CN117824575B - A method and device for evaluating blade chord-wise waviness - Google Patents
A method and device for evaluating blade chord-wise waviness Download PDFInfo
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- 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
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Abstract
The invention discloses a blade chord direction waviness evaluation method and device, wherein the method comprises the steps of registering an acquired blade profile measurement point set with a theoretical point set by utilizing an ICP algorithm to obtain a registered measurement point set, carrying out B spline curve modeling on the registered measurement point set to obtain a fitting measurement point set, comparing the obtained fitting measurement point set with the theoretical point set, calculating to obtain a blade profile measurement deviation data set, calculating the distance between two adjacent points of the obtained blade profile measurement deviation set to obtain a deviation distance data set, calculating the maximum difference value of measurement deviation within any 5mm range to obtain a maximum deviation data set, and taking an average value and a maximum value of the obtained maximum deviation data set to obtain a blade chord direction waviness arithmetic average value and a blade chord direction waviness maximum profile peak height. The invention has high reliability, strong operability, high reliability of detection results and small measurement error.
Description
Technical Field
The invention belongs to the field of processing and detecting of blades of compressors and turbines, and particularly relates to a method and a device for evaluating chordwise waviness of blades.
Background
The blade surface quality parameters occupy a large proportion among a plurality of influencing factors of the performance of the compressor and the turbine, wherein the chord-wise waviness of the blade directly influences the aerodynamic performance, the heat conducting performance and the like of the compressor and the turbine. The blade profile IS a special geometric characteristic closed curve, the blade chordwise waviness parameters cannot be evaluated by utilizing a traditional waviness evaluation method according to relevant standard specifications such as IS04287-2009, a blade chordwise waviness detection mode IS not explicitly pointed out in HB 5647-48, in the process of evaluating the blade chordwise waviness by utilizing commercial metering equipment, the repeatability of each evaluation result IS poor, the variability IS overlarge, and no effective measuring and evaluating method for evaluating the blade chordwise waviness IS generated at present. For the surface profile with a closed curve and an overlarge curvature, various measuring equipment have overlarge space for acquiring point cloud data/insufficient precision, and no mature blade chordwise waviness evaluation method exists. If the parameters of the chord-wise waviness of the blade are out of tolerance, no explicit detection means and no accurate evaluation method exist, the qualification judgment of the chord-wise waviness of the blade, the determination of the process and the design iteration direction cannot be carried out, and in the mass production of the blade, the chord-wise waviness of the blade is often detected by means of experience, visual inspection and the like, and the accurate evaluation method of the chord-wise waviness of the blade is lacked.
Disclosure of Invention
The invention mainly aims to provide a blade chord direction waviness evaluation method and device, computer equipment and a computer readable storage medium, wherein the blade chord direction waviness evaluation method and device are high in reliability, strong in operability, high in reliability of detection results and small in measurement errors.
In order to achieve the above object, an aspect of the present invention provides a blade chordwise waviness evaluation method including:
Step S1, registering the acquired blade profile measuring point set with a theoretical point set by utilizing an ICP algorithm to obtain a registered measuring point set;
S2, performing B spline curve modeling on the registered measurement point set in the step S1 to obtain a fitting measurement point set;
S3, comparing the fitting measurement point set obtained in the step S2 with a theoretical point set, and calculating to obtain a blade profile measurement deviation data set;
s4, calculating the distance between two adjacent points of the blade profile measurement deviation set obtained in the step S3 to obtain a deviation distance data set;
Step S5, calculating the maximum difference value of the measured deviation within any 5mm range for the deviation distance data set obtained in the step S4 to obtain a maximum deviation data set;
and S6, averaging the maximum deviation data set obtained in the step S5 to obtain a blade chord direction waviness arithmetic average value, and averaging the maximum deviation data set obtained in the step S5 to obtain the blade chord direction waviness maximum profile peak height.
Preferably, in step S1, the registering of the blade profile measurement point set with the theoretical point set by using the ICP algorithm includes:
searching a closest point set corresponding to the theoretical point set and the measured point set, calculating centroid position coordinates of the theoretical point set and the closest point set, and solving a cross covariance matrix of the theoretical point set and the closest point set according to the centroid position coordinates;
solving the optimal rotation quaternion vector of the cross covariance matrix, and constructing a 4 multiplied by 4 symmetric matrix;
solving the eigenvectors of the symmetric matrix, unitizing the eigenvectors corresponding to the maximum eigenvalues to obtain unit quaternion vectors, and converting the unit quaternion vectors into a 3×3 rotation matrix;
obtaining an optimal translation vector according to the centroid position coordinates and the rotation matrix;
and carrying out iterative registration on the measuring point set and the theoretical point set by using the obtained rotation matrix and the translation vector, calculating the distance mean square sum of the corresponding point pair of the measuring point set and the nearest point set after each iterative registration, and stopping the iteration by the ICP algorithm when the difference between the distance mean square sums of the two iterations is smaller than a set threshold value.
Preferably, in step S2, the performing B-spline curve modeling includes:
aiming at the registered measurement point set, calculating the chord length of each point, adding a control point V 0,V1,V2,…,Vn, and performing B spline interpolation for 3 times, wherein the following steps are as follows:
xpni＝(1/6)[t3Vi+2(x)+(-3t3+3t2+3t+1)Vi+1(x)+(3t3-6t2+4)Vi(x)+(-t3+3t2-3t+1)Vi-1(x)
ypni＝(1/6)[t3Vi+2(y)+(-3t3+3t2+3t+1)Vi+1(y)+(3t3-6t2+4)Vi(y)+(-t3+3t2-3t+1)Vi-1(y)
zpni＝(1/6)[t3Vi+2(z)+(-3t3+3t2+3t+1)Vi+1(z)+(3t3-6t2+4)Vi(z)+(-t3+3t2-3t+1)Vi-1(z)
Wherein V i(x),Vi(y),Vi (z) is the x i,yi,zi coordinate of the point, t is the arc length parameter, and n represents the number of the points in the registered measurement point set.
Preferably, in step S5, the measured deviation maximum difference num in any 5mm range is calculated as follows:
num=max(errori)-min(errori)
Wherein error i represents blade profile measurement deviation, d i represents distance between two adjacent points, and m and n represent index values of two ends of measurement deviation error i within a range of 5 mm.
Another aspect of the present invention provides a blade chordwise waviness evaluation device, including:
The registration point set acquisition module is configured to register the acquired blade profile measurement point set with the theoretical point set by utilizing an ICP algorithm to obtain a registered measurement point set;
the fitting point set acquisition module is configured to perform B spline curve modeling on the registered measuring point set to obtain a fitting measuring point set;
The measuring deviation data set acquisition module is configured to compare the obtained fitting measuring point set with the theoretical point set and calculate to obtain a blade profile measuring deviation data set;
The deviation distance data set acquisition module is configured to calculate the distance between two adjacent points of the obtained blade profile measurement deviation set to obtain a deviation distance data set;
The maximum deviation data set acquisition module is configured to calculate the maximum difference value of the measured deviation within any 5mm range for the obtained deviation distance data set to obtain a maximum deviation data set;
The waviness parameter obtaining module is configured to average the obtained maximum deviation data set to obtain a blade chord direction waviness arithmetic average value, and maximum the obtained maximum deviation data set to obtain the blade chord direction waviness maximum profile peak height.
A further aspect of the invention provides a computer device comprising a memory storing a computer program and a processor implementing the steps of the method described above when the computer program is executed by the processor.
A further aspect of the invention is a computer-readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method described above.
The blade chord direction waviness evaluation method and device, the computer equipment and the computer readable storage medium have the advantages of high reliability, strong operability, high reliability of detection results and small measurement error.
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For a clearer description of the technical solutions of the present invention, the following description will be given with reference to the attached drawings used in the description of the embodiments of the present invention, it being obvious that the attached drawings in the following description are only some embodiments of the present invention, and that other attached drawings can be obtained by those skilled in the art without the need of inventive effort:
FIG. 1 is a flow chart of a blade chordwise waviness evaluation method of one embodiment of the present invention;
FIG. 2 is a schematic diagram of a set of measured points versus a set of theoretical points of an embodiment of the present invention;
FIG. 3 is a block diagram of a blade chordwise waviness evaluation device according to one embodiment of the present invention;
Fig. 4 is a block diagram of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
An embodiment of the present invention provides a method for evaluating blade chord-wise waviness, and fig. 1 is a flowchart of a method for evaluating blade chord-wise waviness according to an embodiment of the present invention. As shown in FIG. 1, the blade chord direction waviness evaluation method in the embodiment of the invention comprises steps S1-S6.
In step S1, a blade profile measurement point set C (x ci,yci,zci) (hereinafter referred to as C (C i)) acquired by a three-coordinate measuring machine is registered with a theoretical point set PL (x i,yi,zi) (hereinafter referred to as PL (PL i)) by an ICP algorithm, and a registered measurement point set P (x p,yp,zp) (hereinafter referred to as P (P)) is obtained. In which the measurement point set C (C i) and the theoretical point set PL (PL i) are shown in fig. 2, the two middle curves with both ends closed represent the theoretical point set PL (PL i), and the curves intersecting on both sides of the theoretical point set and scattered on both ends represent the measurement point set C (C i).
In step S2, B-spline curve modeling is performed on the set of measurement points P (P) registered in step S1, to obtain a set of fitted measurement points N (x pni,ypni,zpni).
In step S3, the fitting measurement point set N (x pni,ypni,zpni) obtained in step S2 is compared with a theoretical point set, blade profile measurement deviation is calculated,Thereby obtaining a blade profile measurement deviation dataset N (x pni,ypni,zpni,errori).
In step S4, the distance between adjacent two points of the blade profile measurement deviation dataset N (x pni,ypni,zpni,errori) obtained in step S3 is calculated,Thereby obtaining the offset distance dataset ND (d i,errori).
In step S5, the maximum difference num of the measured deviation error within an arbitrary 5mm range is calculated for the deviation distance data set ND (d i,errori) obtained in step S4, and the maximum deviation data set Max (num 1,num2,…,numn) is obtained.
In this step, the basis for calculating waviness in any 5mm range is specified in that the blade surface waviness profile F (x) is composed of sine waves S i (x) of different wavelengths in the range of 1mm to 10mm, and the distance signal S i (x) follows a normal distribution law. Namely:
F(x)=S0(x)+S1(x)+S2(x)+…+Sn(x)
The sine wave S i (x) with the wavelength in the range of (5+/-0.5) mm in the blade waviness profile F (x) accounts for more than 83% by a large number of blade chord-wise waviness test data and waviness profile spectrum analysis, so that the evaluation length of the blade chord-wise waviness is determined to be 5mm.
In step S6, the maximum deviation data set Max (num 1,num2,…,numn) obtained in step S5 is averaged to obtain a Blade chord direction waviness arithmetic average value, which is denoted as blade_wa, and the maximum deviation data set Max (num 1,num2,…,numn) obtained in step S5 is maximized to obtain a Blade chord direction waviness maximum profile peak height, which is denoted as blade_wz. Wherein, blade_wa and blade_wz are both Blade chord-wise waviness parameters. Blade_wa and blade_wz were obtained as Blade chord-wise waviness assessment results.
In one embodiment, in the step S1, the registration of the blade profile measurement point set and the theoretical point set is performed by utilizing an ICP algorithm, and the specific implementation method is that a closest point set Q (x qi,yqi,zqi) (hereinafter referred to as Q (Q i)) corresponding to the measurement point set C (C i) of the blade profile theoretical point set PL (PL i) is searched, and centroid position coordinates of the point sets PL (PL i) and Q (Q i) are respectively recorded as: (the following is written: )、 (the following is written: ). Further solve for the cross covariance matrices of PL (PL i) and Q (Q i), namely:
Where U is the number of corresponding points.
And then solving the optimal rotation quaternion vector of the cross covariance matrix to construct a 4 multiplied by 4 symmetric matrix:
Where Δ is the column vector made up of components of the antisymmetric matrix A ij＝(∑pq-∑pq T)ij, tr (Σ pq) is the trace of the matrix Σ pq, I 3 is the 3×3 identity matrix, and Δ T is the transpose of Δ. Solving the eigenvector of the matrix M (Σ pq), unitizing the eigenvector corresponding to the maximum eigenvalue to obtain a unit quaternion vector d= [ D 0 d1 d2 d3]T ], and converting the unit quaternion vector into a 3×3 rotation matrix R by using the following formula:
The optimal translation vector T is:
and determining a point set P (p+1) after the point set P (P) is transformed by the rotation translation matrix [ R, T ], calculating the distance mean square sum E k of point pairs corresponding to the point set P (p+1) and Q (qi), and stopping the iteration by the ICP algorithm when the absolute value E k-Ek-1 is smaller than epsilon (epsilon is a set threshold value), wherein the measurement of the section profile of the blade is close to the real state.
In one embodiment, the specific implementation method of B-spline curve modeling in step S2 is as follows:
for the registered measurement point set P (x p,yp,zp), calculating the chord length of each point, adding a control point V 0,V1,V2,…,Vn, and performing 3 times of B spline interpolation, wherein the following steps are as follows:
xpni＝(1/6)[t3Vi+2(x)+(-3t3+3t2+3t+1)Vi+1(x)+(3t3-6t2+4)Vi(x)+(-t3+3t2-3t+1)Vi-1(x)
ypni＝(1/6)[t3Vi+2(y)+(-3t3+3t2+3t+1)Vi+1(y)+(3t3-6t2+4)Vi(y)+(-t3+3t2-3t+1)Vi-1(y)
zpni＝(1/6)[t3Vi+2(z)+(-3t3+3t2+3t+1)Vi+1(z)+(3t3-6t2+4)Vi(z)+(-t3+3t2-3t+1)Vi-1(z)
wherein V i(x),Vi(y),Vi (z) is the x i,yi,zi coordinate of the point, t is the arc length parameter, and N represents the number of the points in the registered measurement point set P (P), so that the fitting measurement point set N (x pni,ypni,zpni) can be obtained.
In one embodiment, in step S5, the maximum difference num of the measured deviation error in any 5mm range is calculated as follows:
num=max(errori)-min(errori) m and n are positive integers.
Wherein m and n represent index values at two ends of the array error within a range of 5 mm.
The blade chord direction waviness evaluation method of the embodiment of the invention has the following beneficial effects:
1. according to the invention, a Blade profile measuring point set with the characteristic of a complex curved surface of a Blade is registered with a theoretical point set by utilizing an ICP algorithm, the registered measuring point set is subjected to B spline curve fitting, the deviation between the measuring point set and the theoretical point set is solved, finally, blade chord-wise waviness parameters are evaluated according to the waviness profile wavelength range requirement, parameters such as blade_Wa, blade_Wz are obtained through calculation, and Blade chord-wise waviness parameters of a special complex curved surface workpiece are evaluated.
2. The method can realize the blade chordwise waviness evaluation, fills the blank of the blade chordwise waviness evaluation method, and solves the problems that in the process of evaluating the blade chordwise waviness by using commercial metering equipment for a special geometric characteristic closed curve, the repeatability of each evaluation result is poor, the variability is overlarge, no effective measuring and evaluating method for evaluating the blade chordwise waviness is generated at present, and if the blade chordwise waviness parameter is out of tolerance, no definite detection means and no accurate evaluating method exist.
3. The invention provides a blade chord direction waviness evaluation method which has high reliability, strong operability, high reliability of detection results and small measurement error, and further can guide, optimize and perfect blade design, promote iterative update of blade trial-manufacture and processing technology, and solve the problems that in the current blade batch production, the blade chord direction waviness is often detected by adopting means such as experience, visual inspection and the like, and an accurate blade chord direction waviness evaluation method is lacked.
The embodiment of the invention also provides a blade chord-wise waviness evaluation device. Fig. 3 is a structural view of a blade chord-wise waviness evaluation device according to an embodiment of the present invention. As shown in fig. 3, the blade chord-wise waviness evaluation device of the present embodiment includes:
The registration point set acquisition module 101 is configured to register the acquired blade profile measurement point set with the theoretical point set by utilizing an ICP algorithm to obtain a registered measurement point set;
the fitting point set obtaining module 102 is configured to perform B-spline curve modeling on the registered measuring point set to obtain a fitting measuring point set;
the measurement deviation data set obtaining module 103 is configured to compare the obtained fitting measurement point set with the theoretical point set, and calculate to obtain a blade profile measurement deviation data set;
The deviation distance data set obtaining module 104 is configured to calculate the distance between two adjacent points of the blade profile measurement deviation set to obtain a deviation distance data set;
A maximum deviation data set acquisition module 105 configured to calculate a maximum difference value of the measured deviation within an arbitrary 5mm range for the obtained deviation distance data set, to obtain a maximum deviation data set;
The waviness parameter obtaining module 106 is configured to average the obtained maximum deviation data set to obtain a blade chord direction waviness arithmetic average value, and to maximum the obtained maximum deviation data set to obtain a blade chord direction waviness maximum profile peak height.
Specific examples of the blade chordwise waviness evaluation device of the present embodiment may be referred to above as a limitation of the blade chordwise waviness evaluation method, and will not be described herein. Each module in the blade chord-wise waviness evaluation device can be fully or partially realized by software, hardware and a combination thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
Embodiments of the present invention also provide a computer device, which may be a server, and an internal structure thereof may be as shown in fig. 4. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is used to store operating parameter data for each of the frames. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by a processor, implements the steps of the blade chordwise waviness evaluation method of the present embodiment.
It will be appreciated by persons skilled in the art that the architecture shown in fig. 4 is merely a block diagram of some of the architecture relevant to the present inventive arrangements and is not limiting as to the computer device to which the present inventive arrangements are applicable, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
An embodiment of the present invention also provides a computer-readable storage medium having stored thereon a computer program, characterized in that the computer program, when executed by a processor, implements the steps of the blade chordwise waviness evaluation method of an embodiment of the present invention.
While certain exemplary embodiments of the present invention have been described above by way of illustration only, it will be apparent to those of ordinary skill in the art that modifications may be made to the described embodiments in various different ways without departing from the spirit and scope of the invention. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive of the scope of the invention, which is defined by the appended claims.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110196026A (en) * | 2019-06-27 | 2019-09-03 | 清华大学深圳研究生院 | A kind of method of quick measuring and calculating surface roughness and measurement scale relationship |
| CA3067400A1 (en) * | 2019-11-28 | 2021-05-28 | Xesto Inc. | System and method for mobile 3d scanning and measurement |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH06307840A (en) * | 1993-04-27 | 1994-11-04 | Nippon Steel Corp | Optical surface roughness meter |
| DE102018112805A1 (en) * | 2018-05-29 | 2019-12-05 | Klingelnberg Gmbh | Method for the analysis of surface undulations |
| CN216900319U (en) * | 2021-11-01 | 2022-07-05 | 中国航空工业集团公司北京长城计量测试技术研究所 | Straight rib type thermal power measuring device for nuclear battery |
| CN115808148A (en) * | 2022-10-23 | 2023-03-17 | 国营四达机械制造公司 | Digitized detection method for blade waviness of aircraft engine |
| CN116777831A (en) * | 2023-04-04 | 2023-09-19 | 中国航空工业集团公司北京长城计量测试技术研究所 | Blade front and rear edge roughness evaluation method |
| CN117160912B (en) * | 2023-10-24 | 2024-02-02 | 昆山奥德鲁自动化技术有限公司 | Method and equipment for detecting waviness of ring parts |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110196026A (en) * | 2019-06-27 | 2019-09-03 | 清华大学深圳研究生院 | A kind of method of quick measuring and calculating surface roughness and measurement scale relationship |
| CA3067400A1 (en) * | 2019-11-28 | 2021-05-28 | Xesto Inc. | System and method for mobile 3d scanning and measurement |
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