CN105571502A - Measuring method of weld gap in friction-stir welding - Google Patents
Measuring method of weld gap in friction-stir welding Download PDFInfo
- Publication number
- CN105571502A CN105571502A CN201511019117.5A CN201511019117A CN105571502A CN 105571502 A CN105571502 A CN 105571502A CN 201511019117 A CN201511019117 A CN 201511019117A CN 105571502 A CN105571502 A CN 105571502A
- Authority
- CN
- China
- Prior art keywords
- laser stripe
- weld gap
- image
- friction welding
- measuring method
- 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
- 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
- G01B11/14—Measuring arrangements characterised by the use of optical techniques for measuring distance or clearance between spaced objects or spaced apertures
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention provides a measuring method of a weld gap in friction-stir welding. The method includes: S1, obtaining area image information of the weld gap in stir-friction welding; S2, performing image pre-processing of the image; S3, extracting edge information of a laser stripe by employing an edge detection algorithm; S4, selecting an adaptive interested area; S5, extracting a center line of the laser stripe by employing an improved center-of-gravity method; and S6, obtaining information of the weld gap via the information of the contour and the center line of the laser stripe. According to the method, a basis is laid for the subsequent guiding of the process of stir-friction welding.
Description
Technical field
The present invention relates to vision detection technology and agitating friction welding processing technique field, particularly, relate to the measuring method of weld gap in the welding of a kind of agitating friction.
Background technology
Agitating friction welding (FSW), as a kind of solid phase joining technique, because having the advantages such as cost is low, welding deformation is little, quality is high, the duration is short, becomes the development trend of space industry tank manufacture process welding technology.But due to the weld characteristics of this technology, the weld gap allowed and unfitness of butt joint are all less than 0.2mm, measuring accuracy is ± 0.05mm, and choose the factor such as improper by stirring-head parameter, welding condition and affect, the weld defectss such as such as hole, groove, lack of penetration, overlap and Z line can be produced in welding process unavoidably.For ensureing welding quality, improve the adaptive faculty of welding gear butt welded seam shaped position change and the automatization level of welding control, must measure the seam center of reality before welding or in welding process, thus reduce weld defects rate, improve Product jointing quality.
Because stir friction welding seam feature is small, measuring accuracy requires high.At present, the light sources such as laser and video camera is utilized to form visual sensing system, by laser projection to welded seam area, the image of ccd video camera shooting surface of the work is gone forward side by side row relax, and the vision measurement technology that accurately can obtain body surface three-dimensional information has become the main method of weld seam detection and tracking.Because stir friction welding seam feature is small, measuring accuracy requires high.But existing vision-based detection product can only detect the larger feature of weld gap mostly, is not suitable for the detection of stir friction welding seam feature.Such as, model is the Ji Enshi laser measuring apparatus of LJ-V7080, carries out repeatedly repetition measurement experiment to the weld gap of different size, finds this equipment only when weld gap is greater than 0.7mm, could obtain stable characteristics of weld seam, and measuring accuracy is only ± 0.1mm.
Summary of the invention
For defect of the prior art, the object of this invention is to provide the measuring method of weld gap in the welding of a kind of agitating friction, the method can a measuring position, realize weld gap three-dimensional coordinate and width information quick, accurately and automatically extract, thus obtain the center location information of weld seam, for subsequent boots stir friction welding process lays the foundation.
For realizing above object, the invention provides the measuring method of weld gap in the welding of a kind of agitating friction, described method comprises the steps:
The image in S1, acquisition weld gap region;
S2, Image semantic classification is carried out to the image that S1 obtains, obtain pretreated laser stripe image;
After S3, the pre-service that obtains S2, laser stripe imagery exploitation edge detection algorithm extracts the marginal information of laser stripe;
S4, the laser stripe marginal information obtained S3 carry out the selection of self-adaptation area-of-interest;
S5, the final area-of-interest obtained S4 utilize and improve the center line that gravity model appoach extracts laser stripe;
The center line information that S6, the laser stripe marginal information obtained by S3 and S5 are obtained obtains weld gap information.
Preferably, in described S1:
Adopt laser line generator to project a laser plane, formation crossing with surface of the work laser stripe, and laser stripe is crossing with weld gap; CCD camera is installed vertically on directly over weld gap, shooting laser stripe image.
Preferably, in described S2:
Pre-service is carried out to the laser stripe image that S1 obtains, comprises setting and the adapting to image Threshold segmentation of initial area-of-interest respectively, improve the efficiency of successive image process and reduce noise.
Preferably, in described S3:
To the pretreated laser stripe image that S2 obtains, adopt Canny edge detection algorithm to carry out edge detection process, extract the marginal information of laser stripe; Comprise 2-d gaussian filters process, gradient calculation, gradient non-maximum restraining be connected marginal point.Further, concrete:
First the pretreated laser stripe image, to S2 obtained, carries out convolution algorithm, to eliminate white noise;
Secondly, to each pixel in filtered image, its gradient magnitude and direction is calculated by single order partial differential;
Moreover, adopt non-maximum restraining principle to carry out rim detection, obtain the marginal point of laser stripe;
Finally, marginal point is connected.
Preferably, in described S4:
To the laser stripe marginal information that S3 obtains, search the minimum enclosed rectangle at this edge, thus obtain final area-of-interest.
Preferably, in described S5:
At the final area-of-interest that S4 obtains, adopt and improve the sub-pixel detection that gravity model appoach carries out laser stripe center, comprise rough extraction laser stripe center, boundary intensity threshold value and accurate center extraction; Further, concrete:
First, according to the gray-scale value sum of every row three pixels sort, the pixel position selecting gray-scale value sum maximum is as rough laser stripe center;
Secondly, calculate the average gray of often row pixel, and pixel gray-scale value being greater than average gray carries out quadratic average, as boundary intensity threshold value, rejects the pixel being less than boundary intensity threshold value;
Be finally, with gray-scale value weight, gray-scale value place pixel coordinate is correspondence position, realizes the accurate extraction of laser stripe.
Preferably, in described S6:
The laser stripe marginal information that S3 and S5 is obtained and center line, carry out organically blending of two and three dimensions visual information, and CCD camera, laser plane and motion state are demarcated, the intersection point of laser stripe center line and laser stripe profile is the border of weld gap, distance between two weld gap borders is the width of weld gap, thus realize weld gap in stir friction welding process quick, accurate, automatically extract.
Compared with prior art, the present invention has following beneficial effect:
The method of the invention can a measuring position, realize weld gap three-dimensional coordinate and width information quick, accurately and automatically extract, thus obtain the center location information of weld seam, for subsequent boots stir friction welding process lays the foundation.
Accompanying drawing explanation
By reading the detailed description done non-limiting example with reference to the following drawings, other features, objects and advantages of the present invention will become more obvious:
Fig. 1 is the method flow diagram of one embodiment of the invention;
Fig. 2 is the weld measurement apparatus structure schematic diagram of one embodiment of the invention;
Fig. 3 is the welded seam area raw data schematic diagram of one embodiment of the invention;
Fig. 4 a is the initial area-of-interest schematic diagram of Image semantic classification of one embodiment of the invention;
Fig. 4 b is the Image semantic classification adapting to image Threshold segmentation schematic diagram of one embodiment of the invention;
Fig. 5 is that the edge detection algorithm of one embodiment of the invention extracts laser stripe marginal information schematic diagram;
Fig. 6 is that the self-adaptation area-of-interest of one embodiment of the invention selects schematic diagram;
Fig. 7 is that the improvement gravity model appoach of one embodiment of the invention extracts laser stripe center line schematic diagram;
Fig. 8 is the frontier point schematic diagram of the weld gap of one embodiment of the invention;
In figure: CCD camera 1, camera lens 2, optical filter 3, line source laser 4, workpiece for measurement 5.
Embodiment
Below in conjunction with specific embodiment, the present invention is described in detail.Following examples will contribute to those skilled in the art and understand the present invention further, but not limit the present invention in any form.It should be pointed out that to those skilled in the art, without departing from the inventive concept of the premise, some distortion and improvement can also be made.These all belong to protection scope of the present invention.
As shown in Figure 1, the measuring method of weld gap during the present embodiment provides a kind of agitating friction to weld, concrete enforcement utilizes weld measurement device to carry out, and this experimental provision comprises CCD camera 1, camera lens 2, optical filter 3, line source laser 4 and workpiece for measurement 5, as shown in Figure 2.
