CN111426282B - Method for identifying sealing surface error evaluation defects of optical measurement point cloud - Google Patents
Method for identifying sealing surface error evaluation defects of optical measurement point cloud Download PDFInfo
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- CN111426282B CN111426282B CN201811574410.1A CN201811574410A CN111426282B CN 111426282 B CN111426282 B CN 111426282B CN 201811574410 A CN201811574410 A CN 201811574410A CN 111426282 B CN111426282 B CN 111426282B
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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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract
The invention relates to the technical field of sealing surface measurement defect identification, and particularly discloses a sealing surface error evaluation defect identification method of optical measurement point cloud. The method comprises the following steps: 1. collecting point cloud data of a flange sealing surface of a main pump; 2. preprocessing point cloud data; 5. performing defect identification according to the point cloud data of the flange sealing surface of the main pump; 5.1, establishing an error chromatogram; 5.2, obtaining a closed region enclosed by the pixel points with gradient transformation exceeding a threshold value by utilizing the color transformation gradient of the error chromatogram, and realizing automatic identification of the defects of the sealing surface. The method overcomes the defect that the existing measuring method can not realize in-situ, high-precision and high-efficiency automatic measurement; meanwhile, the 2D size error and the roundness/flatness error of the part are automatically evaluated based on the measured original point cloud data, the surface defect is identified, the data processing and error evaluation processes are simplified, the efficiency and the precision are improved, and the defect that the depth information is difficult to obtain by the traditional method is overcome.
Description
Technical Field
The invention belongs to the technical field of sealing surface measurement defect identification, and particularly relates to a sealing surface error evaluation defect identification method of optical measurement point cloud.
Background
The flange sealing surface is used as a pressure bearing boundary of main equipment of a pressure vessel of a nuclear power plant, and form and position, size errors and surface defects of the sealing surface need to be periodically overhauled. The main failure modes of the sealing surface of the flange comprise micro-deformation in shape and size caused by thermal stress and mechanical stress impact, and defects such as peeling, pits, scratches and the like caused by hydraulic corrosion or mechanical damage in the assembling and disassembling process.
The traditional detection mode adopts special diameter microcallipers, plug gauges and the like to measure shape/size errors, adopts naked eyes to identify surface defects, and is low in detection efficiency, large in random factor influence, poor in reliability and not beneficial to guarantee of sealing performance. Meanwhile, only data measurement such as diameter, depth and the like can be formed, and a sealing surface measurement historical information base which is visual and has clear contrast cannot be established.
In order to overcome the defects of the detection mode, an optical non-contact Measurement technology based on machine vision appears in the prior art, wherein a grating type area array scanner is a widely used optical Measurement mode, the scanner adopts a binocular pmp (phase Measurement profile) three-dimensional Measurement technology based on a triangulation principle, a human vision principle is simulated, and a method of passively sensing distance by using a computer is adopted. Observing an object from two points by using a double camera, projecting reference gratings with different phase differences to the measured object through a projector, acquiring deformed grating images modulated by the surface of the object from two visual angles, calculating a phase value of each pixel point by using the deformed gratings, and calculating a three-dimensional point cloud coordinate of the object according to the phase value. However, the single-amplitude measurement range of the scanner is limited, and the measurement of multiple areas or complete appearance of the workpiece to be measured needs to be completed with the assistance of corresponding motion platforms. The six-degree-of-freedom industrial robot has high flexibility and is widely applied to the manufacturing industry, the tail end of the six-degree-of-freedom industrial robot can reach an appointed position in any posture in a flexible space, and a workpiece clamped by the tail end of the six-degree-of-freedom industrial robot can freely move in the measuring range of the on-line laser scanning sensor, so that the complete measurement of the measured workpiece is realized.
The point cloud is an image expression of a large amount of dense scattered point data containing various information acquired by a three-dimensional optical scanning technology, the point cloud data acquired by a robot optical measurement mode is large in scale, generally tens of millions, and the large-scale measurement point cloud contains rich three-dimensional shape information of a measurement object surface, but the point cloud is used as the most original measurement data and is usually disordered, and if required and effective size and error data are extracted from the point cloud, a certain method is needed for carrying out data processing on the point cloud. The existing point cloud processing method has the general functions of point cloud display, point cloud deletion, simplification, point cloud three-dimensional model matching, error chromatography display and the like, but lacks the special functions of two-dimensional size error calculation, roundness and flatness form and position error calculation and defect identification, and cannot meet the requirements of main pump flange sealing surface error evaluation and defect identification.
