CN106124034A - Thin-wall part operation mode based on machine vision test device and method of testing - Google Patents
Thin-wall part operation mode based on machine vision test device and method of testing Download PDFInfo
- Publication number
- CN106124034A CN106124034A CN201610807568.3A CN201610807568A CN106124034A CN 106124034 A CN106124034 A CN 106124034A CN 201610807568 A CN201610807568 A CN 201610807568A CN 106124034 A CN106124034 A CN 106124034A
- Authority
- CN
- China
- Prior art keywords
- thin
- wall part
- industrial camera
- image
- measured
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H9/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by using radiation-sensitive means, e.g. optical means
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The present invention relates to a kind of thin-wall part operation mode based on machine vision test device and method of testing, thin-wall part operation mode based on the machine vision test device of the present invention includes industrial camera, light source, image collecting device and image processing apparatus；Described industrial camera and light source are placed in the front of thin-wall part to be measured, and the camera lens of industrial camera is towards the leading flank of thin-wall part to be measured, and the leading flank of thin-wall part to be measured is provided with multiple retroreflective feature point；Described industrial camera is connected with image capturing system by data wire；Image collecting device is connected with image processing apparatus.Thin-wall part operation mode based on the machine vision test apparatus structure of the present invention is flexible, need not the debugging process of complexity, low to surface of thin-walled parts prescription, and achieve non-cpntact measurement, do not change the vibration characteristics of thin-wall part to be measured, the operational modal parameter of thin-wall part, certainty of measurement and efficiency advantages of higher can be accurately measured.
Description
Technical field
The present invention relates to a kind of thin-wall part operation mode based on machine vision test device and method of testing.
Background technology
Thin-wall part, due to lightweight, structure efficiency advantages of higher, is widely used the most in the industrial production.But
Owing to the wall thickness of thin-wall part is the least relative to overall dimensions, and general rigidity is little, area big, so transporting in mechanical system
It is easy to during row vibrate, thus causes the interference between mechanical part, send vibration noise, or long-term at machinery
Motion in cause mechanical breakdown, excessive vibration even can cause destructive accident, therefore thin-wall part must be carried out mould
State is tested.
Mode is the natural vibration characteristic of frame for movement, and each mode has specific natural frequency, a damping ratio
With Mode Shape etc..These modal parameters can be by calculating or experimental analysis acquirement, such a calculating or analysis of experiments process
It is referred to as model analysis.This analysis process if being obtained by finite element method, the most referred to as computational modal analysis；If
Modal parameter is determined, referred to as experimental modal analysis by test acquisition system input and structure output；Cannot utilize for those
The measurand of artificial excitation or Unknown worm excitation size cannot be carried out experimental modal test, and referred to as operation mode divides
Analysis.Experimental modal test is use manual type excitation free state in the more satisfactory working environment of laboratory tested right
As, carry out Modal Parameter Identification with the activation sequence of the structural response collected and input for analysis foundation and then obtain mode ginseng
Number.Mainly sensed by acceleration touch sensor or use this contactless vibration-testing of laser doppler vialog
Device measures.Experimental modal method of testing is primarily present the limitation of two aspects, and one requires that measurand is freely
State, it two must be known input stimulus.Artificial excitation cannot be utilized for those or excitation size cannot be recorded
Measurand cannot be carried out experimental modal test, is necessary for being operated model analysis, and the advantage of this method is to have only to obtain
Taking output response data, it is not necessary to know input stimulus data, the vibration data collected derives from the work of structure actual vibration
Make environment, test result more meet reality boundary condition, more can reflect structure dynamic characteristic under actual operating conditions,
Have more engineering actual reference.
The method that acceleration transducer can also be used to be attached to object under test surface carries out the measurement of object mode, but by
In the feature that thin-wall part self lightweight wall is thin so that the method is difficult to that thin-wall part carries out a high-precision mode and measures.Separately
Outward, the mode for some labyrinth thin-wall parts is measured, and the spatial density of test may be had higher requirement, for terrible
To a high-resolution measurement result of the whole audience, it usually needs measure the Vibration Condition of multiple spot, thus must be at body surface
Arrange substantial amounts of sensor, and touch sensor is difficulty with the work of this respect.
Along with the development of laser doppler vibration measuring technology, this contactless vibration-testing of laser doppler vialog senses
Device has gradually been applied in the mode measurement of thin-wall part, solves sensor and thin-wall member produces the problem of additional mass,
But vibration measurement with laser early stage debugging alignment complexity, adds debugging time and difficulty；And higher to testee surface quality requirements,
In addition, also have some whole-field optically vibration measurement methods to can be applicable to the mould measurement of thin-wall part, as speckle-shearing deformation,
Electronic speckle mode-interference and holographic interference etc., although these methods have a good whole audience vibration measurement characteristic, but generally individually
Using laser as light source, light path is extremely complex, and measurement result is easily affected by extraneous vibration, is only used for possessing vibration isolation table
Scientific research measurement is carried out, it is difficult to meet the demand of operation mode test in laboratory.