Described method comprises the steps:
The image in S1, acquisition weld gap region:
Adopt laser line generator to project a laser plane, formation crossing with surface of the work laser stripe, and laser stripe is crossing with weld gap; CCD camera is installed vertically on directly over weld gap, shooting laser stripe image.
Weld measurement device is arranged on Five-axis NC Machining Center, workpiece for measurement 5 clamping on processing platform; Before measuring, butt welded seam measurement mechanism is demarcated, and demarcates and comprises the demarcation of the inside and outside parameter of CCD camera 1, the demarcation of line source laser instrument 4 laser plane equation and the demarcation of weld measurement device and processing platform relative motion position; During measurement, when processing platform moves along X-direction with 630mm/min, the laser stripe image in weld measurement device Real-time Collection weld gap region, as shown in Figure 3, and transfers to computing machine.
S2, Image semantic classification is carried out to image:
1. the setting of initial area-of-interest
The weld image obtained by step S1, sets initial area-of-interest, in one embodiment, 30326 pixels can be remained altogether herein, compared to 1920000 pixels of original image, greatly for successive image process improves efficiency, as shown in fig. 4 a;
2. adapting to image Threshold segmentation
As shown in Figure 3, laser stripe disconnects at weld gap place, and light-colored part (i.e. research object) is called the bright territory of image, and dark parts (i.e. face of weld entity) is called the dark territory of image; Image can be divided into several specific regions by Iamge Segmentation, and (specific region refers to the threshold range needed for image procossing herein, bright for image territory and the dark regional partition of image are opened), and extract interesting target (the bright territory of image); Wherein, carrying out image threshold segmentation has and calculates simple, higher, the fireballing advantage of operation efficiency; And in practical application, corresponding change can be there is in the gray-scale value of image because of factors such as illumination conditions, Adaptive Thresholding is then adopted to obtain measurement target clearly, so-called Adaptive Thresholding decides according to maximum gradation value in image and minimum gradation value, and the bottom threshold of image is set using empirical value 2/3 as factor of influence, the upper threshold of image is defaulted as 255, thus realizes the segmentation in the bright territory of image and the dark territory of image, as shown in Figure 4 b.
S3, edge detection algorithm is utilized to extract the marginal information of laser stripe:
To the pretreated laser stripe image that step S2 obtains, adopt Canny edge detection algorithm to carry out edge detection process, extract the marginal information of laser stripe; Specifically comprise:
1. 2-d gaussian filters process
Convolution algorithm is carried out to the image that step S2 obtains, eliminates white noise;
2. gradient calculation
To each pixel in filtered image, calculate its gradient magnitude and direction by single order partial differential;
3. the non-maximum restraining of gradient
Adopt non-maximum restraining principle to carry out rim detection, obtain the marginal point of laser stripe;
4. marginal point is connected
According to above-mentioned Threshold segmentation and filtering process, and in conjunction with the non-maximum restraining of gradient image edge information rejected and supplement, marginal information point being coupled together and can obtain laser stripe profile, as shown in Figure 5; As seen from the figure, laser stripe border and toe of the weld are shown as white contours, and other information screens are black.
The selection of S4, self-adaptation area-of-interest:
To the laser stripe marginal information that step S3 obtains, search the minimum enclosed rectangle at this edge, thus obtain final area-of-interest, greatly improve the efficiency of successive image process, Fig. 6 is self-adaptation area-of-interest.
S5, heart method is utilized to extract the center line of laser stripe:
In the final area-of-interest that step S4 obtains, gravity model appoach is adopted to carry out the sub-pixel detection at laser stripe center; Specifically comprise:
1. laser stripe center is extracted roughly
Gray-scale value sum according to every row three pixels sorts, and the pixel position selecting gray-scale value sum maximum is as rough laser stripe center;
2. boundary intensity threshold value
Calculate the average gray of often row pixel, and pixel gray-scale value being greater than average gray carries out quadratic average, as boundary intensity threshold value, reject the pixel being less than boundary intensity threshold value;
3. accurate central point extracts
Finally, take gray-scale value as weight, gray-scale value place pixel coordinate is correspondence position, realize the accurate extraction of laser stripe, as shown in Figure 7.
S6, obtain weld gap information by the profile of laser stripe and center line information:
The laser stripe center line that the laser stripe marginal information (i.e. laser stripe profile) obtained by S3 and S5 are obtained, asks both intersection points to be the frontier point of weld gap, as shown in Figure 8; According to CCD camera 1, the laser plane of line source laser instrument 4 and the calibrating parameters of weld measurement device and processing platform relative motion position before measurement, use principle of triangulation, obtain three-dimensional coordinate and the gap width of weld gap frontier point.
The method of the invention is used for the On-line testing of stir friction welding seam information, and wherein image procossing carries out in OpenCV image processing software, and Data Management Analysis carries out in C++ software systems, and is shown in MFC interface by characteristic information.
The method of the invention can a measuring position, realize weld gap three-dimensional coordinate and width information quick, accurately and automatically extract, thus obtain the center location information of weld seam, for subsequent boots stir friction welding process lays the foundation.
Above specific embodiments of the invention are described.It is to be appreciated that the present invention is not limited to above-mentioned particular implementation, those skilled in the art can make various distortion or amendment within the scope of the claims, and this does not affect flesh and blood of the present invention.
Claims (10)
1. the measuring method of weld gap in agitating friction welding, it is characterized in that, described method comprises the steps:
The image in S1, acquisition weld gap region;
S2, Image semantic classification is carried out to the image that S1 obtains, obtain pretreated laser stripe image;
After S3, the pre-service that obtains S2, laser stripe imagery exploitation edge detection algorithm extracts the marginal information of laser stripe;
S4, the laser stripe marginal information obtained S3 carry out the selection of self-adaptation area-of-interest;
S5, the final area-of-interest obtained S4 utilize and improve the center line that gravity model appoach extracts laser stripe;
S6, obtain weld gap information by the marginal information of the laser stripe of S3 and the center line information of S5.
2. the measuring method of weld gap in a kind of agitating friction welding according to claim 1, it is characterized in that, in described S1: adopt laser line generator to project a laser plane, formation crossing with surface of the work laser stripe, and laser stripe is crossing with weld gap; CCD camera is installed vertically on directly over weld gap, shooting laser stripe image.
3. the measuring method of weld gap in a kind of agitating friction welding according to claim 1, is characterized in that, in described S2: Image semantic classification comprises setting and the adapting to image Threshold segmentation of initial area-of-interest.
4. the measuring method of weld gap in a kind of agitating friction welding according to claim 1, it is characterized in that, in described S3: the pretreated laser stripe image that S2 is obtained, adopt Canny edge detection algorithm to carry out edge detection process, extract the marginal information of laser stripe.
5. the measuring method of weld gap in a kind of agitating friction welding according to claim 4, it is characterized in that, described employing Canny edge detection algorithm carries out edge detection process, concrete processing procedure comprise 2-d gaussian filters process, gradient calculation, gradient non-maximum restraining be connected marginal point:
First the pretreated laser stripe image, to S2 obtained, carries out convolution algorithm, to eliminate white noise;
Secondly, to each pixel in filtered image, its gradient magnitude and direction is calculated by single order partial differential;
Moreover, adopt non-maximum restraining principle to carry out rim detection, obtain the marginal point of laser stripe;
Finally, marginal point is connected.
6. the measuring method of weld gap in a kind of agitating friction welding according to claim 1, is characterized in that, in described S4: the laser stripe marginal information obtained S3, searches the minimum enclosed rectangle at this edge, thus obtain final area-of-interest.
7. the measuring method of weld gap in a kind of agitating friction welding according to any one of claim 1-6, it is characterized in that, in described S5: adopt and improve the sub-pixel detection that gravity model appoach carries out laser stripe center, comprise rough extraction laser stripe center, boundary intensity threshold value and accurate center extraction.
8. the measuring method of weld gap in a kind of agitating friction welding according to claim 7, it is characterized in that, described employing improves the sub-pixel detection that gravity model appoach carries out laser stripe center, is specially:
First, according to the gray-scale value sum of every row three pixels sort, the pixel position selecting gray-scale value sum maximum is as rough laser stripe center;
Secondly, calculate the average gray of often row pixel, and pixel gray-scale value being greater than average gray carries out quadratic average, as boundary intensity threshold value, rejects the pixel being less than boundary intensity threshold value;
Be finally, with gray-scale value weight, gray-scale value place pixel coordinate is correspondence position, realizes the accurate extraction of laser stripe.