Disclosure of Invention
The invention aims to provide a method for identifying the error evaluation defect of a sealing surface of an optical measurement point cloud, which solves the problems of automatic detection, error analysis and defect identification of a flange sealing surface of a main pump, realizes the establishment of a sealing digital measurement database and achieves the aim of ensuring the safe and reliable operation of a nuclear power station.
The technical scheme of the invention is as follows: a method for identifying the error evaluation defect of a sealing surface of an optical measurement point cloud specifically comprises the following steps:
step 1, collecting point cloud data of a flange sealing surface of a main pump;
step 2, preprocessing point cloud data;
step 2.1, carrying out simplification processing on the complete three-dimensional point cloud;
step 2.2, removing isolated points from the point cloud data;
step 2.3, matching the point cloud with the design model;
step 5, identifying defects according to point cloud data of a flange sealing surface of the main pump;
step 5.1, establishing an error chromatogram;
and 5.2, obtaining a closed region enclosed by the pixel points of which the gradient transformation exceeds a threshold value by utilizing the color transformation gradient of the error chromatogram, and realizing automatic identification of the defects of the sealing surface.
The method further comprises a step 3 of performing 2D dimensional error calculation as follows:
generating a penetrating object section under a design model coordinate system, acquiring corresponding pair of 2D section point clouds, measuring the actual depth, diameter and size of the main pump flange sealing surface point cloud on the 2D section, comparing the actual depth, diameter and size corresponding to the design model, and automatically judging whether the actual depth exceeds the tolerance range.
The method also comprises a step 4.1 of calculating the roundness error, which comprises the following steps:
step 4.1, measuring the roundness error of the point cloud data;
step 4.1.1, establishing a horizontal section and intercepting a point cloud of a circumferential section;
step 4.1.2, fitting an ideal circle by using a least square method;
and 4.1.3, measuring the roundness error of the point cloud data according to a containment principle.
The method also includes a step 4.2 of performing flatness error calculation as follows:
step 4.2, measuring the flatness error of the point cloud data;
step 4.2.1, generating a circular frame selection area by taking the annular circle center as a central point, and automatically selecting a sealing surface plane point cloud;
step 4.2.2, fitting an ideal plane by using a least square method;
and 4.2.3, measuring the flatness error of the point cloud data according to a containment principle.
The specific steps of establishing the error chromatogram in the step 5.1 are as follows:
after the test point cloud of the flange sealing surface of the main pump is matched and aligned with the design model, the closest distance between each point and the design model is calculated, the distance is represented by colors, and an error chromatogram is formed.
The step 5.2 specifically comprises the following steps:
and selecting an approximate area with defects according to the error chromatogram, calculating the color transformation gradient of the error chromatogram by using an image segmentation algorithm, considering that the surface of the sealing surface has sudden change when a threshold value is set in the process of color transformation gradient, recording the pixel point, and identifying the closed area enclosed by the pixel point with gradient change exceeding the threshold value as the surface defect, thereby realizing the automatic identification of the sealing surface defect.
The specific process of collecting the point cloud data of the flange sealing surface of the main pump in the step 1 comprises the following steps:
the six-degree-of-freedom industrial robot is used for clamping the area array scanner, and the fact that the grating projected by the area array scanner can cover the measured surface of the sealing surface of the flange of the main pump is guaranteed; the robot drives the scanner to move along a preset track, the area to be measured of the flange sealing surface of the main pump is scanned and measured, and complete three-dimensional point cloud data of the area to be measured of the flange sealing surface of the main pump is obtained.
The step 2 specifically comprises:
step 2.1, carrying out simplification processing on the complete three-dimensional point cloud;
the large-scale measurement point cloud obtained by the initial measurement of the grating area array scanner driven by the robot is uniformly sampled, the point cloud data is simplified, and the data processing efficiency is improved;
step 2.2, removing isolated points from the point cloud data;
deleting isolated points and noise points in the measured point cloud data;
step 2.3, matching the point cloud with the design model;
and matching and aligning the measurement point cloud and the design model to the same coordinate system.
The invention has the following remarkable effects: the invention relates to a method for identifying the defect of the error evaluation of a sealing surface of an optical measurement point cloud, which overcomes the defect that the existing measurement methods such as manual measurement of a caliper/plug gauge and offline measurement of a three-coordinate measuring machine cannot realize in-situ, high-precision and high-efficiency automatic measurement; the flexible motion platform can be used for driving the optical scanner to realize one-key automatic in-situ measurement, so that the measurement efficiency is greatly improved, and meanwhile, the measurement precision is higher; meanwhile, 2D size errors and roundness/flatness errors of the parts are automatically evaluated based on the measured original point cloud data, surface defects are identified, the data processing and error evaluation processes are greatly simplified, and the efficiency and the precision are improved; the method can be used for identifying the depth information of the surface defect and overcomes the defect that the traditional method is difficult to acquire the depth information.
Drawings
FIG. 1 is a flow chart of a sealing surface error evaluation defect identification method of an optical measurement point cloud according to the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the embodiments.
As shown in fig. 1, a method for identifying a defect in a sealing surface error evaluation of an optical measurement point cloud includes the following steps:
step 1, collecting point cloud data of a flange sealing surface of a main pump;
the six-degree-of-freedom industrial robot is used for clamping the area array scanner, and the fact that the grating projected by the area array scanner can cover the measured surface of the sealing surface of the flange of the main pump is guaranteed; the robot drives a scanner to move along a preset track, the area to be measured of the flange sealing surface of the main pump is scanned and measured, and complete three-dimensional point cloud data of the area to be measured of the flange sealing surface of the main pump is obtained;
step 2, preprocessing point cloud data;
step 2.1, carrying out simplification processing on the complete three-dimensional point cloud;
the large-scale measurement point cloud obtained by the initial measurement of the grating area array scanner driven by the robot is uniformly sampled, the point cloud data is simplified, and the data processing efficiency is improved;
step 2.2, removing isolated points from the point cloud data;
deleting isolated points and noise points in the measured point cloud data;
step 2.3, matching the point cloud with the design model;
matching and aligning the measurement point cloud and the design model to the same coordinate system;
step 3, calculating 2D size error;
generating a penetrating object section under a design model coordinate system, acquiring corresponding pairs of 2D section point clouds, measuring the actual relative sizes of the depth and the diameter of the main pump flange sealing surface point clouds on the 2D section, comparing the actual relative sizes with the relative sizes corresponding to the design model, and automatically judging whether the actual relative sizes exceed the tolerance range;
step 4, calculating the shape and position errors of the roundness or the flatness;
step 4.1, measuring the roundness error of the point cloud data;
step 4.1.1, establishing a horizontal section and intercepting a point cloud of a circumferential section;
step 4.1.2, fitting an ideal circle by using a least square method;
step 4.1.3, measuring the roundness error of the point cloud data according to a containment principle;
step 4.2, measuring the flatness error of the point cloud data;
step 4.2.1, generating a circular frame selection area by taking the annular circle center as a central point, and automatically selecting a sealing surface plane point cloud;
step 4.2.2, fitting an ideal plane by using a least square method;
4.2.3, measuring the flatness error of the point cloud data according to a containment principle;
step 5, identifying defects according to point cloud data of a flange sealing surface of the main pump;
step 5.1, establishing an error chromatogram;
after matching and aligning the test point cloud of the flange sealing surface of the main pump with the design model, calculating the closest distance from each point to the design model, and expressing the size of the distance by color to form an error chromatogram;
step 5.2, obtaining a closed region enclosed by pixel points with gradient transformation exceeding a threshold value by utilizing the color transformation gradient of the error chromatogram, and realizing automatic identification of the defects of the sealing surface;
and selecting an approximate area with defects according to the error chromatogram, calculating the color transformation gradient of the error chromatogram by using an image segmentation algorithm, considering that the surface of the sealing surface has sudden change when a threshold value is set in the process of color transformation gradient, recording the pixel point, and identifying the closed area enclosed by the pixel point with gradient change exceeding the threshold value as the surface defect, thereby realizing the automatic identification of the sealing surface defect.
Claims (6)
1. A method for identifying the error evaluation defect of a sealing surface of an optical measurement point cloud is characterized by comprising the following steps: the method specifically comprises the following steps:
step 1, collecting point cloud data of a flange sealing surface of a main pump;
step 2, preprocessing point cloud data;
step 2.1, carrying out simplification processing on the complete three-dimensional point cloud;
step 2.2, removing isolated points from the point cloud data;
step 2.3, matching the point cloud with the design model;
step 4.2, measuring the flatness error of the point cloud data;
step 4.2.1, generating a circular frame selection area by taking the annular circle center as a central point, and automatically selecting a sealing surface plane point cloud;
step 4.2.2, fitting an ideal plane by using a least square method;
4.2.3, measuring the flatness error of the point cloud data according to a containment principle;
step 5, identifying defects according to point cloud data of a flange sealing surface of the main pump;
step 5.1, establishing an error chromatogram;
step 5.2, obtaining a closed region surrounded by pixel points of which gradient transformation exceeds a threshold value by utilizing the color transformation gradient of the error chromatogram, and realizing automatic identification of the defects of the sealing surface;
and selecting an approximate area with defects according to the error chromatogram, calculating and obtaining the color transformation gradient of the error chromatogram by using an image segmentation algorithm, considering that the surface of the sealing surface has sudden change when the color transformation gradient exceeds a set threshold, recording the pixel point, and identifying the closed area surrounded by the pixel points with gradient changes exceeding the threshold as the surface defects, thereby realizing the automatic identification of the sealing surface defects.
2. The method for identifying the defect of the optical measurement point cloud in the sealing surface error assessment according to claim 1, wherein: the method further comprises a step 3 of performing 2D dimensional error calculation as follows:
generating a penetrating object section under a design model coordinate system, acquiring corresponding pair of 2D section point clouds, measuring the actual depth and diameter pair size of the main pump flange sealing surface point clouds on the 2D section, comparing the actual depth and diameter pair size with the corresponding pair size of the design model, and automatically judging whether the actual depth and diameter pair size exceeds the tolerance range.
3. The method for identifying the defect of the optical measurement point cloud in the sealing surface error assessment according to claim 1, wherein: the method also comprises a step 4.1 of calculating the roundness error, which comprises the following steps:
step 4.1, measuring the roundness error of the point cloud data;
step 4.1.1, establishing a horizontal section and intercepting a point cloud of a circumferential section;
step 4.1.2, fitting an ideal circle by using a least square method;
and 4.1.3, measuring the roundness error of the point cloud data according to a containment principle.
4. The method for identifying the defect of the optical measurement point cloud in the sealing surface error assessment according to claim 1, wherein: the specific steps of establishing the error chromatogram in the step 5.1 are as follows:
after the test point cloud of the flange sealing surface of the main pump is matched and aligned with the design model, the closest distance between each point and the design model is calculated, the distance is represented by colors, and an error chromatogram is formed.
5. The method for identifying the defect of the optical measurement point cloud in the sealing surface error assessment according to claim 1, wherein: the specific process of collecting the point cloud data of the flange sealing surface of the main pump in the step 1 comprises the following steps:
the six-degree-of-freedom industrial robot is used for clamping the area array scanner, and the fact that the grating projected by the area array scanner can cover the measured surface of the sealing surface of the flange of the main pump is guaranteed; the robot drives the scanner to move along a preset track, the area to be measured of the flange sealing surface of the main pump is scanned and measured, and complete three-dimensional point cloud data of the area to be measured of the flange sealing surface of the main pump is obtained.
6. The method for identifying the defect of the optical measurement point cloud in the sealing surface error assessment according to claim 1, wherein: the step 2 specifically comprises:
step 2.1, carrying out simplification processing on the complete three-dimensional point cloud;
the large-scale measurement point cloud obtained by the initial measurement of the grating area array scanner driven by the robot is uniformly sampled, the point cloud data is simplified, and the data processing efficiency is improved;
step 2.2, removing isolated points from the point cloud data;
deleting isolated points and noise points in the measured point cloud data;
step 2.3, matching the point cloud with the design model;
and matching and aligning the measurement point cloud and the design model to the same coordinate system.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001091234A (en) * | 1999-09-20 | 2001-04-06 | Sankyu Inc | Deformation-measuring device and measuring method for flange surface |
| CN101706257A (en) * | 2009-12-04 | 2010-05-12 | 重庆建设摩托车股份有限公司 | Method for detecting product produced by using mold and provided with irregularly-shaped inner hole or channel |
| CN101876536A (en) * | 2009-04-29 | 2010-11-03 | 鸿富锦精密工业（深圳）有限公司 | Three-dimensional color scale comparison dynamic analysis method |
| CN102999916A (en) * | 2012-12-12 | 2013-03-27 | 清华大学深圳研究生院 | Edge extraction method of color image |
| CN103673916A (en) * | 2012-09-06 | 2014-03-26 | 上海船舶工艺研究所 | On-line detection method for line heating forming |
| CN103760240A (en) * | 2014-01-28 | 2014-04-30 | 龙源（北京）风电工程技术有限公司 | Automatic detecting device for defects of flange and detecting method thereof |
| CN203894198U (en) * | 2014-05-16 | 2014-10-22 | 甘肃蓝科石化高新装备股份有限公司 | Phased array detecting and scanning frame for flange sealing surface corrosion defect |
| CN104422422A (en) * | 2013-08-30 | 2015-03-18 | 鸿富锦精密工业（深圳）有限公司 | Product profile deformation analysis system and method |
| CN104422406A (en) * | 2013-08-30 | 2015-03-18 | 鸿富锦精密工业（深圳）有限公司 | Planeness measurement system and method |
| CN105526882A (en) * | 2015-12-28 | 2016-04-27 | 西南交通大学 | Turnout wear detection system and detection method based on structured light measurement |
| CN106170677A (en) * | 2014-02-11 | 2016-11-30 | 易兹镭射公司 | For measuring the method and system of the geometric jacquard patterning unit surface characteristic of flange surface |
| CN106959078A (en) * | 2017-02-28 | 2017-07-18 | 苏州凡目视觉科技有限公司 | A kind of contour measuring method for measuring three-dimensional profile |
| CN108120394A (en) * | 2017-12-20 | 2018-06-05 | 大连交通大学 | The high-speed train curved face quality determining method of flexibility |
| CN108682012A (en) * | 2018-05-15 | 2018-10-19 | 佛山市南海区广工大数控装备协同创新研究院 | A kind of 3D bend glass profile pattern defect inspection methods for sweeping laser based on line |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP6774793B2 (en) * | 2016-06-24 | 2020-10-28 | 株式会社キーエンス | Three-dimensional measuring device |
-
2018
- 2018-12-21 CN CN201811574410.1A patent/CN111426282B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2001091234A (en) * | 1999-09-20 | 2001-04-06 | Sankyu Inc | Deformation-measuring device and measuring method for flange surface |
| CN101876536A (en) * | 2009-04-29 | 2010-11-03 | 鸿富锦精密工业（深圳）有限公司 | Three-dimensional color scale comparison dynamic analysis method |
| CN101706257A (en) * | 2009-12-04 | 2010-05-12 | 重庆建设摩托车股份有限公司 | Method for detecting product produced by using mold and provided with irregularly-shaped inner hole or channel |
| CN103673916A (en) * | 2012-09-06 | 2014-03-26 | 上海船舶工艺研究所 | On-line detection method for line heating forming |
| CN102999916A (en) * | 2012-12-12 | 2013-03-27 | 清华大学深圳研究生院 | Edge extraction method of color image |
| CN104422422A (en) * | 2013-08-30 | 2015-03-18 | 鸿富锦精密工业（深圳）有限公司 | Product profile deformation analysis system and method |
| CN104422406A (en) * | 2013-08-30 | 2015-03-18 | 鸿富锦精密工业（深圳）有限公司 | Planeness measurement system and method |
| CN103760240A (en) * | 2014-01-28 | 2014-04-30 | 龙源（北京）风电工程技术有限公司 | Automatic detecting device for defects of flange and detecting method thereof |
| CN106170677A (en) * | 2014-02-11 | 2016-11-30 | 易兹镭射公司 | For measuring the method and system of the geometric jacquard patterning unit surface characteristic of flange surface |
| CN203894198U (en) * | 2014-05-16 | 2014-10-22 | 甘肃蓝科石化高新装备股份有限公司 | Phased array detecting and scanning frame for flange sealing surface corrosion defect |
| CN105526882A (en) * | 2015-12-28 | 2016-04-27 | 西南交通大学 | Turnout wear detection system and detection method based on structured light measurement |
| CN106959078A (en) * | 2017-02-28 | 2017-07-18 | 苏州凡目视觉科技有限公司 | A kind of contour measuring method for measuring three-dimensional profile |
| CN108120394A (en) * | 2017-12-20 | 2018-06-05 | 大连交通大学 | The high-speed train curved face quality determining method of flexibility |
| CN108682012A (en) * | 2018-05-15 | 2018-10-19 | 佛山市南海区广工大数控装备协同创新研究院 | A kind of 3D bend glass profile pattern defect inspection methods for sweeping laser based on line |
Non-Patent Citations (1)
| Title |
|---|
| "法兰密封面三维光学检测系统设计";郝庆军 等;《机械与电子》;20180531;第36卷(第5期);第31-41页 * |
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