In sum, the existing operation mode method of testing about thin-wall part can not meet the demand in market, at this
In the case of Zhong, for the operation mode test problem of thin-wall part, it is badly in need of one and will not increase additional mass, space to measured workpiece
High, the easy to operate method for fast measuring of resolution meets the needs of industrial development.
Summary of the invention
In order to solve above-mentioned technical problem, the present invention provide a kind of flexible for installation, debugging easily, measure the base of efficiently and accurately
Thin-wall part operation mode in machine vision tests device and method of testing, it is achieved that to the noncontact of thin-wall part operation mode,
High-acruracy survey.
Technical scheme is as follows: a kind of thin-wall part operation mode based on machine vision test device, including work
Industry camera, light source, image collecting device and image processing apparatus；Before described industrial camera and light source are placed in thin-wall part to be measured
Side, the camera lens of industrial camera is provided with multiple retroreflective feature towards the leading flank of thin-wall part to be measured, the leading flank of thin-wall part to be measured
Point；Described industrial camera is connected with image capturing system by data wire；Image collecting device is connected with image processing apparatus.
In above-mentioned thin-wall part operation mode based on machine vision test device, described industrial camera passes through spider
It is fixed on the dead ahead of thin-wall part to be measured.
In above-mentioned thin-wall part operation mode based on machine vision test device, described industrial camera uses
CCD industrial camera；Described light source uses LED light source.
In above-mentioned thin-wall part operation mode based on machine vision test device, spiral shell is passed through in one end of described thin-wall part
Bolt is fixed on base.
A kind of thin-wall part operation mode method of testing based on machine vision, comprises the steps:
1st step: ask for the relative pose relation of industrial camera and thin-wall part to be measured, and the principal point coordinate to industrial camera
(u0,v0), the equivalent focal length a of horizontal directionx, the equivalent focal length a of vertical directiony, distortion parameter γ demarcates；
2nd step: utilize power to hammer hammering thin-wall part to be measured into shape, utilize the full sequence figure of industrial camera this process of continuous acquisition
Picture, is sent to image processing apparatus by image collecting device, carries out sequence specific primers-polymerase chain reaction, extracts each retroreflective feature point
Vibration signal in units of pixel, according to the 1st step model and be translated into the vibration signal of effective unit；
3rd step: use stochastic subspace identification method to carry out mode the vibration information of the multiple retroreflective feature points extracted
Parameter identification, obtains the operational modal parameter of thin-wall part to be measured.
The present invention compared with prior art, has the advantages that
(1) thin-wall part operation mode based on the machine vision test device of the present invention, utilizes machine vision metrology technology
The displacement of the retroreflective feature point on tracking thin-wall part, the vibration information of multiple spot on available thin-wall part, and can be to the vibration of multiple spot
Information carries out Synchronization Analysis, and then available operational modal parameter, and it is high that the present invention has spatial resolution, and efficiently and accurately etc. is excellent
Point.
(2) the thin-wall part operation mode method of testing based on machine vision of the present invention does not affect the vibration spy of thin-wall part
Property, flexible structure, easy to operate；When solving laser scanning vibration measuring, debugging alignment complexity, high to surface of thin-walled parts prescription
Problem；And achieve non-cpntact measurement, do not change the vibration characteristics of thin-wall part to be measured, the work of thin-wall part can be accurately measured
Modal parameter,
Accompanying drawing explanation
Fig. 1 is the structural representation of thin-wall part operation mode based on the machine vision test device of the present invention.
Fig. 2 is the retroreflective feature point of present invention layout drawing on thin-wall part to be measured.
Fig. 3 is the outline dimensional drawing of the thin-wall part to be measured of the present invention.
Fig. 4 is the workflow diagram of the sequence specific primers-polymerase chain reaction of the present invention.
Fig. 5 is the program flow diagram of the image filtering denoising of the present invention.
Fig. 6 is the stochastic subspace identification method flow diagram of the present invention.
Fig. 7 is the first-order modal vibration shape of thin walled beam based on ANSYS finite element simulation.
Fig. 8 is the second-order modal vibration shape of thin walled beam based on ANSYS finite element simulation.
Fig. 9 is the one of the thin walled beam obtained according to the thin-wall part operation mode method of testing based on machine vision of the present invention
Order mode state bending vibation mode picture.
Figure 10 is the thin walled beam obtained according to the thin-wall part operation mode method of testing based on machine vision of the present invention
Second-order modal bending vibation mode picture.
Detailed description of the invention
The present invention is further illustrated below in conjunction with the accompanying drawings.
