CN104964886A - Welded member fatigue stress and strain real-time non-contact type monitoring method - Google Patents
Welded member fatigue stress and strain real-time non-contact type monitoring method Download PDFInfo
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Abstract
The present invention discloses a welded member fatigue stress and strain real-time non-contact type monitoring method. According to the method, the welded member welding seam and a certain surrounding area are subjected to grid division with a certain interval, each grid intersection point is adopted as an infinitesimal body as the stress and strain analysis unit, the feature information of the pixel points of all infinitesimal bodies in the area to be measured during the fatigue load acting process are extracted through a machine vision monitoring system when the fatigue load acts on the member, the collected member image is subjected to pixel point matching, the relation between the stress and strain change law and the image feature is established so as to obtain the feature change of the infinitesimal body during the fatigue load acting process, and finally the strain value and stress distribution law and the stress strain evolution law during the fatigue process are obtained. The method of the present invention is used for solving the monitoring problem of the stress and strain status distribution and evolution law of the welded member in the steel structure under the fatigue load acting.
Description
Technical field
The invention belongs to Experimental Mechanics, be specifically related to a kind of weld assembly fatigue stress and strain real-time contactless monitoring method.
Background technology
Be welded to connect and be widely used as construction steel structure and the isostructural main connected mode of wind-powered electricity generation structural tower cylinder, it subjects the alternate load effect between each link, under drawing, pressing alternate load effect, directly affects the security of structure.The fatigue strength of weld seam is far below the fatigue strength of mother metal, the principal mode of welding seam failure is tired, the fatigue resistence of weld seam depends on the both macro and micro geometric configuration of weld seam to a great extent, the factor affecting weld fatigue intensity is a lot, such as stress amplitude, mean stress, welding residual stress etc., therefore the monitoring of butt welded seam fatigue stress is most important.Can not be found in time as weld fatigue stress state changes, then can affect the normal work of structure, severe patient will cause huge personnel's property damage.At present, online network for weld fatigue stress state is still in the blank stage, be the stress being calculated commissure by finite element analysis software for solder joint fatigue analytical approach, but there is stress to problems such as finite elements type, mesh shape and dimension sensitive, the method of traditional monitoring of structures stress-strain state is normally pasted strain at key position and is obtained relevant information, but touch sensor is inconvenient to the part of installing, such as weld seam and welding toe thereof, because the layout of be concerned about weld seam position foil gauge is subject to the geometric configuration of weld seam and the restriction of locus, foil gauge generally can only be pasted leaving position while welding a distance, and the quantity of pasting foil gauge is also restricted, this makes the position can only monitored limited position by the sensor of this contact and be convenient to paste, the ess-strain situation being not easy to position and any position of pasting then is restricted, in order to the ess-strain obtaining component can only adopt some to suppose, such as homogeneity hypothesis, this may obtain the result of mistake in some cases, also weld seam real stress distribution law and development under Fatigue Load during one's term of military service can not just be reflected really.Therefore be necessary to develop a kind of non-contact monitoring technology that can be used for being not easy to install contact stress strain gauge and need to be grasped component any position Stress distribution rule and Evolution.
Both at home and abroad in the robotization and Study of intelligent of weld seam detection, mainly concentrate on and Vision Builder for Automated Inspection is used on weld seam inherent vice and weld joint tracking, mainly pass judgment on for weldquality and carried out a large amount of research and discussions, but but do not have report about the monitoring for weld fatigue stress.
Summary of the invention
The present invention is directed in prior art is solve the position of touch sensor of the ess-strain monitoring regularity of distribution and fatigue stress strain Evolution problem touch sensor is not easy to install to(for) weld assembly, proposes a kind of weld assembly fatigue stress and strains real-time contactless monitoring method.
The technical solution used in the present invention: a kind of weld assembly fatigue stress and strain real-time contactless monitoring method, comprises the steps:
(1) on weld assembly, arrange mesh lines, the intersection point of described mesh lines is monitoring point;
First to weld assembly with and the obvious pastel of component color contrast along the horizontal and vertical size according to component of component and the size determination mesh spacing of monitoring cross section, grid division, using the intersection point of each grid as stress, strain monitoring point, this intersection point shows as the set of multiple pixel in image processing process, using this set as a micro unit;
(2) adopt monitoring system to be gathered by the dynamic image in weld assembly region to be measured in fatigue load process, and image information is transmitted;
Described monitoring system comprises ccd sensor, camera lens, image pick-up card, image processing system; A series of dynamic deformation images of weld assembly monitoring point in fatigue load process are gathered with ccd sensor and camera lens, the optical imagery that ccd sensor is received changes into vision signal and exports to image pick-up card, and digital signal is changed into digital image information and processes for image processing system by image pick-up card again;
(3) the monitoring point image obtained is carried out pixel coupling;
In each monitoring point fatigue load process, macroscopically just show trickle change, the pixel number size that camera lens gathers image is at every turn fixed, micro-scale then shows as the difference of shared pixel at number, mating of pixel and pixel, is carried out to the image of identical monitoring point different time, obtains the change of each monitoring point at the number of pixels in each moment;
(4) according to the change of pixel in monitoring point, ε is utilized
i=Δ L
i/ L
icalculate monitoring point strain value, wherein ε
ifor strain value, Δ L
ifor length varying value, L
ifor the length value of monitoring section, and then determine the strain distributing disciplinarian of position, whole region surface monitoring point to be measured;
Change into discrete for region to be measured continuous print Strain Distribution the strain value solving each monitoring point, when the division of grid is enough meticulous, calculate the distribution situation of each monitoring point in the just approximate strain obtained in fatigue load process of not strain value in the same time;
(5) according to described strain value, σ is utilized
i=E ε
icalculate monitoring point stress, wherein, σ
ifor stress value, E is elastic modulus, obtains the monitoring point stress distribution situation in region to be measured and tired Evolution thereof further;
In fatigue load process, transversely obtain the strain value of each monitoring point along both direction with the change calculations of longitudinal direction by the pixel in monitoring, suppose that welding material is that elastic modulus E in fatigue process does not change, according to σ
i=E ε
ithe stress distribution situation of further weld assembly any position and tired Evolution thereof.
As preferably, image processing system described in above-mentioned steps (2) selects LabVIEW software as development platform, is directly docked with software by the digital image information on image pick-up card; Image processing system processes the image collected, and obtains the dynamic picture of test specimen in process of the test, finally realizes the monitoring of ess-strain through the coupling that dynamic picture is real-time; Comprise Image semantic classification, rim detection, Iamge Segmentation and image pixel Point matching; The object of Image semantic classification is to eliminate in Image Acquisition, transmission, acceptance and processing procedure due to noise that external disturbance and internal interference cause, improve the effect of original image, improve the sharpness of welded seam area, the pixel value uniformity coefficient of state diagram image space.Image semantic classification mainly by image filtering, carries out noise processed for image, and noise decrease affects; Gray-level correction, revises the intensity profile of original image according to actual needs, thus eliminates tonal distortion, strengthens contrast, improves visual effect; Image sharpening, object makes edge and the fuzzy image of outline line become clear; Geometry correction, for the geometry deformity that some imaging system causes, carries out position correction to the coordinate points of distortion.Rim detection extracts boundary line between objects in images and background by certain algorithm of gradient magnitude threshold criterion, split by test component, preferably adopt Roberts operator edge detection in operation according to boundary line.The fundamental purpose of Iamge Segmentation is that welding piece image is split according to the different parts such as weld seam, mother metal, analyzes respectively zones of different.Main process is by method for detecting image edge, namely different grey-scale not or gray scale have larger region of variation, identify the zones of different of welding piece, be divided into by test specimen zones of different according to pixel analysis method, identify Stress distribution and Evolution thereof.Finally using not carrying out the characteristic information of fatigue load time chart picture as with reference to model, the mode of simple pixel for pixel is adopted to carry out images match.
As preferably, the image for having obtained in above-mentioned steps (2), arrange light source, the preferred LED light emitting diode of light source, described camera lens and ccd sensor are ccd video camera, by external signal trigger-type unlatching collection.For obtaining the image of high-quality, high-contrast, by weld assembly and background obvious difference as far as possible, the color being different from mesh lines is adopted monitoring point to carry out identifying, reducing as far as possible the impact of reflection, as far as possible shading ring environmental light, adopt LED light emitting diode at a certain angle in the preposition irradiation of weld assembly, make whole welded seam area be subject to uniform illumination.
The present invention will carry out the stress and strain model of a determining deviation in weld assembly weld seam and surrounding certain area thereof, regard each grid intersection point as micro unit as stress-strain analysis unit, under component bears Fatigue Load, by machine vision monitoring system, all micro unit image feature informations in region to be measured in Fatigue Load process are extracted, and pixel coupling is carried out to the component diagram picture collected, set up the mutual relationship between ess-strain Changing Pattern and characteristics of image, thus obtain the changing features of micro unit in Fatigue Load process, obtain strain value and stress distribution situation, ultimately provide a kind of based on the weld assembly fatigue stress Strain Distribution of Machine Vision Inspecting System and the non-contact monitoring method of Evolution thereof.
The present invention obtains the weld assembly stress of different accuracy and the data of Strain Distribution situation and tired Evolution, can change mesh lines spacing in described mesh lines, obtains another group monitoring point strain and stress information.
The present invention uses fatigue tester to grasp the stress of specific weld member welding joints and fringe region thereof and Strain Distribution and Evolution, thus grasp the Changing Pattern of the mechanical property in weld assembly During Fatigue Damage Process, set up fatigue damage stress (strain) variation model, for the Fatigue Life Assessment of weld assembly and prediction provide foundation.
Beneficial effect: the present invention has the following advantages relative to prior art:
1. what propose in the present invention treats micrometer element deformation analytic approach, the Stress distribution situation at position while welding place can be reflected really, higher than the method precision of traditional stickup foil gauge monitor strain value, avoid foil gauge to adopt resistance change to measure the transformation of strain value change pilot process.
2, the present invention is simple to operate, belongs to non-cpntact measurement, is mainly used in solving the Stress distribution rule at position that cannot paste of touch sensor and the monitoring problem of tired Evolution thereof.
3, the invention belongs to contactless measurement, solve the ess-strain that touch sensor can only test limited position, the problem of the ess-strain of all positions of one-piece construction cannot be obtained, the method can monitor the Stress distribution rule in a face by a monitoring equipment, instead of point.
4, the present invention is applicable to rig-site utilization because analytical equipment volume is little, low price, structure simple, is particularly suitable for complicated syndeton, particularly heavy construction structure.
5, native system is based on LabVIEW software as development platform, and have powerful post processor, precision is high, applying flexible.
Accompanying drawing explanation
Fig. 1 weld assembly floor map of the present invention;
The grid schematic diagram of Fig. 2 monitored area of the present invention;
Fig. 3 monitoring system structural representation of the present invention;
Monitoring system workflow diagram in Fig. 4 embodiment of the present invention.
Embodiment
Below in conjunction with drawings and Examples, the present invention is further illustrated.
The present embodiment is when fatigue tester carries out fatigue loading to weld assembly, carries out the experiment of welded seam area stress and strain monitoring.
As depicted in figs. 1 and 2, the steel adopted in this test are the Q345B steel that building is commonly used, and its yield strength is 345Mpa, require production standard test weld assembly 1 according to related specifications.Weld assembly 1 comprises monitored area 2, because the stress of commissure plays major control effect for weld assembly 1, therefore get weld seam and edge extent thereof as monitored area 2, monitored area 2 comprises weld seam 3 and weld edge, the wide 40mm of long 75mm, grid is drawn in monitored area 2.Grid is read for the ease of monitoring device vision system, adopt and there is the wide pastel of the 2mm of striking contrast degree at monitored area 2 grid division with weld assembly 1 color, mesh spacing is 5mm, then the intersection point (i.e. monitoring point) of grid is of a size of 2mm × 2mm.
As shown in Figure 3, the present embodiment monitoring system comprises LED light emitting diode 4, ccd video camera 5, fatigue tester 6 and computing machine.Be fixed on fatigue tester 6 by weld assembly 1, determine Protonation constant, this time adopt axial tension torture test, stress ratio R=-1, i.e. axial asymmetrical load, the range of stress gets the yield strength of 0.7 times, i.e. 190kN.Adopt the weld assembly 1 in ccd video camera 5 pairs of fatigue load processes to carry out Real-Time Monitoring, obtain the dynamic image information of the monitored area 2 of weld assembly 1.
The interface function carried by ccd video camera 5 docks with the program interface that computing machine is write, to carry out subsequent treatment and Real-Time Monitoring to the image information of the monitored area 2 collected.
According to digital image processing techniques, extract the pixel characteristic of monitoring point, by the change of monitoring point pixel characteristic in process of the test, obtain stress (strain) Evolution in stress (strain) regularity of distribution and fatigue process.The processing procedure of image comprises Image semantic classification, rim detection, Iamge Segmentation and image pixel Point matching, and wherein Image semantic classification mainly includes effect extracted region and denoising.Effective coverage is extracted: in gathered image, monitored area 2 to comprise outside weld seam and toe of weld within the scope of certain distance, scope shared by it only accounts for the sub-fraction of entire image width, in order to invalid data effectively can be got rid of to detecting the interference brought, reduce required data volume to be processed, set the width of each 60 pixels up and down from center, monitored area 2 as effective coverage; In order to the noise existed in filtering image of trying one's best, adopt the method for Wavelet transformation to show on different resolution level respectively by the structure of image and texture, reconstruct after thresholding process is carried out to coefficient of dissociation; For each feature extraction tested point out, Roberts operator edge detection method is adopted to obtain the deformation information of each monitoring point; After the digital picture obtained for each moment carries out aforesaid operations, be referred to as the extraction of the characteristic information of each moment monitored area 2, these information are set up an image characteristic information data storehouse, using not loading the characteristic information of time chart picture as with reference to model, the mode of pixel for pixel is adopted to carry out images match.
In this test, the resolution of the ccd video camera 5 of employing is 0.01mm, and that is monitoring point is made up of 200 × 200 pixels.Before fatigue load, each monitoring point is made up of 200 × 200 pixels, after the stretch by carrying out Treatment Analysis discovery to image, the pixel of this monitoring point becomes 209 × 198 pixels, can find out by calculating, after weld assembly 1 is subject to axial tension, component adds 9 pixels vertically, illustrates that this monitoring point there occurs the deformation of 0.01mm × 9=0.09mm, passes through ε
i=Δ L
i/ L
ithe strain value that calculating can obtain this monitoring point is ε
i=0.09/75 × 10
-6=1200 μ ε, this and calculated value 1187.5 μ ε are substantially identical, and error is 1%, within the scope of engineering acceptable.To improve precision in Practical Project, high precision video camera can be adopted.
By carrying out the pixel value of all monitoring points analyzing the strain value that can obtain each monitoring point, the strain distributing disciplinarian of weld assembly 1 can be obtained; σ can be passed through after obtaining the strain distributing disciplinarian of component
i=E ε
icalculate the stress of any position, can stress distribution law be obtained; The Fatigue Life Assessment that the data obtained are weld assembly and prediction provide foundation.
Vision Builder for Automated Inspection combines with image processing techniques the monitoring achieved weld assembly fatigue stress (strain) distribution and Evolution thereof by this method of testing, establishes the test model based on micro unit distortion.The present embodiment obtains stress (strain) regularity of distribution at weld assembly 1 position while welding place, and monitoring system workflow diagram as shown in Figure 4.
It should be pointed out that for those skilled in the art, under the premise without departing from the principles of the invention, can also make some improvements and modifications, these improvements and modifications also should be considered as protection scope of the present invention.The all available prior art of each ingredient not clear and definite in the present embodiment is realized.
Claims (3)
1. weld assembly fatigue stress and strain a real-time contactless monitoring method, is characterized in that: comprise the steps:
(1) on weld assembly, arrange mesh lines, the intersection point of described mesh lines is monitoring point;
First to weld assembly with and the obvious pastel of component color contrast along the horizontal and vertical size according to component of component and the size determination mesh spacing of monitoring cross section, grid division, using the intersection point of each grid as stress, strain monitoring point, this intersection point shows as the set of multiple pixel in image processing process, using this set as a micro unit;
(2) adopt monitoring system to be gathered by the dynamic image in weld assembly region to be measured in fatigue load process, and image information is transmitted;
Described monitoring system comprises ccd sensor, camera lens, image pick-up card, image processing system; A series of dynamic deformation images of weld assembly monitoring point in fatigue load process are gathered with ccd sensor and camera lens, the optical imagery that ccd sensor is received changes into vision signal and exports to image pick-up card, and digital signal is changed into digital image information and processes for image processing system by image pick-up card again;
(3) the monitoring point image obtained is carried out pixel coupling;
In each monitoring point fatigue load process, macroscopically just show trickle change, the pixel number size that camera lens gathers image is at every turn fixed, micro-scale then shows as the difference of shared pixel at number, mating of pixel and pixel, is carried out to the image of identical monitoring point different time, obtains the change of each monitoring point at the number of pixels in each moment;
(4) according to the change of pixel in monitoring point, ε is utilized
i=Δ L
i/ L
icalculate monitoring point strain value, wherein ε
ifor strain value, Δ L
ifor length varying value, L
ifor the length value of monitoring section, and then determine the strain distributing disciplinarian of position, whole region surface monitoring point to be measured;
Change into discrete for region to be measured continuous print Strain Distribution the strain value solving each monitoring point, when the division of grid is enough meticulous, calculate the distribution situation of each monitoring point in the just approximate strain obtained in fatigue load process of not strain value in the same time;
(5) according to described strain value, σ is utilized
i=E ε
icalculate monitoring point stress, wherein, σ
ifor stress value, E is elastic modulus, obtains the monitoring point stress distribution situation in region to be measured and tired Evolution thereof further;
In fatigue load process, transversely obtain the strain value of each monitoring point along both direction with the change calculations of longitudinal direction by the pixel in monitoring, suppose that welding material is that elastic modulus E in fatigue process does not change, according to σ
i=E ε
ithe stress distribution situation of further weld assembly any position and tired Evolution thereof.
2. a kind of weld assembly fatigue stress according to claim 1 and strain real-time contactless monitoring method, it is characterized in that: image processing system described in described step (2) selects LabVIEW software as development platform, is directly docked with software by the digital image information on image pick-up card, image processing system processes the image collected, and obtains the dynamic picture of test specimen in process of the test, finally realizes the monitoring of ess-strain through the coupling that dynamic picture is real-time, comprise Image semantic classification, rim detection, Iamge Segmentation and image pixel Point matching, Image semantic classification passes through image filtering, gray-level correction, image sharpening and geometry correction process, rim detection extracts boundary line between objects in images and background by certain algorithm of gradient magnitude threshold criterion, according to boundary line, test component is split, Roberts operator edge detection is adopted in operation, Iamge Segmentation passes through method for detecting image edge, namely different grey-scale not or gray scale have larger region of variation, identify the zones of different of welding piece, be divided into by test specimen zones of different according to pixel analysis method, identify Stress distribution and Evolution thereof, finally using not carrying out the characteristic information of fatigue load time chart picture as reference model, the mode of simple pixel for pixel is adopted to carry out images match.
3. a kind of weld assembly fatigue stress according to claim 1 and strain real-time contactless monitoring method, it is characterized in that: described step arranges light source in (2), light source is LED light emitting diode, described camera lens and ccd sensor are ccd video camera, opened by external signal trigger-type and gather, weld assembly and background are obviously distinguished, the color being different from mesh lines is adopted to identify monitoring point, reduce reflection, the impact of shading ring environmental light, adopt LED light emitting diode at a certain angle in the preposition irradiation of weld assembly, whole welded seam area is made to be subject to uniform illumination.
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| CN105912805A (en) * | 2016-04-28 | 2016-08-31 | 北京汽车研究总院有限公司 | Modeling method and device of finite element model of metal plate welding line |
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1458528A (en) * | 2003-05-07 | 2003-11-26 | 天津大学 | New enginering structure and material deformation measuring technology |
| CN101196390A (en) * | 2007-12-19 | 2008-06-11 | 哈尔滨工业大学 | Non-contact type strain measurement method based on visual discrimination |
| CN101413901A (en) * | 2008-12-01 | 2009-04-22 | 南京航空航天大学 | Surface fatigue crack detecting method based on CCD image characteristic |
| CN102539438A (en) * | 2011-12-02 | 2012-07-04 | 上海电机学院 | Real-time state monitoring and fault diagnosing system and method for blades of wind generating set |
| CN104181225A (en) * | 2014-08-01 | 2014-12-03 | 河海大学 | Steel material fatigue crack tracking and monitoring device and monitoring method thereof |
| CN105122046A (en) * | 2013-03-29 | 2015-12-02 | 斯奈克玛 | System for detecting defects on an object |
-
2015
- 2015-05-27 CN CN201510280759.4A patent/CN104964886A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1458528A (en) * | 2003-05-07 | 2003-11-26 | 天津大学 | New enginering structure and material deformation measuring technology |
| CN101196390A (en) * | 2007-12-19 | 2008-06-11 | 哈尔滨工业大学 | Non-contact type strain measurement method based on visual discrimination |
| CN101413901A (en) * | 2008-12-01 | 2009-04-22 | 南京航空航天大学 | Surface fatigue crack detecting method based on CCD image characteristic |
| CN102539438A (en) * | 2011-12-02 | 2012-07-04 | 上海电机学院 | Real-time state monitoring and fault diagnosing system and method for blades of wind generating set |
| CN105122046A (en) * | 2013-03-29 | 2015-12-02 | 斯奈克玛 | System for detecting defects on an object |
| CN104181225A (en) * | 2014-08-01 | 2014-12-03 | 河海大学 | Steel material fatigue crack tracking and monitoring device and monitoring method thereof |
Cited By (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105547834A (en) * | 2016-01-13 | 2016-05-04 | 南京航空航天大学 | Fast stress-strain curve measuring system and method based on binocular vision |
| CN105912805A (en) * | 2016-04-28 | 2016-08-31 | 北京汽车研究总院有限公司 | Modeling method and device of finite element model of metal plate welding line |
| CN106910434A (en) * | 2017-02-13 | 2017-06-30 | 武汉随戈科技服务有限公司 | A kind of exhibitions conference service electronics seat card |
| CN107870084A (en) * | 2017-09-18 | 2018-04-03 | 中车工业研究院有限公司 | The contactless monitoring method and system of train bogie |
| CN108362561A (en) * | 2018-02-02 | 2018-08-03 | 上海理工大学 | A kind of weld seam and the material mechanical performance of welding heat affected zone determine method |
| CN108362561B (en) * | 2018-02-02 | 2020-06-09 | 上海理工大学 | Method for determining mechanical properties of materials of welding seam and welding heat affected zone |
| CN109858551A (en) * | 2019-01-31 | 2019-06-07 | 湖南大学 | Method, apparatus, equipment and medium based on image recognition detection structure stress |
| CN110082025A (en) * | 2019-05-05 | 2019-08-02 | 湘潭大学 | It is a kind of for rotary shaft simultaneously by axial force and torque when the new method that accurately measures and monitor |
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| CN112013779A (en) * | 2020-07-17 | 2020-12-01 | 南京航空航天大学 | Deformation online measuring device and method for skin-stringer double-laser-beam bilateral synchronous welding T-shaped structure |
| CN111948002A (en) * | 2020-08-06 | 2020-11-17 | 中国石油天然气集团有限公司 | Experimental method for weld characteristic region deformation damage evolution law |
| CN111948002B (en) * | 2020-08-06 | 2023-04-25 | 中国石油天然气集团有限公司 | Weld joint characteristic region deformation damage evolution rule experimental method |
| CN114518295A (en) * | 2020-11-19 | 2022-05-20 | 中车株洲电力机车研究所有限公司 | Tower load measuring method, device and system |
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