CN117368203A - Complex shape surface defect identification positioning and shape detection method based on point cloud matching - Google Patents
Complex shape surface defect identification positioning and shape detection method based on point cloud matching Download PDFInfo
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- CN117368203A CN117368203A CN202311134248.2A CN202311134248A CN117368203A CN 117368203 A CN117368203 A CN 117368203A CN 202311134248 A CN202311134248 A CN 202311134248A CN 117368203 A CN117368203 A CN 117368203A
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- 230000007547 defect Effects 0.000 title claims abstract description 86
- 238000001514 detection method Methods 0.000 title claims abstract description 30
- 238000007789 sealing Methods 0.000 claims abstract description 34
- 238000000034 method Methods 0.000 claims abstract description 26
- 238000005259 measurement Methods 0.000 claims abstract description 24
- 230000005477 standard model Effects 0.000 claims abstract description 8
- 238000013461 design Methods 0.000 claims description 13
- 230000009466 transformation Effects 0.000 claims description 10
- 238000012360 testing method Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 230000011218 segmentation Effects 0.000 claims description 7
- 230000004927 fusion Effects 0.000 claims description 4
- 230000009286 beneficial effect Effects 0.000 abstract description 2
- 238000005070 sampling Methods 0.000 abstract description 2
- 239000011159 matrix material Substances 0.000 description 6
- 239000003550 marker Substances 0.000 description 4
- 230000005855 radiation Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 239000008358 core component Substances 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000011179 visual inspection Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
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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
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
Abstract
The invention belongs to the field of three-dimensional defect detection of in-service parts, and particularly relates to a complex shape surface defect identification positioning and shape detection method based on point cloud matching. The method comprises the following steps: the control robot sends the three-dimensional scanner to the position above the flange sealing surface, and scans the sealing surface according to a planned path to acquire data; forming complete point cloud data of the sealing surface by splicing the scanning results; denoising and simplifying the collected point cloud data; matching the simplified data with a standard nuclear power main pump flange sealing surface reference point cloud by adopting a point cloud matching algorithm, and unifying the point-collected cloud data and a designed standard model into a coordinate system; comparing the generated 3D point cloud with a standard model, identifying the shape surface defect through comparison, and calculating the position and the size of the selected defect through selecting the defect area by a frame. The beneficial effects are that: by adopting uniform sampling, the amount of point cloud data can be effectively reduced, and the problem of uneven distribution density of the point cloud data in the measurement overlapping area can be solved.
Description
Technical Field
The invention belongs to the field of three-dimensional defect detection of in-service parts of nuclear power plants, and particularly relates to a complex shape surface defect identification positioning and shape detection method based on point cloud matching.
Background
The nuclear main pump flange sealing structure is a core component of the nuclear power station, and regular in-service maintenance (surface defect identification, shape and size detection and the like) is carried out on surface defects and shape and size of the nuclear main pump flange sealing structure according to regulations, so that the nuclear power station is vital to ensuring safe and stable operation. The sealing structure is large in size (the diameter of the nuclear main pump flange sealing surface is about 2m, the thickness is about 80 mm), the shape and the structure are complex (the nuclear main pump flange sealing surface comprises a plurality of annular grooves), manual visual inspection and caliper/plug gauge measurement are adopted at present, overhaul operation (peeling/pit/scratch identification and shape profile deformation detection) is very easy to generate interference under the on-site narrow space environment, and the detection efficiency is low and the reliability is poor. In addition, the parts are in long-term service in a radiation environment, and nuclear radiation hazard exists in the existing manual overhaul mode.
Disclosure of Invention
The invention aims to provide a point cloud matching-based complex shape surface defect identification positioning and shape detection method, which can realize identification positioning and shape detection of defects on a complex shape surface in the three-dimensional intelligent detection process of nuclear power in-service parts and is used in a three-dimensional intelligent detection system of a nuclear power in-service part robot.
The technical scheme of the invention is as follows: the complex shape surface defect identification positioning and shape detection method based on point cloud matching comprises the following steps:
s1: the control robot sends the three-dimensional scanner to the position above the flange sealing surface, and scans the sealing surface according to a planned path to acquire data;
s2: forming complete point cloud data of the sealing surface by splicing the scanning results;
s3: denoising and simplifying the collected point cloud data;
s4: matching the simplified data with a standard nuclear power main pump flange sealing surface reference point cloud by adopting a point cloud matching algorithm, and unifying the point-collected cloud data and a designed standard model into a coordinate system;
s5: comparing the generated 3D point cloud with a standard model, identifying the shape surface defect through comparison, and calculating the position and the size of the selected defect through selecting the defect area by a frame.
S2 include that the robot carries on the scanner and can acquire the multiunit point cloud data after accomplishing the multi-angle scanning, the multiunit point cloud data that will acquire based on the result of the position appearance transformation relation of scanner relative to robot terminal flange and the multiunit internal joint parameter that the robot corresponds under the multiaspect is unified and is represented under robot base coordinate system, accomplish multi-angle diversified scanning data concatenation and fusion.
The denoising process in the step S3 is as follows:
if a point p in a group of point clouds i E P and another set of points P j And E, if the minimum distance of the P is greater than d, the two point clouds belong to two different sets, the nearest neighbor points of each point are obtained through kd-tree by using nearest neighbor query to calculate the distance value, so that Euclidean segmentation is completed, the result of the Euclidean segmentation is that a plurality of point cloud sets are obtained, the largest point cloud set is taken as a final result, and the rest point clouds are all deleted, so that the denoised point clouds are obtained.
The point cloud simplifying process in the step S3 is as follows:
calculating the projection of the point cloud on the coordinate axis, and taking three directionsConstructing a minimum bounding box by using the distance difference of the point clouds, dividing the bounding box into a plurality of small grids by taking the distance between grids to be divided as d, and taking the point clouds in the kth small grid as the point cloudsCalculation of P k Barycentric coordinates +.>Recalculating the points within the small grid to +.>And taking the point with the smallest distance as the reduced point in the grid, and deleting all the rest points.
And S4, the simplified data is used as a test model, a design model of a flange sealing surface of a standard nuclear power main pump is used as a reference model, and an ADF algorithm is adopted to match the test model to the reference model, so that point cloud-model matching is completed.
The step S5 includes defect position identification: after the point cloud is matched, the plane of the non-defect area is spliced to the plane of the design model, and the defect part is positioned above or below the plane of the design model, so that the deviation value of the defect area can be obtained through 3D comparison.
The defect depth size in S5 is calculated as follows:
let the defect area to be calculated be A and the 3D point cloud error set in the area A be D= { D 1 ,d 2 ,...,d n For a defect to be calculated, the defect depth is h=max { d= { |d } 1 |,|d 2 |,...,|d n |}}。
Defect area size calculation:
for the error set d= { D 1 ,d 2 ,...,d n Setting the measurement error of the point cloud as delta d, when the error d i When the data is more than delta D, the data is a defect part, and the quantity of point cloud data of the defect part obtained by traversing the set D is D= { D 1 ,d 2 ,...,d m The point cloud of the defect part is T= { T after triangle gridding 1 ,t 2 ,...,t n },t i ＝{p j ,p k ,p l N is the number of triangular meshes, and the ith triangular mesh contains three vertexes p j ,p k ,p l The total area of the triangular mesh is
Wherein the method comprises the steps of
e i ＝(a i +b i +c i )/2
The point clouds are basically equidistant between the points after being reduced, and the area of the defect area is obtained according to the point cloud quantity ratio of the defect area to be S 1 ＝mS/n。
The invention has the beneficial effects that: 1) According to the invention, the amount of point cloud data can be effectively reduced by adopting uniform sampling, the sampled point cloud is uniformly distributed, the problem of uneven distribution density of the point cloud data in the measurement overlapping area can be solved, the uniformly sampled point cloud can completely reflect the outline characteristics of a model, enough point cloud data can be reserved in the area with unchanged curvature such as the plane and the circular arc surface of the sealing groove on the flange sealing surface of the main pump, and the accuracy of measuring and calculating the shape and position errors such as flatness and roundness can be ensured. 2) The method integrates the ADF algorithm into the scanning software, and can effectively improve the efficiency and stability of matching the sealing groove measurement point cloud with the design model. 3) By comparing the collected point cloud data with the standard three-dimensional model data, the shape of the shape surface and the defects on the shape surface can be displayed, and the defect identification and shape surface detection of the complex shape surface are realized.
Drawings
FIG. 1 is a schematic diagram of a nuclear power main pump flange surface defect identification positioning and shape detection system;
fig. 2 is a schematic diagram of a coordinate system of a robot measurement system.
In the figure: 1 robot, 2 scanner, 3 main bolt holes, 4 support posts, 5 nuclear main pump flange seal profiles.
Detailed Description
The invention will be described in further detail with reference to the accompanying drawings and specific examples.
Therefore, the three-dimensional intelligent detection technology of the nuclear power in-service part robot is researched, the on-line automatic detection intelligent equipment is developed, and the technical problems that the existing manual overhaul mode is low in detection efficiency, poor in measurement reliability, harmful to nuclear radiation and the like are fundamentally solved. The complex shape surface defect identification positioning and shape detection method based on point cloud matching is a key technology in three-dimensional intelligent detection.
The method for identifying, positioning and detecting the defects of the complex surface based on the point cloud matching can realize the identification, positioning and detecting the defects of the complex surface of the nuclear main pump flange sealing structure in the three-dimensional intelligent detection process of the nuclear power in-service parts. The method is suitable for in-service parts such as a nuclear power main pump flange sealing surface and the like. The method comprises the following steps: carrying out multi-angle data acquisition on the flange sealing surface of the main pump by a robot carrying grating type area array scanner; forming complete point cloud data of the sealing surface by splicing multi-angle and multi-azimuth scanning results; simplifying and processing data acquired by the system; establishing reference point cloud data for a flange sealing surface of a standard nuclear power main pump; matching the simplified data with a reference point cloud by adopting a point cloud matching algorithm, and unifying the point cloud data and a design model into a coordinate system; and comparing the produced point cloud data with a design model, and then identifying and detecting the defects.
And establishing a robot measurement system coordinate system, wherein the robot measurement system coordinate system comprises a geodetic coordinate system, a default tool coordinate system (a robot end flange coordinate system), a scanner measurement coordinate system and a workpiece coordinate system. In order to study the multi-view measurement data fusion of the robot, a scanner measurement coordinate system and a marking point global coordinate system are mainly considered.
As shown in fig. 2, { S } is the scanner measurement coordinate system, { G } is the marker global coordinate system, and the marker point set is denoted as p= { P 1,2,3,...i,...,n The set of points obtained by scanning the surface of the workpiece under test is denoted as c= { C } 1,2,3,...,i,...,n }。
And (3) establishing a global mark point model, and recording a transformation matrix from a single mark point coordinate system to the global mark point coordinate system in the primary measurement process, so that no mark point splicing in the repeated measurement process is realized. Specifically, a global marker point model is established through photogrammetry, and a global marker point set P is obtained. When scanning is performed for the first time, a marked point set acquired by the scanner in the initial posture is set as P 0 ＝{p 01,02,03,...,0k,...,i Then (V) isIf P 0 Expressed in the scanner measurement coordinate system { S }, as S P 0 Expressed as under the global coordinate system { G } of the mark points G P 0 P is then 0 Any marking point p in 0k Expressed in the scanner measurement coordinate system { S }, as S p 0k Expressed as under the global coordinate system { G } of the mark points G p 0k . Let the transformation matrix of the coordinate system { S } to { G } be +.>Then there are:
in the transformation matrixCan be based on point sets S P 0 And G P 0 and solving by an ICP matching algorithm.
Recording scanner acquisition under initial postureThe collected single point cloud is c j The following steps are:
to sum up, to solve the obtained transformation matrixIs a bridge, and can finish the measurement point cloud under the measurement coordinate system { S }, of the scanner S Measurement point cloud under global coordinate system { G } from C to mark point G And C, coordinate transformation, namely realizing multi-view measurement point cloud splicing of the unmarked points.
And comparing the produced point cloud data with a design model, and then identifying and detecting the defects.
Specifically, the preprocessed measurement point cloud is used as a test model, the designed 3D model is used as a reference model, an error chromatogram of the workpiece to be tested can be obtained by matching the test model with the reference model, an error display rule is determined based on the wavelength of the color, namely the color with the short wavelength (such as purple) shows small error, the color with the long wavelength (such as red) shows large error, and the defect position of the complex surface can be identified after the error threshold is determined. The point cloud of the defect can be obtained through frame selection of the identified defects, the point cloud of the defect is converted into triangular grids, and the defect area can be further calculated, so that the shape detection of the workpiece to be detected is completed.
As shown in FIG. 1, the three-dimensional intelligent detection system for nuclear power in-service parts applied by the invention comprises: the device comprises a support frame, a 6-degree-of-freedom robot, a three-dimensional scanner and the like. The support frame is arranged on the main pump flange surface of the nuclear power to be detected and positioned through the main pump flange sealing surface; the 6-degree-of-freedom robot is arranged on the support frame; the three-dimensional scanner is arranged at the tail end of the 6-degree-of-freedom robot, and the full coverage of the flange sealing surface is realized through the robot.
The complex shape surface defect identification positioning and shape detection method based on point cloud matching comprises the following steps:
s1: and the control robot sends the three-dimensional scanner to the upper part of the flange sealing surface, scans the sealing surface according to a planned path to acquire data, and scans in multiple angles and multiple directions.
S2: and forming the complete point cloud data of the sealing surface by splicing multi-angle and multi-azimuth scanning results.
How to splice multi-angle multi-azimuth scanning results
The robot carries the scanner and can acquire multiple groups of point cloud data after completing multi-angle scanning, and the acquired multiple groups of point cloud data are uniformly expressed under a robot base coordinate system based on the result of hand-eye calibration (the pose transformation relation of the scanner relative to the end flange of the robot) and multiple groups of internal joint parameters corresponding to the robot under multiple poses, so that multi-angle multi-azimuth scanning data splicing and fusion are completed. The specific process is as follows:
the robot is operated to scan the flange surface from different poses, assuming that the flange surface is represented in { B }, as B The flange face you and the position after the j-th scan are expressed in { S }, as S p j Then
Wherein { B } represents the robot base coordinate system,for the transformation matrix from the terminal coordinate system to the base coordinate system determined by the joint parameters in the robot during the jth scan, the robot operating system directly calculates +.>And (5) representing a hand-eye calibration matrix with unchanged parameters in the scanning process, scanning the flange surface for multiple times, and fitting the flange surface position to obtain a coordinate transformation equation.
S3: and denoising, simplifying and the like are carried out on the collected point cloud data.
Specific process of denoising and simplifying
European segmentation-based point cloud denoising:
if a point p in a group of point clouds i E P and another set of points P j If the minimum distance of e P is greater than d, then the two point clouds belong to two different sets. The Euclidean segmentation can be completed by calculating a distance value by using a nearest neighbor query to acquire the nearest neighbor point of each point through the kd-tree. The European segmentation results are that a plurality of point cloud sets are obtained, the largest point cloud set is taken as a final result, and the rest point clouds are all deleted, so that the denoised point clouds are obtained.
Wherein P represents a point cloud set, P i And p j Respectively represents any point in the point cloud set P, and d represents two points P i And p j Distance between them.
Point cloud reduction based on grid division:
and calculating the projection of the point cloud on the coordinate axis, and constructing a minimum bounding box by taking the distance difference values of the point cloud in three directions. If the grid interval to be divided is d', dividing the bounding box into a plurality of small grids, and setting the point cloud in the kth small grid asCalculation of P k Barycentric coordinates +.>Recalculating the points within the small grid to +.>And taking the point with the smallest distance as the reduced point in the grid, and deleting all the rest points.
Wherein P is k Representing the set of point clouds within the kth small grid,representing a set of point clouds P k S represents a point cloud set P k The number of midpoints>Representing a set of point clouds P k The mean of all the point coordinates in the graph.
S4: and matching the simplified data with a standard nuclear power main pump flange sealing surface reference point cloud by adopting a point cloud matching algorithm, and unifying the point-collected cloud data and a designed standard model into a coordinate system.
Specific procedure of this step
And (3) taking the simplified data as a test model, taking a design model of a flange sealing surface of a standard nuclear power main pump as a reference model, and adopting an ADF algorithm to match the test model to the reference model so as to finish point cloud-model matching.
S5: comparing the generated 3D point cloud with a standard model, identifying the shape surface defect through comparison, and calculating the position and the size of the selected defect through selecting the defect area by a frame.
A specific calculation process.
Defect position identification:
after the point cloud is matched, the plane of the non-defect area is spliced to the plane of the design model, and the defect part is positioned above or below the plane of the design model, so that the deviation value of the defect area can be obtained through 3D comparison.
Defect depth dimension calculation:
let the defect area to be calculated be A and the 3D point cloud error set in the area A be D= { D 1 ,d 2 ,...,d n For a defect to be calculated, the defect depth is h=max { d= { |d } 1 |,|d 2 |,...,|d n |}}。
d 1 、d 2 、d n And respectively representing the distance deviation values of the 1 st, the 2 nd and the n th points in the point cloud to the reference model.
Defect area size calculation:
for the error set d= { D 1 ,d 2 ,...,d n Setting the measurement error of the point cloud as delta d, when the error d i When the data is more than delta D, the data is a defect part, and the quantity of point cloud data of the defect part obtained by traversing the set D is D= { D 1 ,d 2 ,...,d m }. The point cloud with the defect part is T= { T after triangle gridding 1 ,t 2 ,...,t n },t i ＝{p j ,p k ,p l N is the number of triangular meshes, and the ith triangular mesh contains three vertexes p j ,p k ,p l . The total area of the triangular mesh is
Wherein the method comprises the steps of
e i ＝(a i +b i +c i )/2
p jx 、p jy 、p jz Respectively representing x, y and z coordinates, p of the jth point in the point cloud set kx 、p ky 、p kz Respectively representing x, y and z coordinates, p of kth point in point cloud set lx 、p ly 、p lz Respectively representing x, y and z coordinates of a first point in the point cloud set; a, a i 、b i 、c i Respectively represent the side lengths of three sides of the ith triangular mesh, e i Representing half the perimeter of the ith triangular mesh.
Because the point clouds are basically equidistant between the points after being reduced, the area of the defect area can be obtained according to the point cloud quantity ratio of the defect area to be S 1 ＝mS/n。
Claims (7)
1. The complex shape surface defect identification positioning and shape detection method based on point cloud matching is characterized by comprising the following steps of:
s1: the control robot sends the three-dimensional scanner to the position above the flange sealing surface, and scans the sealing surface according to a planned path to acquire data;
s2: forming complete point cloud data of the sealing surface by splicing the scanning results;
s3: denoising and simplifying the collected point cloud data;
s4: matching the simplified data with a standard nuclear power main pump flange sealing surface reference point cloud by adopting a point cloud matching algorithm, and unifying the point-collected cloud data and a designed standard model into a coordinate system;
s5: comparing the generated 3D point cloud with a standard model, identifying the shape surface defect through comparison, and calculating the position and the size of the selected defect through selecting the defect area by a frame.
2. The complex surface defect identification positioning and shape detection method based on point cloud matching as claimed in claim 1, wherein the method is characterized in that: s2 include that the robot carries on the scanner and can acquire the multiunit point cloud data after accomplishing the multi-angle scanning, the multiunit point cloud data that will acquire based on the result of the position appearance transformation relation of scanner relative to robot terminal flange and the multiunit internal joint parameter that the robot corresponds under the multiaspect is unified and is represented under robot base coordinate system, accomplish multi-angle diversified scanning data concatenation and fusion.
3. The complex surface defect recognition positioning and shape detection method based on point cloud matching as set forth in claim 1, wherein the denoising process in S3 is as follows:
if a point p in a group of point clouds i E P and another set of points P j And E, if the minimum distance of the P is greater than d, the two point clouds belong to two different sets, the nearest neighbor points of each point are obtained through kd-tree by using nearest neighbor query to calculate the distance value, so that Euclidean segmentation is completed, the result of the Euclidean segmentation is that a plurality of point cloud sets are obtained, the largest point cloud set is taken as a final result, and the rest point clouds are all deleted, so that the denoised point clouds are obtained.
4. The method for identifying, positioning and detecting the defects of the complex surface based on the point cloud matching as set forth in claim 1, wherein the point cloud compacting process in S3 is as follows:
calculating the projection of the point cloud on the coordinate axis, constructing a minimum bounding box by taking the distance difference values of the point cloud in three directions, dividing the bounding box into a plurality of small grids by taking the grid interval to be divided as d, and taking the point cloud in the kth small grid asCalculation of P k Barycentric coordinates +.>Recalculating the points within the small grid to +.>And taking the point with the smallest distance as the reduced point in the grid, and deleting all the rest points.
5. The complex surface defect identification positioning and shape detection method based on point cloud matching as claimed in claim 1, wherein the method is characterized in that: and S4, the simplified data is used as a test model, a design model of a flange sealing surface of a standard nuclear power main pump is used as a reference model, and an ADF algorithm is adopted to match the test model to the reference model, so that point cloud-model matching is completed.
6. The complex surface defect identification positioning and shape detection method based on point cloud matching as claimed in claim 1, wherein the method is characterized in that: the step S5 includes defect position identification: after the point cloud is matched, the plane of the non-defect area is spliced to the plane of the design model, and the defect part is positioned above or below the plane of the design model, so that the deviation value of the defect area can be obtained through 3D comparison.
7. The method for identifying, positioning and detecting defects of complex surface based on point cloud matching as set forth in claim 1, wherein the depth dimension of the defects in S5 is calculated as follows:
let the defect area to be calculated be A and the 3D point cloud error set in the area A be D= { D 1 ,d 2 ,...,d n For a defect to be calculated, the defect depth is h=max { d= { |d } 1 |,|d 2 |,...,|d n |}}。
Defect area size calculation:
for the error set d= { D 1 ,d 2 ,...,d n Setting the measurement error of the point cloud as delta d, when the error d i When the data is more than delta D, the data is a defect part, and the quantity of point cloud data of the defect part obtained by traversing the set D is D= { D 1 ,d 2 ,...,d m The point cloud of the defect part is T= { T after triangle gridding 1 ,t 2 ,...,t n },t i ＝{p j ,p k ,p l N is the number of triangular meshes, and the ith triangular mesh contains three vertexes p j ,p k ,p l The total area of the triangular mesh is
Wherein the method comprises the steps of
e i ＝(a i +b i +c i )/2
The point clouds are basically equidistant between the points after being reduced, and the area of the defect area is obtained according to the point cloud quantity ratio of the defect area to be S 1 ＝mS/n。
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117788476A (en) * | 2024-02-27 | 2024-03-29 | 南京邮电大学 | Workpiece defect detection method and device based on digital twin technology |
| CN117788476B (en) * | 2024-02-27 | 2024-05-10 | 南京邮电大学 | Workpiece defect detection method and device based on digital twin technology |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117788476A (en) * | 2024-02-27 | 2024-03-29 | 南京邮电大学 | Workpiece defect detection method and device based on digital twin technology |
| CN117788476B (en) * | 2024-02-27 | 2024-05-10 | 南京邮电大学 | Workpiece defect detection method and device based on digital twin technology |
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