9. the measuring method of weld gap in a kind of agitating friction welding according to any one of claim 1-6, it is characterized in that, in described S6: the laser stripe marginal information that S3 and S5 is obtained and center line, carry out organically blending of two and three dimensions visual information, and to CCD camera, laser plane and motion state are demarcated, the intersection point of laser stripe center line and laser stripe marginal information is the border of weld gap, distance between two weld gap borders is the width of weld gap, thus realize the quick of weld gap in stir friction welding process, accurately, automatic extraction.
10. the measuring method of weld gap in a kind of agitating friction welding according to any one of claim 1-6, it is characterized in that, described method is used for the On-line testing of stir friction welding seam information, wherein image procossing carries out in OpenCV image processing software, Data Management Analysis carries out in C++ software systems, and is shown in MFC interface by characteristic information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201511019117.5A CN105571502B (en) | 2015-12-29 | 2015-12-29 | The measurement method of weld gap in Friction Stir Welding |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201511019117.5A CN105571502B (en) | 2015-12-29 | 2015-12-29 | The measurement method of weld gap in Friction Stir Welding |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN105571502A true CN105571502A (en) | 2016-05-11 |
| CN105571502B CN105571502B (en) | 2019-08-09 |
Family
ID=55881909
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201511019117.5A Active CN105571502B (en) | 2015-12-29 | 2015-12-29 | The measurement method of weld gap in Friction Stir Welding |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105571502B (en) |
Cited By (25)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106017341A (en) * | 2016-07-15 | 2016-10-12 | 湖北三江航天红阳机电有限公司 | Shaft hole part assembly gap monitoring system |
| CN107764205A (en) * | 2017-11-06 | 2018-03-06 | 长安大学 | High-frequency resistance welding (HFRW) seam center three-dimensional detection device and detection method are scanned based on line-structured light |
| CN107843202A (en) * | 2017-12-22 | 2018-03-27 | 易思维（天津）科技有限公司 | A kind of gap, face difference vision measurement device and method being used cooperatively with robot |
| CN107957247A (en) * | 2017-12-22 | 2018-04-24 | 易思维（天津）科技有限公司 | A kind of hand-held gap, face difference vision measurement device and method |
| CN108344693A (en) * | 2018-02-14 | 2018-07-31 | 东北大学 | A kind of oil conservater thin board welding seam unfitness of butt joint vision measurement device and method |
| CN108830841A (en) * | 2016-08-30 | 2018-11-16 | 大连民族大学 | The calculation method of laser image characteristic quantity selection threshold value |
| CN109128488A (en) * | 2018-10-08 | 2019-01-04 | 北京众视精控科技有限公司 | Weld seam automatic tracking system based on polarized illumination |
| CN109615603A (en) * | 2018-10-27 | 2019-04-12 | 南昌大学 | A kind of visual attention model of task based access control driving extracts the universal method of laser stripe |
| CN110794389A (en) * | 2019-11-18 | 2020-02-14 | 淮阴工学院 | Image processing-based non-contact distance measuring method |
| WO2020042032A1 (en) * | 2018-08-29 | 2020-03-05 | 深圳配天智能技术研究院有限公司 | Method and system for detecting gap of visual welding system |
| CN110887787A (en) * | 2019-10-30 | 2020-03-17 | 成都绝影智能科技有限公司 | Narrow slit detection and positioning laser measuring device and measuring method thereof |
| CN110930368A (en) * | 2019-10-31 | 2020-03-27 | 中船重工鹏力（南京）智能装备系统有限公司 | Method for extracting characteristics of real-time welding image of sheet lap weld |
| CN111918741A (en) * | 2018-08-29 | 2020-11-10 | 深圳配天智能技术研究院有限公司 | Gap detection method and system for visual welding system |
| CN112197715A (en) * | 2020-10-27 | 2021-01-08 | 上海市特种设备监督检验技术研究院 | Elevator brake wheel and brake shoe gap detection method based on image recognition |
| CN112361977A (en) * | 2020-11-10 | 2021-02-12 | 成都新西旺自动化科技有限公司 | Linear distance measuring method based on weight distribution |
| CN112355478A (en) * | 2020-10-30 | 2021-02-12 | 北京博清科技有限公司 | Welding control method and device and electronic equipment |
| CN112508971A (en) * | 2020-12-21 | 2021-03-16 | 华南理工大学 | Weld positioning method and system based on simulated structural light stripes and storage medium |
| CN112541925A (en) * | 2020-12-15 | 2021-03-23 | 华南理工大学 | Image interesting region intercepting method and system combined with visual simulation and storage medium |
| CN112837307A (en) * | 2021-02-24 | 2021-05-25 | 北京博清科技有限公司 | Method, device, processor and system for determining welding bead profile |
| CN113137932A (en) * | 2021-05-14 | 2021-07-20 | 淮阴工学院 | Portable surface clearance measuring device and measuring method |
| CN113483682A (en) * | 2021-06-16 | 2021-10-08 | 苏州赛腾精密电子股份有限公司 | Gap measuring method and system based on machine vision |
| CN113649672A (en) * | 2021-08-06 | 2021-11-16 | 武汉理工大学 | Adaptive extraction method for geometric characteristics of butt weld |
| CN114111576A (en) * | 2021-11-24 | 2022-03-01 | 易思维（杭州）科技有限公司 | Aircraft skin clearance surface difference detection method and sensor |
| CN114620091A (en) * | 2022-04-11 | 2022-06-14 | 南京拓控信息科技股份有限公司 | Train wheel out-of-roundness detection method based on three-dimensional information |
| CN115953459A (en) * | 2023-03-10 | 2023-04-11 | 齐鲁工业大学(山东省科学院) | Method for extracting laser stripe center line under complex illumination condition |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004069446A (en) * | 2002-08-06 | 2004-03-04 | Nippon Sheet Glass Co Ltd | Thickness and gap measuring instrument |
| CN102297658A (en) * | 2011-05-20 | 2011-12-28 | 南京航空航天大学 | Three-dimensional information detection method based on dual laser |
| US8149409B2 (en) * | 2005-12-23 | 2012-04-03 | Posco | Apparatus and method for on-line detecting welding part of strip |
| CN102779279A (en) * | 2012-06-14 | 2012-11-14 | 南京航空航天大学 | Image identification method of stirring friction welding arc stripe interval |
| CN102853777A (en) * | 2012-10-15 | 2013-01-02 | 山东大学 | Braking gap width measuring method and equipment based on image processing |
| CN104535006A (en) * | 2015-01-21 | 2015-04-22 | 杭州电子科技大学 | Bottle cap gap width estimation method by using transmission type illuminating and imaging system |
| KR20150074680A (en) * | 2013-12-24 | 2015-07-02 | 주식회사 포스코 | Apparatus and method of measuring gap between nozzle and cooling wheel using rear light in manufacturing amorphous fiber |
-
2015
- 2015-12-29 CN CN201511019117.5A patent/CN105571502B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2004069446A (en) * | 2002-08-06 | 2004-03-04 | Nippon Sheet Glass Co Ltd | Thickness and gap measuring instrument |
| US8149409B2 (en) * | 2005-12-23 | 2012-04-03 | Posco | Apparatus and method for on-line detecting welding part of strip |
| CN102297658A (en) * | 2011-05-20 | 2011-12-28 | 南京航空航天大学 | Three-dimensional information detection method based on dual laser |
| CN102779279A (en) * | 2012-06-14 | 2012-11-14 | 南京航空航天大学 | Image identification method of stirring friction welding arc stripe interval |
| CN102853777A (en) * | 2012-10-15 | 2013-01-02 | 山东大学 | Braking gap width measuring method and equipment based on image processing |
| KR20150074680A (en) * | 2013-12-24 | 2015-07-02 | 주식회사 포스코 | Apparatus and method of measuring gap between nozzle and cooling wheel using rear light in manufacturing amorphous fiber |
| CN104535006A (en) * | 2015-01-21 | 2015-04-22 | 杭州电子科技大学 | Bottle cap gap width estimation method by using transmission type illuminating and imaging system |
Non-Patent Citations (3)
| Title |
|---|
| 吴家勇等: "基于梯度重心法的线结构光中心亚像素提取方法", 《中国图象图形学报》 * |
| 王伟等: "焊缝激光跟踪图像处理中关键技术的研究", 《光电工程》 * |
| 贺俊吉等: "结构光三维视觉检测中光条图像处理方法研究", 《北京航空航天大学学报》 * |
Cited By (36)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106017341A (en) * | 2016-07-15 | 2016-10-12 | 湖北三江航天红阳机电有限公司 | Shaft hole part assembly gap monitoring system |
| CN108830841B (en) * | 2016-08-30 | 2021-11-26 | 大连民族大学 | Method for calculating laser image characteristic quantity selection threshold |
| CN108830841A (en) * | 2016-08-30 | 2018-11-16 | 大连民族大学 | The calculation method of laser image characteristic quantity selection threshold value |
| CN107764205A (en) * | 2017-11-06 | 2018-03-06 | 长安大学 | High-frequency resistance welding (HFRW) seam center three-dimensional detection device and detection method are scanned based on line-structured light |
| CN107957247B (en) * | 2017-12-22 | 2023-09-08 | 易思维（天津）科技有限公司 | Handheld gap and surface difference vision measurement device and method |
| CN107843202A (en) * | 2017-12-22 | 2018-03-27 | 易思维（天津）科技有限公司 | A kind of gap, face difference vision measurement device and method being used cooperatively with robot |
| CN107957247A (en) * | 2017-12-22 | 2018-04-24 | 易思维（天津）科技有限公司 | A kind of hand-held gap, face difference vision measurement device and method |
| CN107843202B (en) * | 2017-12-22 | 2023-09-08 | 易思维（天津）科技有限公司 | Gap and face difference vision measurement device and method matched with robot |
| CN108344693B (en) * | 2018-02-14 | 2020-05-05 | 东北大学 | Automatic welding-oriented visual measurement method for misalignment of sheet welding seam |
| CN108344693A (en) * | 2018-02-14 | 2018-07-31 | 东北大学 | A kind of oil conservater thin board welding seam unfitness of butt joint vision measurement device and method |
| CN111918741A (en) * | 2018-08-29 | 2020-11-10 | 深圳配天智能技术研究院有限公司 | Gap detection method and system for visual welding system |
| WO2020042032A1 (en) * | 2018-08-29 | 2020-03-05 | 深圳配天智能技术研究院有限公司 | Method and system for detecting gap of visual welding system |
| CN109128488A (en) * | 2018-10-08 | 2019-01-04 | 北京众视精控科技有限公司 | Weld seam automatic tracking system based on polarized illumination |
| CN109615603A (en) * | 2018-10-27 | 2019-04-12 | 南昌大学 | A kind of visual attention model of task based access control driving extracts the universal method of laser stripe |
| CN109615603B (en) * | 2018-10-27 | 2022-07-12 | 南昌大学 | Method for extracting laser stripes based on task-driven visual attention model |
| CN110887787A (en) * | 2019-10-30 | 2020-03-17 | 成都绝影智能科技有限公司 | Narrow slit detection and positioning laser measuring device and measuring method thereof |
| CN110930368A (en) * | 2019-10-31 | 2020-03-27 | 中船重工鹏力（南京）智能装备系统有限公司 | Method for extracting characteristics of real-time welding image of sheet lap weld |
| CN110930368B (en) * | 2019-10-31 | 2022-02-18 | 中船重工鹏力（南京）智能装备系统有限公司 | Method for extracting characteristics of real-time welding image of sheet lap weld |
| CN110794389A (en) * | 2019-11-18 | 2020-02-14 | 淮阴工学院 | Image processing-based non-contact distance measuring method |
| CN110794389B (en) * | 2019-11-18 | 2023-02-28 | 淮阴工学院 | Image processing-based non-contact distance measurement method |
| CN112197715B (en) * | 2020-10-27 | 2022-07-08 | 上海市特种设备监督检验技术研究院 | Elevator brake wheel and brake shoe gap detection method based on image recognition |
| CN112197715A (en) * | 2020-10-27 | 2021-01-08 | 上海市特种设备监督检验技术研究院 | Elevator brake wheel and brake shoe gap detection method based on image recognition |
| CN112355478A (en) * | 2020-10-30 | 2021-02-12 | 北京博清科技有限公司 | Welding control method and device and electronic equipment |
| CN112361977B (en) * | 2020-11-10 | 2021-05-28 | 成都新西旺自动化科技有限公司 | Linear distance measuring method based on weight distribution |
| CN112361977A (en) * | 2020-11-10 | 2021-02-12 | 成都新西旺自动化科技有限公司 | Linear distance measuring method based on weight distribution |
| CN112541925A (en) * | 2020-12-15 | 2021-03-23 | 华南理工大学 | Image interesting region intercepting method and system combined with visual simulation and storage medium |
| CN112508971B (en) * | 2020-12-21 | 2023-08-22 | 华南理工大学 | Weld joint positioning method, system and storage medium based on simulation structure light stripe |
| CN112508971A (en) * | 2020-12-21 | 2021-03-16 | 华南理工大学 | Weld positioning method and system based on simulated structural light stripes and storage medium |
| CN112837307A (en) * | 2021-02-24 | 2021-05-25 | 北京博清科技有限公司 | Method, device, processor and system for determining welding bead profile |
| CN113137932B (en) * | 2021-05-14 | 2023-02-28 | 淮阴工学院 | Portable surface clearance measuring device and measuring method |
| CN113137932A (en) * | 2021-05-14 | 2021-07-20 | 淮阴工学院 | Portable surface clearance measuring device and measuring method |
| CN113483682A (en) * | 2021-06-16 | 2021-10-08 | 苏州赛腾精密电子股份有限公司 | Gap measuring method and system based on machine vision |
| CN113649672A (en) * | 2021-08-06 | 2021-11-16 | 武汉理工大学 | Adaptive extraction method for geometric characteristics of butt weld |
| CN114111576A (en) * | 2021-11-24 | 2022-03-01 | 易思维（杭州）科技有限公司 | Aircraft skin clearance surface difference detection method and sensor |
| CN114620091A (en) * | 2022-04-11 | 2022-06-14 | 南京拓控信息科技股份有限公司 | Train wheel out-of-roundness detection method based on three-dimensional information |
| CN115953459A (en) * | 2023-03-10 | 2023-04-11 | 齐鲁工业大学(山东省科学院) | Method for extracting laser stripe center line under complex illumination condition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN105571502B (en) | 2019-08-09 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105571502A (en) | Measuring method of weld gap in friction-stir welding | |
| CN210046133U (en) | Welding seam visual tracking system based on laser structured light | |
| Shah et al. | Butt welding joints recognition and location identification by using local thresholding | |
| CN103499585B (en) | Based on noncontinuity lithium battery film defect inspection method and the device thereof of machine vision | |
| CN102175700B (en) | Method for detecting welding seam segmentation and defects of digital X-ray images | |
| CN106181162B (en) | A kind of real-time weld joint tracking detection method based on machine vision | |
| CN105913415B (en) | A kind of image sub-pixel edge extracting method with extensive adaptability | |
| CN102496161B (en) | Method for extracting contour of image of printed circuit board (PCB) | |
| CN110363781B (en) | Molten pool contour detection method based on deep neural network | |
| CN106990112B (en) | Multi-layer multi-pass welding track detection device and method based on multi-visual information fusion | |
| CN102455171B (en) | Method for detecting geometric shape of back of tailor-welding weld and implementing device thereof | |
| CN103759648A (en) | Complex fillet weld joint position detecting method based on laser binocular vision | |
| CN107909571A (en) | A kind of weld beam shape method, system, equipment and computer-readable storage medium | |
| CN103993548A (en) | Multi-camera stereoscopic shooting based pavement damage crack detection system and method | |
| CN114252449B (en) | Aluminum alloy weld joint surface quality detection system and method based on line structured light | |
| CN109030502A (en) | A kind of welding point defect detection system based on machine vision | |
| CN104777174A (en) | Shaft type part section abrupt change position surface fatigue crack detection system and method thereof | |
| CN115601359A (en) | Welding seam detection method and device | |
| CN105023018A (en) | Jet code detection method and system | |
| CN113554587A (en) | Molten pool image geometric feature extraction method and system based on deep learning | |
| CN115713476A (en) | Visual detection method and device based on laser welding and readable storage medium | |
| Lin et al. | Intelligent seam tracking of an ultranarrow gap during K-TIG welding: a hybrid CNN and adaptive ROI operation algorithm | |
| CN114113116A (en) | Accurate detection process method for micro-defects on surface of large-diameter element | |
| CN116664508A (en) | Weld surface quality detection method and computer readable storage medium | |
| CN117102727A (en) | Control method and control device for real-time welding quality and welding system |
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 |