CN116777831A - Blade front and rear edge roughness evaluation method - Google Patents
Blade front and rear edge roughness evaluation method Download PDFInfo
- Publication number
- CN116777831A CN116777831A CN202310348666.5A CN202310348666A CN116777831A CN 116777831 A CN116777831 A CN 116777831A CN 202310348666 A CN202310348666 A CN 202310348666A CN 116777831 A CN116777831 A CN 116777831A
- Authority
- CN
- China
- Prior art keywords
- blade
- roughness
- edge
- rear edge
- data set
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/20—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring contours or curvatures, e.g. determining profile
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B21/00—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant
- G01B21/30—Measuring arrangements or details thereof, where the measuring technique is not covered by the other groups of this subclass, unspecified or not relevant for measuring roughness or irregularity of surfaces
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/16—Matrix or vector computation, e.g. matrix-matrix or matrix-vector multiplication, matrix factorization
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/12—Edge-based segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30164—Workpiece; Machine component
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Pure & Applied Mathematics (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Data Mining & Analysis (AREA)
- Computing Systems (AREA)
- Geometry (AREA)
- Quality & Reliability (AREA)
- Algebra (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- General Engineering & Computer Science (AREA)
- Length Measuring Devices With Unspecified Measuring Means (AREA)
Abstract
The invention discloses a method for evaluating the roughness of the front and rear edges of a blade, and belongs to the field of processing and detecting of blades of compressors and turbines. The implementation method of the invention comprises the following steps: carrying out data segmentation and alignment on the blade type surface point cloud data obtained by the three-coordinate precision measuring machine, and multiplying the three-dimensional point cloud data set of the front edge and the rear edge of the blade by a rotation translation matrix to convert the three-dimensional point cloud data set into a two-dimensional point cloud data set; removing profile contours of the blades to obtain front and rear edge measurement data of the blades, and performing signal enhancement processing on the data characteristics; and (5) carrying out evaluation analysis on the roughness algorithm of the front edge and the rear edge of the blade, and calculating to obtain Ra, rz, rsm and other roughness parameters. The blade leading and trailing edge geometry approximates a >2b semi-elliptical shape. The invention can evaluate the surface roughness of the front and rear edge positions of the workpiece profile with the complex curved surface characteristics of the blade, optimize the blade and improve the blade trial production and processing technology. The invention has the characteristics of high reliability, strong operability, high reliability of detection results, small measurement error and the like.
Description
Technical Field
The invention belongs to the field of processing and detecting of blades of compressors and turbines, and relates to a method for evaluating roughness of front and rear edges of blades.
Background
The blade surface quality parameters occupy a large specific gravity among a plurality of influencing factors of compressor and turbine performances, wherein the surface roughness is a key characteristic parameter of the blade surface quality. The front edge and the rear edge of the blade are special in geometric shape, the front edge and the rear edge of the blade cannot be evaluated by utilizing the traditional roughness evaluation method according to related standard specifications such as ISO4287-2009, the detection mode of the blade roughness is not explicitly pointed out in HB 5647-48, and no effective roughness measurement and evaluation method for evaluating the special shape of the front edge and the rear edge of the blade is generated at present. The contact type roughness measuring equipment cannot measure and evaluate the surface profile with the overlarge curvature, and the non-contact type roughness measuring equipment can measure the surface profile with the larger curvature, but no mature roughness evaluating method exists. If the surface quality parameters of the front edge and the rear edge of the blade are out of tolerance, and no explicit detection means and no accurate evaluation method exist, the roughness qualification judgment, the process determination and the design iteration direction cannot be carried out; in the mass production of the blades, the roughness of the front and rear edges of the blades is usually detected by means of experience, visual inspection and the like, and an accurate evaluation method for the roughness of the front and rear edges of the blades is lacking.
Disclosure of Invention
The invention mainly aims to provide a method for evaluating the roughness of the front edge and the rear edge of a blade, which can evaluate the surface roughness of the front edge and the rear edge of a workpiece profile with complex curved surface characteristics of the blade, optimize the blade and improve the blade trial production and the processing technology. The invention has the characteristics of high reliability, strong operability, high reliability of detection results, small measurement error and the like.
The aim of the invention is achieved by the following technical scheme.
The invention discloses a method for evaluating the roughness of the front edge and the rear edge of a blade, which comprises the following steps:
step 1: measuring point cloud C of acquired blade profile i (x ci ，y ci ，z ci ) Data segmentation is carried out to obtain blade front and rear edge measurement data C 1i (x ci ，y ci ，z ci )；
Step 2: using a rotational translation matrix [ R T ]]Measuring data C of the front edge and the rear edge of the blade 1i (x ci ，y ci ，z ci ) Converting into a certain space coordinate plane to obtain aligned point cloud data, and recording the aligned point cloud data as D i (x di ，y di 1) a step of; simplified to D i (x di ，y di )；
Step 3: step 2 obtaining data set D i (x di ，y di ) Removing the shape of the front and rear edges of the blade to obtain a data set PD i (x ei ，y ei )；
Step 4: for data set PD i (x ei ，y ei ) Enhancing the data signal to obtain an enhanced data set RD i (x ri ，y ri )；
Step 5: for data sets RD in accordance with roughness standard specifications i (x ri ，y ri ) And (5) evaluating the roughness parameters, namely, evaluating the surface roughness of the front and rear edges of the complex curved surface feature workpiece molded surface of the blade. The parameters include the arithmetic mean deviation Ra of the profile, the maximum height Rz of the profile and the mean width Rsm of the profile elements.
Further, the blade leading and trailing edge geometry approximates a >2b semi-elliptical shape.
Further, in the data segmentation method in the step 1, the longest chord of the profile of the blade is connected, and 4% of each of the two ends of the longest chord is cut and taken as the front edge and the rear edge of the blade.
Further, the step 2 utilizes a rotational translation matrix [ R T ]]The alignment method comprises the following steps: let C 1 Normal vector k in Z direction c =1. I.e.
D[x di ，y di ，1，1] T ＝[R T]·C 1 (x ci ，y ci ，z ci ，1) T
Wherein R is a rotation matrix, T is a translation matrix, and θ is a rotation angle.
Further, in the step 3, a B-spline curve f (x, y) is fitted by using theoretical values of the front and rear edges of the blade. The specific implementation method is as follows,
for the front and rear edge discrete point data D i (x di ，y di ) Calculating the chord length of each point, and adding a control point V 0 ，V 1 ，V 2 ，...，V n Performing B spline interpolation N times, where let n=3, and then:
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)
wherein V is i (x)，V i (y) is the x of the dot i ，y i Coordinates, t, are arc length parameters.
Further solving for the deviation from the fitted curve, wherein further solving for the deviation PD from the fitted curve i (x ei ，y ei )＝D i (x di ，y di ) -f (x, y) to obtain xx ei For a fixed sampling step (0.5 μm or 0.25 μm), y di Is the error value. Thus discrete data set PD i (x ei ，y ei ) May be abbreviated as D (n);
further, in the step 4, the conversion function and the discrete data set D (n) are designed; a convolution operation is performed to obtain the enhanced data set RD (n).
Further, in the step 5, λ is selected by using a relationship between Rsm and a roughness evaluation length c Contour filter and lambda s Contour filter, lambda c The surface profile obtained by filtering is a roughness assessment center line.
The beneficial effects are that:
1. the invention discloses a method for evaluating the roughness of the front and rear edges of a blade, which is characterized in that data segmentation is carried out on blade surface point cloud data obtained by a three-coordinate precision measuring machine, the data are aligned, a three-dimensional point cloud data set of the front and rear edges of the blade is multiplied by a rotation translation matrix to be converted into a two-dimensional point cloud data set, the profile of the blade is removed to obtain blade front and rear edge measurement data, signal enhancement processing is carried out on the data feature set, and finally, the roughness parameters such as Ra, rz, rsm and the like are obtained through calculation and analysis by a blade front and rear edge roughness algorithm. And further, the surface roughness parameter evaluation of the special complex curved surface workpiece is realized.
2. The invention discloses a method for evaluating the roughness of the front edge and the rear edge of a blade, which is characterized in that at present, for the surface profile with overlarge curvature, a contact type roughness measuring device cannot measure and evaluate the surface profile, and a non-contact type roughness measuring device can measure the surface profile with larger curvature, but no mature roughness evaluating method exists. If the surface quality parameters of the front edge and the rear edge of the blade are out of tolerance, no clear detection means and no accurate evaluation method exist, the method can realize the evaluation of the roughness of the special profile of the blade, and fills the blank of the evaluation method of the surface roughness of the front edge and the rear edge of the blade with special geometric characteristics.
3. The invention discloses a method for evaluating the roughness of the front and rear edges of a blade, which aims at the defect that in the current mass production of the blade, the roughness of the front and rear edges of the blade is usually detected by adopting means such as experience, visual inspection and the like, and lacks of an accurate method for evaluating the roughness of the front and rear edges of the blade.
Drawings
Fig. 1: a flow chart of a method for evaluating the roughness of the front edge and the rear edge of a blade;
fig. 2: schematic diagram of the positions of the front edge and the rear edge of the blade;
fig. 3: and (5) blade actual measurement point cloud data.
The specific embodiment is as follows:
the technical scheme of the invention is described below with reference to the accompanying drawings.
For a better description of the objects and advantages of the present invention, the following description of the invention refers to the accompanying drawings and examples.
Blade profile measurement point cloud C i (x ci ，y ci ，z ci ) The method for evaluating the roughness of the front edge and the rear edge of the blade, which is acquired by a three-coordinate measuring machine and is disclosed in the embodiment, comprises the following specific implementation steps:
step 1: measuring point cloud C of acquired blade profile i (x ci ，y ci ，z ci ) Data segmentation is carried out to obtain blade leading edge and trailing edge measurement data C 1i (x ci ，y ci ，z ci )；
And connecting the longest chord of the profile of the blade, and intercepting 4% of each of the two ends of the longest chord as the front and rear edge parts of the blade to finish data segmentation.
Step 2: using a rotational translation matrix [ R T ]]Measuring data C of the front edge and the rear edge of the blade 1i (x ci ，y ci ，z ci ) Converting into a certain space coordinate plane to obtain aligned point cloud data, and recording the aligned point cloud data as D i (x di ，y di 1) a step of; can be simplified into D i (x di ，y di ) Wherein
Using a rotational translation matrix [ R T ]]The alignment method comprises the following steps: let C 1 Normal vector k in Z direction c =1. I.e.
D[x di ,y di ,1,1] T ＝[R T]·C 1 (x ci ,y ci ,z ci ,1) T
Wherein R is a rotation matrix, T is a translation matrix, and θ is a rotation angle.
Step 3: considering that the geometry of the front and rear edges of the blade is special (curvature is large), the surface profile has a large influence on the accuracy of the roughness evaluation result, and the data set D is utilized i (x di ，y di ) The roughness parameters cannot be directly assessed, so that the shapes of the front edge and the rear edge of the blade are removed to obtain a data set PD i (x ei ，y ei ) The method comprises the steps of carrying out a first treatment on the surface of the The specific implementation method is as follows:
fitting a B-spline curve f (x, y) by using theoretical values of front and rear edges of the blade, and aiming at the discrete point data D of the front and rear edges i (x di ，y di ) Calculating the chord length of each point, and adding a control point V 0 ，V 1 ，V 2 ，...，V n Performing B spline interpolation N times, where let n=3, and then:
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)
wherein V is i (x)，V i (y) is the x of the dot i ，y i Coordinates, t, are arc length parameters.
Further solving for the deviation from the fitted curve, wherein further solving for the deviation PD from the fitted curve i (x ei ，y ei )＝D i (x di ，y di ) -f (x, y) to obtain xx ei For a fixed sampling step (0.5 μm or 0.25 μm), y di Is the error value. Thus discrete data set PD i (x ei ，y ei ) May be abbreviated as D (n);
step 4: consider measuring party in point cloud data acquisition processThe influence of the method, the real point cloud data has distortion when reflecting the surface profile of the real workpiece, thus aiming at the data set PD i (x ei ，y ei ) Enhancing the data signal to obtain an enhanced data set RD i (x ri ，y ri )；
Designing a conversion function and a discrete data set D (n); a convolution operation is performed to obtain the enhanced data set RD (n).
Step 5: for data sets RD in accordance with roughness standard specifications i (x ri ，y ri ) And (5) evaluating the unfolding roughness parameters. Obtaining roughness parameters of the front edge and the rear edge of the blade; the parameters include Ra (arithmetic mean deviation of profile), rz (maximum height of profile) and Rsm (mean width of profile units).
According to the related roughness standard specification, selecting lambda by utilizing the relation between Rsm and roughness evaluation length c Contour filter and lambda s Contour filter, lambda c The surface profile obtained by filtering is a roughness assessment center line.
The method for evaluating the roughness of the front and rear edges of the blade realizes the roughness evaluation of the front and rear edge positions of the special profile of the workpiece with the complex curved surface characteristics of the blade, meets the technical requirements of surface roughness detection of the front and rear edge positions of the special geometrical characteristics of the blade, and solves the technical problems of related engineering.
While the foregoing is directed to embodiments of the present invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof, and the scope thereof is determined by the claims that follow.
Claims (7)
1. A method for evaluating the roughness of the front edge and the rear edge of a blade is characterized by comprising the following steps: comprises the steps of,
step 1: measuring point cloud C of acquired blade profile i (x ci ，y ci ，z ci ) Data segmentation is carried out to obtain blade front and rear edge measurement data C 1i (x ci ，y ci ，z ci )；
Step 2: using a rotational translation matrix [ R T ]]Measuring data C of the front edge and the rear edge of the blade 1i (x ci ，y ci ，z ci ) Converting into a certain space coordinate plane to obtain aligned point cloud data, and recording the aligned point cloud data as D i (x di ，y di 1) a step of; simplified to D i (x di ，y di )；
Step 3: step 2 obtaining data set D i (x di ，y di ) Removing the shape of the front and rear edges of the blade to obtain a data set PD i (x ei ，y ei )；
Step 4: for data set PD i (x ei ，y ei ) Enhancing the data signal to obtain an enhanced data set RD i (x ri ，y ri )；
Step 5: for data sets RD in accordance with roughness standard specifications i (x ri ，y ri ) Spreading out the roughness parameter evaluation to obtain the roughness parameters of the front edge and the rear edge of the blade, namely, the surface roughness evaluation of the front edge and the rear edge of the profile of the workpiece with complex curved surface characteristics of the blade is realized; the parameters include the arithmetic mean deviation Ra of the profile, the maximum height Rz of the profile and the mean width Rsm of the profile elements.
2. The blade leading and trailing edge roughness evaluation method as claimed in claim 1, wherein: the blade leading and trailing edge geometry approximates a >2b semi-elliptical shape.
3. The blade leading and trailing edge roughness evaluation method as claimed in claim 1, wherein: the data segmentation method in the step 1 is as follows: connecting the longest chord of the profile of the blade, and intercepting 4% of each of the two ends of the longest chord as the front edge and the rear edge of the blade.
4. The blade leading and trailing edge roughness evaluation method as claimed in claim 1, wherein: the step 2 utilizes a rotation translation matrix [ R T ]]The alignment method comprises the following steps: let C 1 Normal vector k in Z direction c =1; i.e.
D[x di ,y di ,1,1] T ＝[R T]·C 1 (x ci ,y ci ,z ci ,1) T
Wherein R is a rotation matrix, T is a translation matrix, and θ is a rotation angle.
5. The blade leading and trailing edge roughness evaluation method as claimed in claim 1, wherein: in the step 3, a B spline curve f (x, y) is fitted by utilizing theoretical values of the front edge and the rear edge of the blade; the specific implementation method is as follows,
for the front and rear edge discrete point data D i (x di ，y di ) Calculating the chord length of each point, and adding a control point V 0 ，V 1 ，V 2 ，...，V n Performing B spline interpolation N times, where let n=3, and then:
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)
wherein V is i (x)，V i (y) is the x of the dot i ，y i Coordinates, t is an arc length parameter;
further solving for the deviation from the fitted curve, wherein further solving for the deviation PD from the fitted curve i (x ei ，y ei )＝D i (x di ，y di ) -f (x, y) to obtain xx ei For a fixed sampling step (0.5 μm or 0.25 μm), y di Is an error value; thus discrete data set PD i (x ei ，y ei ) Abbreviated as D (n).
6. The blade leading and trailing edge roughness evaluation method as claimed in claim 1, wherein: in the step 4, a conversion function and a discrete data set D (n) are designed; performing convolution operation to obtain an enhanced data set RD (n);
7. the blade leading and trailing edge roughness evaluation method as claimed in claim 1, wherein: in the step 5, lambda is selected by using the relation between Rsm and the roughness evaluation length c Contour filter and lambda s Contour filter, lambda c The surface profile obtained by filtering is a roughness assessment center line.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310348666.5A CN116777831A (en) | 2023-04-04 | 2023-04-04 | Blade front and rear edge roughness evaluation method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310348666.5A CN116777831A (en) | 2023-04-04 | 2023-04-04 | Blade front and rear edge roughness evaluation method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116777831A true CN116777831A (en) | 2023-09-19 |
Family
ID=88010531
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310348666.5A Pending CN116777831A (en) | 2023-04-04 | 2023-04-04 | Blade front and rear edge roughness evaluation method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116777831A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117824575A (en) * | 2023-12-28 | 2024-04-05 | 中国航空工业集团公司北京长城计量测试技术研究所 | A method and device for evaluating blade chord-wise waviness |
-
2023
- 2023-04-04 CN CN202310348666.5A patent/CN116777831A/en active Pending
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117824575A (en) * | 2023-12-28 | 2024-04-05 | 中国航空工业集团公司北京长城计量测试技术研究所 | A method and device for evaluating blade chord-wise waviness |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN117237368B (en) | Bridge crack detection method and system | |
| CN104697462B (en) | A kind of aerial blade surface-type feature parameter extracting method based on axis | |
| JP5087794B2 (en) | How to repair a workpiece such as a turbomachine blade of a dam blade | |
| CN106407502B (en) | Blade section profile parameter evaluation method based on optimal matching | |
| CN112446123B (en) | Measuring head pose planning method for blisk three-coordinate measuring machine | |
| CN109887015A (en) | A Point Cloud Automatic Registration Method Based on Local Surface Feature Histogram | |
| CN110276138B (en) | Digitized evaluation method for shapes of front edge and rear edge of blade of aero-engine | |
| CN105627923B (en) | A kind of planning parameters of scanning paths method that blade dense point cloud based on laser ranging is obtained | |
| CN106446343B (en) | A method for automatic extraction of parametric profiles of straight-grained blades of radial flow impellers | |
| CN109858124B (en) | Method for measuring and calculating grinding amount of aero-engine blade | |
| CN107702651A (en) | The method for registering of four axle measuring system coordinate systems and tested leaf model coordinate system based on distance measuring sensor | |
| CN102735204A (en) | Chord-line-based aviation thin-wall blade machining torsion degree error measurement method | |
| Jiang et al. | A novel centerline extraction algorithm for a laser stripe applied for turbine blade inspection | |
| CN116777831A (en) | Blade front and rear edge roughness evaluation method | |
| CN103292760A (en) | Thin-wall blade error analytical method | |
| CN111862054A (en) | A rivet contour point cloud extraction method | |
| CN113094964B (en) | Method and device for generating blade machining coordinates | |
| CN110672032A (en) | Blade machining torsion error measuring method based on chord line | |
| CN101839149A (en) | Computing method of revolution section profile of turbomachinery blade | |
| CN106021885A (en) | Illumination model-based discrete surface quality analysis method and system | |
| CN114492062A (en) | Fan blade reverse reconstruction method based on non-contact measurement | |
| CN115330977A (en) | A Surface Reconstruction Algorithm for Blade Repair Area Based on V-direction Optimal Reference Iteration | |
| CN114972306B (en) | Image-based blade parameter measurement method | |
| CN114882083B (en) | Method and device for preprocessing profile contour point cloud of thin-wall annular part with complex special-shaped cross section | |
| CN113962021A (en) | A compressor simulated blade stress gradient test piece and optimization method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |