CN104199742A - Method for accurately dividing blade cross section character point cloud - Google Patents
Method for accurately dividing blade cross section character point cloud Download PDFInfo
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- CN104199742A CN104199742A CN201410449437.3A CN201410449437A CN104199742A CN 104199742 A CN104199742 A CN 104199742A CN 201410449437 A CN201410449437 A CN 201410449437A CN 104199742 A CN104199742 A CN 104199742A
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Abstract
The invention relates to a method for accurately dividing a blade cross section character point cloud. According to the method, firstly, initial dividing is achieved by extracting a midpoint of every two adjacent points in the point cloud; secondly, the parameters of a front edge and the parameters of a back edge are accurately extracted; thirdly, the intersections with the front edge and the intersections with the back edge are deleted from a blade pot point set and a blade back point set respectively so that the character point cloud can be accurately divided, wherein the blade pot point set and the blade point set are formed through initial dividing. The method overcomes the defects of a convex hull algorithm and an algorithm improved on the basis of the convex hull algorithm without loss of generality, and is suitable for dividing cross section character point clouds of blades within a wider range, and accuracy of extracting of the subsequent blade molded face feature parameters is guaranteed.
Description
Technical field
The present invention relates to field of computer data processing, especially a kind of accurate division methods of division of the section feature point cloud that is applicable to wider intra vane.
Background technology
Blade is one of kernel component of aeromotor, has the technical characterstics such as strong distortion, thin-wall part, yielding, low damage.The accuracy of detection of blade directly has influence on the performance of engine, so must adopt strict detection means.Most measurement means is all to judge that by detecting the characteristic parameter of blade profile whether blade is qualified, therefore, high precision, extracts blade profile parameter expeditiously and has very high use value.
The accurate division of blade profile unique point cloud is the prerequisite of extracting blade profile parameter.At present, conventional cross section division methods is mostly based on classical algorithm of convex hull, or carries out on its basis the improvement of algorithm, substantially can meet the division of most of blade profile.But for some special cross section, algorithm of convex hull may be divided on leaf basin or directly and deletes originally belonging to non-convex closure point on blade back, causes the extraction accuracy of section feature parameter poor even failed, algorithm of convex hull result as shown in Figure 2:
In Fig. 2, the point of '+' mark belongs to blade back originally, but is not convex closure point.In the time adopting algorithm of convex hull, these points can be skipped over, be directly connected with the point of contact in leading edge, and gauge point is used as to the point on leaf basin.Cause on blade back '+' marker field error of fitting larger, simultaneously leaf basin section curve mistake.
Summary of the invention
The technical problem to be solved in the present invention is: the accurate division methods that proposes a kind of blade profile unique point cloud, not only make up the deficiency of algorithm of convex hull and improvement algorithm thereof, and without loss of generality, be applicable to the division of the section feature point cloud of wider intra vane, ensured the accuracy of the extraction of follow-up blade profile characteristic parameter.
The technical solution adopted in the present invention is: a kind of accurate division methods of blade profile unique point cloud, comprises the following steps:
1) the each cross section of blade is carried out laser measurement and carried out the cloud data that data processing obtains simplifying; And set up X, Y coordinate figure;
2) cloud data is sorted according to the ascending order of X coordinate figure; The threshold value of setting the Y coordinate figure variation of adjacent 2, the variation of searching successively coordinate figure in a cloud is greater than the consecutive point of this threshold value, obtains middle point coordinate;
3) to step 2) in the mid point that obtains carry out matching; And the upper and lower two parts of a cloud are carried out to Preliminary division;
4) point of the point of X coordinate figure minimum, Y coordinate figure minimum and the point of X coordinate figure maximum in selected point cloud; Taking the point of Y coordinate figure minimum as reference point, the lower part of putting cloud after Preliminary division finds out slope corresponding point hour;
5) point of the point of the point of X coordinate figure minimum in step 4), Y coordinate figure minimum, X coordinate figure maximum and the lower part of putting cloud after Preliminary division are found out to interval that slope corresponding point hour form and carry out matching and draw front and rear edges, and in certain area, find out all point sets that are positioned in front and rear edges;
6) ask for respectively upper and lower two parts of obtaining after Preliminary division and the common factor of front and rear edges point set; And obtain the point set on leaf basin and blade back;
7) point on leaf basin, blade back is carried out respectively to the accurate division of interpolation and matching realization character point cloud.
In step 3) of the present invention, adopt cubic polynomial alignment to carry out matching; The mode of matching is: cloud data is brought in cubic polynomial successively, realized the upper and lower two-part Preliminary division of a cloud by the size of cloud data relatively and the Y coordinate figure that draws through cubic polynomial.
In step 5) of the present invention, the point of the point of X coordinate figure minimum and Y coordinate figure minimum forms the first interval; The point of X coordinate figure maximum and the lower part of putting cloud after Preliminary division find out slope corresponding point hour and form between Second Region; Between the first interval and Second Region, go out out front and rear edges with least square fitting.
In step 6) of the present invention, concentrate and deduct common factor and just obtain the point set leaf basin, blade back from upper and lower two parts point.
The invention has the beneficial effects as follows: the cloud data of the blade profile that laser optical method is recorded is accurately divided, thereby ensureing the fitting precision of follow-up zones of different data point and the extraction accuracy of section feature parameter, is integrated the laying the foundation of the processing of blade-measure.
Brief description of the drawings
Below in conjunction with drawings and Examples, the present invention is further described.
Fig. 1 is the cross sectional shape figure of the present invention's blade that will use;
Fig. 2 is the design sketch of available technology adopting algorithm of convex hull;
Fig. 3 is the Preliminary division figure of cloud data of the present invention;
Fig. 4 is method design sketch of the present invention;
Fig. 5 is method flow diagram of the present invention.
Embodiment
The present invention is further detailed explanation with preferred embodiment by reference to the accompanying drawings now.These accompanying drawings are the schematic diagram of simplification, and basic structure of the present invention is only described in a schematic way, and therefore it only shows the formation relevant with the present invention.
The each several part of blade profile defines as shown in Figure 1: comprise leaf basin, blade back, leading edge and trailing edge.
In the time that some special blade profile is carried out to the division of unique point cloud, conventional algorithm of convex hull or improvement algorithm of convex hull exist obvious deficiency, can the point that originally be positioned on blade profile blade back are divided on leaf basin or directly deletion.As shown in Figure 2, in Fig. 2, the point of '+' mark belongs to blade back originally, but is not convex closure point.In the time adopting algorithm of convex hull, these points can be skipped over, be directly connected with the point of contact in leading edge, and gauge point is used as to the point on leaf basin.Cause on blade back '+' marker field error of fitting larger, simultaneously leaf basin section curve mistake.
Consider cloud data coordinate compared with small neighbourhood in, it is more obvious that the coordinate figure of leaf basin and blade back changes the variation of relative coordinate value.On this basis, the mid point of putting in cloud at adjacent 2 by extraction is realized Preliminary division, then the parameter of front and rear edges is accurately extracted, last, thus the accurate division of concentrating the common factor realization character point cloud of deletion respectively and front and rear edges from leaf basin and the blade back point of Preliminary division.As shown in Figure 5, method is as follows for idiographic flow:
(1) thus the cloud data in the each cross section of blade being recorded by laser optical method is carried out to the cloud data that the operations such as filtering and eliminating noise, data compaction obtain simplifying.
(2) by cloud data according to the ascending sort of coordinate figure.Set the threshold value that adjacent two point coordinate values change, search successively the consecutive point that the variation of coordinate figure in a cloud is greater than, and solve middle point coordinate.
(3) adopt cubic polynomial to carry out matching to the mid point of asking for.Cloud data is brought into successively in the cubic polynomial simulating, realized to the upper and lower two-part Preliminary division of a cloud, as shown in Figure 3 by the size of cloud data relatively and the coordinate figure that draws through cubic polynomial.
(4) search point, the point of coordinate figure minimum and the point of coordinate figure maximum of coordinate figure minimum in a cloud.Taking point as reference point, the lower part of putting cloud after Preliminary division finds out hour corresponding point of slope.
(5) from the geometric relationship in cross section, interval and be respectively the subset in front and rear edges.Interval and in, go out front and rear edges with least square fitting, and in appointed area, find out all point sets that are positioned in front and rear edges, taking trailing edge as example:
If the trailing edge center of circle simulating is that radius is that the algebraic distance of putting leading edge is:, setting threshold.Taking an A as starting point, all points that are less than in scope in search direction.
(6) ask for respectively upper and lower two parts of obtaining after Preliminary division and the common factor of front and rear edges point set.Concentrate and deduct common factor and just obtain the point set leaf basin, blade back from upper and lower two parts point.
(7) point on the leaf basin of trying to achieve, blade back is adopted respectively to cubic spline interpolation and matching, effect as shown in Figure 4.
Compare the result of algorithm of convex hull in Fig. 2, the algorithm that the present invention adopts can better be avoided the deficiency of algorithm of convex hull in the time processing special cross-section, has ensured the accuracy of follow-up leaf characteristic parameter extraction
Just the specific embodiment of the present invention of describing in above instructions, various not illustrating is construed as limiting flesh and blood of the present invention, person of an ordinary skill in the technical field read after instructions can to before described embodiment make an amendment or be out of shape, and do not deviate from the spirit and scope of the invention.
Claims (4)
1. an accurate division methods for blade profile unique point cloud, is characterized in that comprising the following steps:
1) the each cross section of blade is carried out laser measurement and carried out the cloud data that data processing obtains simplifying; And set up X, Y coordinate figure;
2) cloud data is sorted according to the ascending order of X coordinate figure; The threshold value of setting the Y coordinate figure variation of adjacent 2, the variation of searching successively coordinate figure in a cloud is greater than the consecutive point of this threshold value, obtains middle point coordinate;
3) to step 2) in the mid point that obtains carry out matching; And the upper and lower two parts of a cloud are carried out to Preliminary division;
4) point of the point of X coordinate figure minimum, Y coordinate figure minimum and the point of X coordinate figure maximum in selected point cloud; Taking the point of Y coordinate figure minimum as reference point, the lower part of putting cloud after Preliminary division finds out slope corresponding point hour;
5) point of the point of the point of X coordinate figure minimum in step 4), Y coordinate figure minimum, X coordinate figure maximum and the lower part of putting cloud after Preliminary division are found out to interval that slope corresponding point hour form and carry out matching and draw front and rear edges, and in certain area, find out all point sets that are positioned in front and rear edges;
6) ask for respectively upper and lower two parts of obtaining after Preliminary division and the common factor of front and rear edges point set; And obtain the point set on leaf basin and blade back;
7) point on leaf basin, blade back is carried out respectively to the accurate division of interpolation and matching realization character point cloud.
2. the accurate division methods of a kind of blade profile unique point cloud as claimed in claim 1, is characterized in that: in described step 3), adopt cubic polynomial alignment to carry out matching; The mode of matching is: cloud data is brought in cubic polynomial successively, realized the upper and lower two-part Preliminary division of a cloud by the size of cloud data relatively and the Y coordinate figure that draws through cubic polynomial.
3. the accurate division methods of a kind of blade profile unique point cloud as claimed in claim 1, is characterized in that: in described step 5), the point of the point of X coordinate figure minimum and Y coordinate figure minimum forms the first interval; The point of X coordinate figure maximum and the lower part of putting cloud after Preliminary division find out slope corresponding point hour and form between Second Region; Between the first interval and Second Region, go out out front and rear edges with least square fitting.
4. the accurate division methods of a kind of blade profile unique point cloud as claimed in claim 1, is characterized in that: in described step 6), concentrate and deduct common factor and just obtain the point set leaf basin, blade back from upper and lower two parts point.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106021177A (en) * | 2016-05-19 | 2016-10-12 | 兰州交通大学 | Scanning line-based three-dimensional laser scanning data compaction method |
| CN113469907A (en) * | 2021-06-28 | 2021-10-01 | 西安交通大学 | Data simplification method and system based on blade profile characteristics |
| CN115861266A (en) * | 2022-12-20 | 2023-03-28 | 中国航发贵州黎阳航空动力有限公司 | Intelligent detection method for compressor blades |
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| CN101680429A (en) * | 2007-04-30 | 2010-03-24 | Lm玻璃纤维有限公司 | Measuring of geometrical parameters for a wind turbine blade |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106021177A (en) * | 2016-05-19 | 2016-10-12 | 兰州交通大学 | Scanning line-based three-dimensional laser scanning data compaction method |
| CN106021177B (en) * | 2016-05-19 | 2018-10-23 | 兰州交通大学 | A kind of laser scanning data compressing method based on scan line |
| CN113469907A (en) * | 2021-06-28 | 2021-10-01 | 西安交通大学 | Data simplification method and system based on blade profile characteristics |
| CN113469907B (en) * | 2021-06-28 | 2023-04-07 | 西安交通大学 | Data simplification method and system based on blade profile characteristics |
| CN115861266A (en) * | 2022-12-20 | 2023-03-28 | 中国航发贵州黎阳航空动力有限公司 | Intelligent detection method for compressor blades |
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