As it is shown in figure 1, the present invention thin-wall part operation mode based on machine vision test device, including tripod 1,
At CCD industrial camera 2, LED light source 3, retroreflective feature point 4, thin-wall part to be measured 5, firm banking 6, image collecting device 7 and image
Reason device 8.One end of thin-wall part 5 to be measured is fixed on base 6 by two bolts, and the other end is in free vacant state.
As in figure 2 it is shown, be equidistantly provided with multiple retroreflective feature point 4, retroreflective feature point 4 on the centrage of the leading flank of thin-wall part 5 to be measured
Using laser printing patch, laser printing, according to needing setting flexibly, is attached to be measured thin by retroreflective feature point 4 quantity before using
On center line before wall pieces 5.CCD industrial camera 2 is horizontally fixed on the dead ahead of thin-wall part 5 to be measured, CCD work by spider 1
The camera lens of industry camera 2 is towards the leading flank of thin-wall part 5 to be measured；And it is connected to image collecting device 7, image acquisition by data wire
Device 7 is connected with image processing apparatus 8, and LED light source 3 is arranged on the front of thin-wall part 5 to be measured, is positioned at CCD industrial camera 2 left
Side.
Utilize the thin-wall part work based on machine vision of above-mentioned thin-wall part operation mode based on machine vision test device
Make mode testing method, comprise the steps:
1st step: ask for the relative pose relation of CCD industrial camera 2 and thin-wall part 5 to be measured, and to CCD industrial camera 2
Principal point coordinate (u0,v0), the equivalent focal length a of horizontal directionx, the equivalent focal length a of vertical directiony, distortion parameter γ demarcates.
(1.1) CCD industrial camera 2 imaging process essence is the conversion process between several coordinate system, including world coordinates
Being tied to the rigid body translation of camera coordinates system, CCD industrial camera 2 coordinate is tied to perspective transform and the image coordinate of image coordinate system
Being tied to the conversion of image pixel coordinates system, its mathematical model is as follows:
In formula (1): f is the focal length of industrial camera；axFor the equivalent focal length on u axle；ayFor the equivalent focal length on v axle；(u0,
v0) it is the principal point coordinate of image, distortion factor γ=f α, α are camera distortion characterising parameters, are obtained by camera calibration；When
During γ=0, pixel planes is rectangle, and when γ ≠ 0, pixel planes is not rectangle；(Xc,Yc,Zc)、(Xwp,Ywp,Zwp) be respectively
Coordinate under camera coordinates system and world coordinate system, R is the orthogonal spin matrix of unit of 3 × 3, and T is the translation matrix of 3 × 1, logical
Cross camera calibration to obtain；M1It is 3 × 4 projection matrixes；M1For Intrinsic Matrix, by camera intrinsic parameter ax、ay、γ、u0、v0Determine；
M2For outer parameter matrix, industrial camera determine relative to the outer parameter of world coordinate system.
(1.2) utilize CCD industrial camera 2 imaging model, use plane gridiron pattern scaling board to determine CCD industrial camera 2
Inside and outside parameter, in the field range of CCD industrial camera 2, gather 25 width gridiron pattern scaling board images of different positions and pose, to 25
Width uncalibrated image utilizes the program write to carry out focus extraction, and calibrated and calculated obtains intrinsic parameter and the distortion of CCD industrial camera 2
Parameter, by a wherein width scaling board with CCD industrial camera 2 relative pose relation as CCD industrial camera 2 and thin-wall part 5 to be measured
Relative pose relation.
2nd step: firmly hammer hammering thin-wall part to be measured 5, wherein the input stimulus of power hammer is unknown, and thin-wall part 5 to be measured is in constraint
Under the conditions of produce vibration, the full sequence image of CCD industrial camera 2 this process of continuous acquisition, transmitted by image collecting device 7
To image processing apparatus 8, carry out sequence specific primers-polymerase chain reaction, extract the vibration signal of each retroreflective feature point 4, according to the 1st step
Model is also translated into the vibration signal of effective unit.
(2.1) according to the position of 12 retroreflective feature points 4 in the first two field picture and the peak swing of thin-wall part to be measured 5,
Determine a maximum test zone ROI；In the present invention, only consider the Mode Shape of the level fluctuation of thin-wall part 5 to be measured, to be measured
Thin-wall part 5 vertical direction vibrate, in the image of 640 × 480 pixels, thin-wall part 5 to be measured image width all the time
In field range, when the pixel in picture altitude direction reaches some, when there is peak swing in thin-wall part 5 the most to be measured,
It is further added by the pixel that processes to result substantially without affecting, but the whole calculating time can be increased.Therefore, on picture altitude direction
Only process one part of pixel number, process the time with shortening.Therefore, can be manually interactive to the first two field picture of each sequence image
Frame selects rectangular testing zone ROI, the width of test zone ROI to be picture traverse, and height is upper and lower peak swing, test zone
ROI can be automatically used for ensuing all two field pictures after determining.
