3. The blade leading and trailing edge roughness evaluation method as described in claim 1, characterized in that: The data segmentation method in step 1 is as follows: connect the longest chord of the blade profile, and cut off 4% of each end of the longest chord as the leading and trailing edges of the blade.

4. The blade leading and trailing edge roughness evaluation method as described in claim 1, characterized in that: The method of alignment using the rotation and translation matrix [RT] in step 2 is as follows: Let the Z-direction normal vector k in C1 be... c =1; that is D[x di ,y di ,1,1] T =[R T]·C1(x ci ,y ci ,z ci ,1) T Where R is the rotation matrix, T is the translation matrix, and θ is the rotation angle.

5. The blade leading and trailing edge roughness evaluation method as described in claim 1, characterized in that: In step 3, the B-spline curve f(x, y) is fitted using the theoretical values ​​of the leading and trailing edges of the blade; the specific implementation method is as follows. For discrete point data D at the leading and trailing edges i (x di y di Calculate the chord length at each point, and add control points V0, V1, V2, ..., V n Perform N B-spline interpolations, where N = 3, then we have: x i =(1 / 6)[t 3 V i+2 (x)+(-3t 3 +3t 2 +3t+1)V i+1 (x)+(3t 3 -6t 2 +4)V i (x)+(-t 3 +3t 2 -3t+1)V i-1 (x) y i =(1 / 6)[t 3 V i+2 (y)+(-3t 3 +3t 2 +3t+1)V i+1 (y)+(3t 3 -6t 2 +4)V i (y)+(-t 3 +3t 2 -3t+1)V i-1 (y) where V i (x), V i (y) is the x of the point i y i Coordinates, where t is the arc length parameter; Then, the deviation relative to the fitted curve is calculated, where, the deviation PD relative to the fitted curve is then calculated. i (x ei y ei ) = D i (x di y di )-f(x,y), to get xx ei To fix the sampling step size (0.5μm or 0.25μm), y di This is the error value; therefore, the discrete dataset PD i (x ei y ei ) is abbreviated as D(n).

6. The blade leading and trailing edge roughness evaluation method as described in claim 1, characterized in that: In step 4, a transformation function is designed and a discrete dataset D(n) is processed; a convolution operation is performed to obtain the enhanced dataset RD(n).

7. The blade leading and trailing edge roughness evaluation method as described in claim 1, characterized in that: In step 5, λ is selected using the relationship between Rsm and the roughness evaluation length. c Contour filter and λ s Contour filter, with λ c The surface profile obtained by filtering is the roughness evaluation centerline.

Images