(2.2) use adaptive wavelet threshold denoising method that the image in test zone ROI is processed；
(2.3) grey linear transformation is used to strengthen the brightness of image the test zone ROI after filtering and noise reduction；In vibration
During, owing to the sample rate of CCD industrial camera 2 is higher, time of exposure is short, uses common LED light source 3 to increase whole visual field
Brightness, but due to thin-wall part 5 up-down vibration to be measured, may result in the uneven illumination of partial sequence image, cause follow-up point
Cut incomplete, use grey linear transformation can improve the contrast of foreground and background, it is to avoid subsequent singulation is made mistakes.
(2.4) maximum between-cluster variance threshold segmentation method is used to extract the spot area of 12 retroreflective feature points；Maximum kind
Between variance method be that image histogram is separated at a certain threshold value, calculate the variance between separate two groups, when variance reaches
When being worth greatly, segmentation threshold is optimal threshold.Then utilize this threshold value that image is carried out segmentation and be converted into bianry image.
(2.5) use the spot area of the morphological method 12 retroreflective feature points to splitting to carry out morphology and close fortune
Calculate.
(2.6) according to the size of retroreflective feature point facula area after above-mentioned process, choose suitably height area threshold, pick
Remove other interference regions in image so that remaining bianry image only comprises the hot spot of retroreflective feature point, calculate each instead
The center-of-mass coordinate of light characteristic point hot spot, and it is recorded as the preservation of measuring point pixel coordinate, the abscissa u in units of pixel and vertical coordinate
v；The mathematical formulae extracting facula mass center is as follows:
In formula (2): M, N represent that spot area has the pixel composition of M row N row；I represents the row at pixel place；J table
Show the row at pixel place；F (i, j) be i-th row jth row pixel gray value.
(2.7) according to the sequence image order gathered, the vibration displacement signal of each retroreflective feature point 4 is extracted successively,
And pixel unit is converted into the physical unit of reality by the model set up according to the 1st step, it is thus achieved that the reality of 12 retroreflective feature points 4
Vibration displacement signal.
3rd step: use stochastic subspace identification method can carry out mould the vibration information of the multiple retroreflective feature points extracted
State parameter identification, obtains the operational modal parameter of thin-wall part 5 to be measured.
Experimental verification:
Utilize the thin-wall part operation mode method of testing based on machine vision of the present invention, be aluminum to material, a length of
777mm, wide 50mm, the operational vibration mode of the thin walled beam of thick 2mm tests, and CCD industrial camera 2 produces for IMI company
Industrial camera, adjusts the distance of CCD industrial camera 2 and thin walled beam, makes the image of thin walled beam be positioned at whole visual field, record CCD
Industrial camera, from thin walled beam about 2.4m, is connected with image collecting device 7 by data wire, and LED light source 3 is placed in left front, thin
Wall beam one end is unsettled, and the other end is bolted on base 6, and the side surface midline of thin walled beam posts retroreflective feature point 4.
Being first turned on image collecting device 7 and set the parameter of CCD industrial camera 2, camera frame frequency is 200fps, during exposure
Between be 900us, resolution is 640 × 480, and firmly hammer hammering thin walled beam, meanwhile, carries out sequential image acquisition, by gathered
Data process at image processing apparatus 8, it is possible to obtain the operational modal parameter of thin walled beam.
According to the size of thin walled beam, utilize ANSYS software, the DM module in workbench directly establishes thin-walled
The threedimensional model of beam, thin walled beam material is aluminum, density 2750kg/m3, Young's modulus is 6.9 × 1010Pa, Poisson's ratio is 0.35.
In view of simple in construction herein, selecting the mode of automatic grid division, sizing grid is 1.5mm, gives FEM (finite element) model here
Environment plus some suitable constraint simulation experiments.Table 1 is the modal parameter that Finite Element Simulation Analysis obtains with this method, removes
Go swing and the vibration shape of translation occurred in finite element simulation.Can show that the front second-order modal of the finite element analysis of thin walled beam is intrinsic
Frequency is respectively 3.71HZ and 22.81HZ, and with the error of experiment test value the most all within 10%, the mode of two kinds of methods is divided
Analysis effect is basically identical, only exists some fine distinctions.But owing in experiment, some complicated factors are cannot in simulations
Simulation, the most just determine the necessity that these difference exist.Therefore, the mode test result obtained by this method is correct
's.
Table 1 Finite Element Simulation Analysis obtains Comparative result with this method
| Order | Finite element simulation frequency (Hz) admittedly | The solid frequency (Hz) that this method obtains | The damping ratios identified |
| 1 | 3.73 | 3.54 | 0.01 |
| 2 | 22.94 | 22.78 | 0.02 |
Claims (7)
1. thin-wall part operation mode based on a machine vision test device, is characterized in that: include industrial camera, light source, figure
As harvester and image processing apparatus；Described industrial camera and light source are placed in the front of thin-wall part to be measured, industrial camera
Camera lens is towards the leading flank of thin-wall part to be measured, and the leading flank of thin-wall part to be measured is provided with multiple retroreflective feature point；Described industry
Camera is connected with image capturing system by data wire；Image collecting device is connected with image processing apparatus.
Thin-wall part operation mode based on machine vision the most according to claim 1 test device, is characterized in that: described
Industrial camera is fixed on the dead ahead of thin-wall part to be measured by spider.
Thin-wall part operation mode based on machine vision the most according to claim 1 test device, is characterized in that: described
Industrial camera uses CCD industrial camera；Described light source uses LED light source.
Thin-wall part operation mode based on machine vision the most according to claim 1 test device, is characterized in that: described
One end of thin-wall part is bolted on base.
5. thin-wall part operation mode based on the machine vision test that a kind utilizes in claim 1-4 described in any claim
The thin-wall part operation mode method of testing based on machine vision of device, comprises the steps:
1st step: ask for the relative pose relation of industrial camera and thin-wall part to be measured, and the principal point coordinate (u to industrial camera0,
v0), the equivalent focal length a of horizontal directionx, the equivalent focal length a of vertical directiony, distortion parameter γ demarcates；
2nd step: utilize power to hammer hammering thin-wall part to be measured into shape, utilize the full sequence image of industrial camera this process of continuous acquisition, logical
Cross image collecting device and be sent to image processing apparatus, carry out sequence specific primers-polymerase chain reaction, extract each retroreflective feature point with picture
Element is the vibration signal of unit, according to the 1st step model and be translated into the vibration signal of effective unit；
3rd step: use stochastic subspace identification method to carry out modal parameter the vibration information of the multiple retroreflective feature points extracted
Identify, obtain operational modal parameter.
Thin-wall part operation mode method of testing based on machine vision the most according to claim 5, the 1st step concrete operations are such as
Under:
(1.1) industrial camera imaging process essence is the conversion process between several coordinate system, is tied to camera including world coordinates
The rigid body translation of coordinate system, industrial camera coordinate is tied to the perspective transform of image coordinate system and image coordinate is tied to image pixel
The conversion of coordinate system, its mathematical model is as follows:
In formula: f is the focal length of industrial camera；axFor the equivalent focal length on u axle；ayFor the equivalent focal length on v axle；(u0,v0) for scheming
The principal point coordinate of picture, distortion factor γ=f α, α are camera distortion characterising parameters, are obtained by camera calibration；When γ=0,
Pixel planes is rectangle, and when γ ≠ 0, pixel planes is not rectangle；(Xc,Yc,Zc)、(Xwp,Ywp,Zwp) it is respectively camera coordinates
Coordinate under system and world coordinate system, R is the orthogonal spin matrix of unit of 3 × 3, and T is the translation matrix of 3 × 1, by camera mark
Fixed acquisition；M1It is 3 × 4 projection matrixes；M1For Intrinsic Matrix, by camera intrinsic parameter ax、ay、γ、u0、v0Determine；M2For outer ginseng
Matrix number, is determined relative to the outer parameter of world coordinate system by industrial camera；
(1.2) utilize industrial camera imaging model, use plane gridiron pattern scaling board to determine the inside and outside parameter of industrial camera,
Gather 25 width gridiron pattern scaling board images of different positions and pose in the field range of industrial camera, 25 width uncalibrated images are utilized and writes
Program carry out focus extraction, calibrated and calculated obtains intrinsic parameter and the distortion parameter of industrial camera, will wherein a width scaling board with
The relative pose relation of industrial camera is as the relative pose relation of industrial camera Yu thin-wall part to be measured.
Thin-wall part operation mode method of testing based on machine vision the most according to claim 5, the 2nd step concrete operations are such as
Under:
(2.1) according to position and the peak swing of thin-wall part to be measured of retroreflective feature points multiple in the first two field picture, one is determined
Individual test zone ROI；
(2.2) use adaptive wavelet threshold denoising method that the image in test zone ROI is processed；
(2.3) grey linear transformation is used to strengthen the brightness of image the test zone ROI after filtering and noise reduction；
(2.4) maximum between-cluster variance threshold segmentation method is used to extract the spot area of multiple retroreflective feature points；
(2.5) spot area of the morphological method multiple retroreflective feature points to splitting is used to carry out closing operation of mathematical morphology；
(2.6) according to the size of retroreflective feature point facula area after above-mentioned process, choose area threshold, weed out in image other
Interference region so that only comprise the hot spot of retroreflective feature point in remaining image, calculates the barycenter of each retroreflective feature point hot spot
Coordinate, and it is recorded as the preservation of measuring point pixel coordinate, the abscissa u in units of pixel and vertical coordinate v；Extract the number of facula mass center
Formula is as follows:
In formula: M, N represent that spot area has the pixel composition of M row N row；I represents the row at pixel place；J represents pixel
The row at some place；F (i, j) be i-th row jth row pixel gray value；
(2.7) according to the sequence image order gathered, the vibration displacement signal of each retroreflective feature point is extracted successively, and according to
Pixel unit is converted into the physical unit of reality by the model that the 1st step is set up, it is thus achieved that the actual vibration position of multiple retroreflective feature points
Shifting signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610807568.3A CN106124034B (en) | 2016-09-07 | 2016-09-07 | Thin-wall part working mode testing device and method based on machine vision |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610807568.3A CN106124034B (en) | 2016-09-07 | 2016-09-07 | Thin-wall part working mode testing device and method based on machine vision |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106124034A true CN106124034A (en) | 2016-11-16 |
| CN106124034B CN106124034B (en) | 2022-07-08 |
Family
ID=57271328
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610807568.3A Active CN106124034B (en) | 2016-09-07 | 2016-09-07 | Thin-wall part working mode testing device and method based on machine vision |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106124034B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106989812A (en) * | 2017-05-03 | 2017-07-28 | 湖南科技大学 | Large fan blade modal method of testing based on photogrammetric technology |
| CN108593087A (en) * | 2018-03-29 | 2018-09-28 | 湖南科技大学 | A kind of thin-wall part operational modal parameter determines method and system |
| CN109186483A (en) * | 2018-09-30 | 2019-01-11 | 华南理工大学 | A kind of the deformation driving detection device and method of the deformable intelligent wing |
| CN109269623A (en) * | 2018-09-18 | 2019-01-25 | 浙江工业大学 | A kind of hardened structure vibration measurement device based on camera |
| CN110261052A (en) * | 2019-06-19 | 2019-09-20 | 西北工业大学 | Using power hammer excitation and photogrammetric Modal Analysis of Structures system and method |
| CN112434659A (en) * | 2020-12-07 | 2021-03-02 | 深圳市优必选科技股份有限公司 | Method and device for rejecting light reflection characteristic points, robot and readable storage medium |
| CN114518161A (en) * | 2022-01-11 | 2022-05-20 | 武汉理工大学 | Measuring method for transverse vibration of end part of cylindrical simply supported beam based on computer vision |
| CN114659737A (en) * | 2020-12-22 | 2022-06-24 | 余姚舜宇智能光学技术有限公司 | Modal measuring method and system and electronic equipment |
| CN115246081A (en) * | 2022-06-02 | 2022-10-28 | 淮阴工学院 | Rapid and reliable milling chatter detection method |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO1999039194A1 (en) * | 1998-02-03 | 1999-08-05 | The Trustees Of The Stevens Institute Of Technology | Method and apparatus for acoustic detection and location of defects in structures or ice on structures |
| US6222181B1 (en) * | 1995-03-10 | 2001-04-24 | Seft Development Laboratory Co., Ltd. | Position measuring instrument measuring relative turning angle of light sources for receiving devices |
| DE202004021523U1 (en) * | 2004-12-23 | 2008-09-25 | "F. u. K." Frölich & Klüpfel Drucklufttechnik GmbH & Co. KG | coring device |
| US20090204355A1 (en) * | 2006-06-27 | 2009-08-13 | Ata Engineering, Inc. | Methods and apparatus for modal parameter estimation |
| CN102252792A (en) * | 2010-05-18 | 2011-11-23 | 同济大学 | Absolute axial force test method of bar |
| JP2013047674A (en) * | 2011-07-28 | 2013-03-07 | Kobe Steel Ltd | Vibration measurement apparatus and vibration measurement method |
| CN103048103A (en) * | 2012-04-23 | 2013-04-17 | 北京航空航天大学 | Non-contact modal test system and method |
| CN203069261U (en) * | 2013-03-04 | 2013-07-17 | 湖南科技大学 | Modal test system based on single-point laser continuous plane scanning vibration measurement |
| CN103267567A (en) * | 2013-06-05 | 2013-08-28 | 南京理工大学 | Measuring device and method for vibration of flexible cantilever on basis of machine vision |
| RU2012112903A (en) * | 2012-04-04 | 2013-10-10 | Государственное научное учреждение Всероссийский научно-исследовательский институт электрификации сельского хозяйства Российской академии сельскохозяйственных наук (ГНУ ВИЭСХ Россельхозакадемии) | DEVICE FOR VISUALIZING ACOUSTIC FIELD IN OPTICALLY REFLECTING ELASTIC SURFACE |
| CN103528782A (en) * | 2013-10-23 | 2014-01-22 | 东北大学 | Thin-walled structure part vibration test device and method based on piezoelectric ceramic vibration exciter |
| CN103575382A (en) * | 2013-11-28 | 2014-02-12 | 重庆长安汽车股份有限公司 | Method for testing local mode of automotive sheet part |
| CN104132791A (en) * | 2014-07-17 | 2014-11-05 | 浙江工业大学 | Operation mode analysis experiment method and device based on pulse excitation |
| CN104165742A (en) * | 2014-07-17 | 2014-11-26 | 浙江工业大学 | Cross spectral function-based operational modal analysis experiment method and apparatus |
| CN104166804A (en) * | 2014-08-20 | 2014-11-26 | 中国科学技术大学 | Working modal identification method based on time-frequency domain single-source-point sparse component analysis |
| CN105043700A (en) * | 2015-07-14 | 2015-11-11 | 工业和信息化部电子第五研究所 | Electronic device modal test system and method, and traveling wave tube slow wave structure modal test system |
| CN105424350A (en) * | 2015-12-19 | 2016-03-23 | 湖南科技大学 | Method and system for thin-wall part modal testing based on machine vision |
| CN205981439U (en) * | 2016-09-07 | 2017-02-22 | 湖南科技大学 | Thin wall task work makes mode testing arrangement based on machine vision |
-
2016
- 2016-09-07 CN CN201610807568.3A patent/CN106124034B/en active Active
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6222181B1 (en) * | 1995-03-10 | 2001-04-24 | Seft Development Laboratory Co., Ltd. | Position measuring instrument measuring relative turning angle of light sources for receiving devices |
| WO1999039194A1 (en) * | 1998-02-03 | 1999-08-05 | The Trustees Of The Stevens Institute Of Technology | Method and apparatus for acoustic detection and location of defects in structures or ice on structures |
| DE202004021523U1 (en) * | 2004-12-23 | 2008-09-25 | "F. u. K." Frölich & Klüpfel Drucklufttechnik GmbH & Co. KG | coring device |
| US20090204355A1 (en) * | 2006-06-27 | 2009-08-13 | Ata Engineering, Inc. | Methods and apparatus for modal parameter estimation |
| CN102252792A (en) * | 2010-05-18 | 2011-11-23 | 同济大学 | Absolute axial force test method of bar |
| JP2013047674A (en) * | 2011-07-28 | 2013-03-07 | Kobe Steel Ltd | Vibration measurement apparatus and vibration measurement method |
| RU2012112903A (en) * | 2012-04-04 | 2013-10-10 | Государственное научное учреждение Всероссийский научно-исследовательский институт электрификации сельского хозяйства Российской академии сельскохозяйственных наук (ГНУ ВИЭСХ Россельхозакадемии) | DEVICE FOR VISUALIZING ACOUSTIC FIELD IN OPTICALLY REFLECTING ELASTIC SURFACE |
| CN103048103A (en) * | 2012-04-23 | 2013-04-17 | 北京航空航天大学 | Non-contact modal test system and method |
| CN203069261U (en) * | 2013-03-04 | 2013-07-17 | 湖南科技大学 | Modal test system based on single-point laser continuous plane scanning vibration measurement |
| CN103267567A (en) * | 2013-06-05 | 2013-08-28 | 南京理工大学 | Measuring device and method for vibration of flexible cantilever on basis of machine vision |
| CN103528782A (en) * | 2013-10-23 | 2014-01-22 | 东北大学 | Thin-walled structure part vibration test device and method based on piezoelectric ceramic vibration exciter |
| CN103575382A (en) * | 2013-11-28 | 2014-02-12 | 重庆长安汽车股份有限公司 | Method for testing local mode of automotive sheet part |
| CN104132791A (en) * | 2014-07-17 | 2014-11-05 | 浙江工业大学 | Operation mode analysis experiment method and device based on pulse excitation |
| CN104165742A (en) * | 2014-07-17 | 2014-11-26 | 浙江工业大学 | Cross spectral function-based operational modal analysis experiment method and apparatus |
| CN104166804A (en) * | 2014-08-20 | 2014-11-26 | 中国科学技术大学 | Working modal identification method based on time-frequency domain single-source-point sparse component analysis |
| CN105043700A (en) * | 2015-07-14 | 2015-11-11 | 工业和信息化部电子第五研究所 | Electronic device modal test system and method, and traveling wave tube slow wave structure modal test system |
| CN105424350A (en) * | 2015-12-19 | 2016-03-23 | 湖南科技大学 | Method and system for thin-wall part modal testing based on machine vision |
| CN205981439U (en) * | 2016-09-07 | 2017-02-22 | 湖南科技大学 | Thin wall task work makes mode testing arrangement based on machine vision |
Non-Patent Citations (3)
| Title |
|---|
| ABRAMOVICH, H: "Buckling prediction of panels using the vibration correlation technique", 《PROGRESS IN AEROSPACE SCIENCES》 * |
| WU, JIGANG: "A new thin-wall beam modal testing method based on single point laser continuous plane scanning vibration measurement", 《SIXTH INTERNATIONAL SYMPOSIUM ON PRECISION MECHANICAL MEASUREMENTS》 * |
| 王宇等: "模态数量对薄壁短圆柱壳振动响应分析的影响", 《噪声与振动控制》 * |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106989812A (en) * | 2017-05-03 | 2017-07-28 | 湖南科技大学 | Large fan blade modal method of testing based on photogrammetric technology |
| CN108593087A (en) * | 2018-03-29 | 2018-09-28 | 湖南科技大学 | A kind of thin-wall part operational modal parameter determines method and system |
| CN109269623A (en) * | 2018-09-18 | 2019-01-25 | 浙江工业大学 | A kind of hardened structure vibration measurement device based on camera |
| CN109186483A (en) * | 2018-09-30 | 2019-01-11 | 华南理工大学 | A kind of the deformation driving detection device and method of the deformable intelligent wing |
| CN110261052A (en) * | 2019-06-19 | 2019-09-20 | 西北工业大学 | Using power hammer excitation and photogrammetric Modal Analysis of Structures system and method |
| CN112434659A (en) * | 2020-12-07 | 2021-03-02 | 深圳市优必选科技股份有限公司 | Method and device for rejecting light reflection characteristic points, robot and readable storage medium |
| CN112434659B (en) * | 2020-12-07 | 2023-09-05 | 深圳市优必选科技股份有限公司 | Reflection characteristic point eliminating method, device, robot and readable storage medium |
| CN114659737A (en) * | 2020-12-22 | 2022-06-24 | 余姚舜宇智能光学技术有限公司 | Modal measuring method and system and electronic equipment |
| CN114518161A (en) * | 2022-01-11 | 2022-05-20 | 武汉理工大学 | Measuring method for transverse vibration of end part of cylindrical simply supported beam based on computer vision |
| CN114518161B (en) * | 2022-01-11 | 2023-02-24 | 武汉理工大学 | Measuring method for transverse vibration of end part of cylindrical simply supported beam based on computer vision |
| CN115246081A (en) * | 2022-06-02 | 2022-10-28 | 淮阴工学院 | Rapid and reliable milling chatter detection method |
| CN115246081B (en) * | 2022-06-02 | 2023-08-25 | 淮阴工学院 | Quick and reliable milling chatter detection method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106124034B (en) | 2022-07-08 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106124034A (en) | Thin-wall part operation mode based on machine vision test device and method of testing | |
| CN105424350B (en) | Thin-wall part mode testing method and system based on machine vision | |
| CN113516660B (en) | Visual positioning and defect detection method and device suitable for train | |
| CN110555889B (en) | CALTag and point cloud information-based depth camera hand-eye calibration method | |
| CN109883533B (en) | Low-frequency vibration measurement method based on machine vision | |
| CN110108348B (en) | Thin-wall part micro-amplitude vibration measurement method and system based on motion amplification optical flow tracking | |
| CN103994724B (en) | Structure two-dimension displacement and strain monitoring method based on digital image processing techniques | |
| CN103207987B (en) | A kind of registration recognition methods of pointer instrument | |
| CN102003938B (en) | Thermal state on-site detection method for large high-temperature forging | |
| CN205981439U (en) | Thin wall task work makes mode testing arrangement based on machine vision | |
| CN102589516B (en) | Dynamic distance measuring system based on binocular line scan cameras | |
| CN111174961B (en) | Cable force optical measurement method based on modal analysis and measurement system thereof | |
| CN106600647A (en) | Binocular visual multi-line projection structured light calibration method | |
| CN104700395A (en) | Method and system for detecting appearance crack of structure | |
| CN107238534A (en) | The method and device of on-line monitoring plate stretch performance is calculated based on image | |
| CN111623942A (en) | Displacement measurement method for test structure model of unidirectional vibration table | |
| CN107328502A (en) | A kind of anchor rod pallet board load visualization digital imaging method | |
| CN109978940A (en) | A kind of SAB air bag size vision measuring method | |
| CN103149087A (en) | Follow-up window and digital image-based non-contact real-time strain measurement method | |
| CN113205511A (en) | Electronic component batch information detection method and system based on deep neural network | |
| CN115760837A (en) | Crystal quality evaluation method and system based on deep neural network | |
| CN115201883A (en) | Moving target video positioning and speed measuring system and method | |
| CN105300302A (en) | Diameter measurement method for Brinell hardness indent circle | |
| CN103413319A (en) | Industrial camera parameter on-site calibration method | |
| CN107578001B (en) | Method and device for testing resolution of fingerprint acquisition equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |