CN107796818A - The method of on-line checking bi-metal bandsaw blades welding quality - Google Patents
The method of on-line checking bi-metal bandsaw blades welding quality Download PDFInfo
- Publication number
- CN107796818A CN107796818A CN201810078718.0A CN201810078718A CN107796818A CN 107796818 A CN107796818 A CN 107796818A CN 201810078718 A CN201810078718 A CN 201810078718A CN 107796818 A CN107796818 A CN 107796818A
- Authority
- CN
- China
- Prior art keywords
- welding
- affected zone
- particle
- heat
- area
- 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
- 238000003466 welding Methods 0.000 title claims abstract description 90
- 238000000034 method Methods 0.000 title claims abstract description 33
- 239000002184 metal Substances 0.000 title claims abstract description 12
- 239000002245 particle Substances 0.000 claims abstract description 47
- 238000001514 detection method Methods 0.000 claims abstract description 18
- 238000004458 analytical method Methods 0.000 claims abstract description 7
- 238000007619 statistical method Methods 0.000 claims abstract description 6
- 230000011218 segmentation Effects 0.000 claims description 5
- 238000012360 testing method Methods 0.000 abstract description 4
- 238000007689 inspection Methods 0.000 abstract description 3
- 238000005520 cutting process Methods 0.000 description 4
- 238000010586 diagram Methods 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000004927 fusion Effects 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 239000000047 product Substances 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 238000007796 conventional method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002028 premature Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 239000011265 semifinished product Substances 0.000 description 1
- 238000010301 surface-oxidation reaction Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B23—MACHINE TOOLS; METAL-WORKING NOT OTHERWISE PROVIDED FOR
- B23K—SOLDERING OR UNSOLDERING; WELDING; CLADDING OR PLATING BY SOLDERING OR WELDING; CUTTING BY APPLYING HEAT LOCALLY, e.g. FLAME CUTTING; WORKING BY LASER BEAM
- B23K37/00—Auxiliary devices or processes, not specially adapted to a procedure covered by only one of the preceding main groups
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Analytical Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- Biochemistry (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Geometry (AREA)
- Chemical & Material Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Optics & Photonics (AREA)
- Mechanical Engineering (AREA)
- Investigating And Analyzing Materials By Characteristic Methods (AREA)
Abstract
The present invention relates to the method for on-line checking bi-metal bandsaw blades welding quality, and in bi-metal bandsaw blades welding process, the image information of welding position after the completion of being welded in base band is obtained by vision-based detection module；Gray scale adjustment is carried out to the image information of acquisition, the area S in welding position heat-affected zone after the completion of analysis calculating is welded1；The welding of a particle is often completed, just by the above method, in the synchronization after the completion of welding or in the period, the image information of welding position after the completion of the welding is obtained and analyzes the area S for calculating acquisition associated heat-affected zone domainn；To area S1、……、SnStatistical analysis is carried out, so as to judge stability of the welding quality.The method of the present invention belongs to on-line checking, it is not necessary to destroys saw blade, testing cost is low；Belong to full inspection method, each pad is detected, it is with a high credibility；Based on statistical method, the stability of entire volume saw blade welding quality can interpolate that.
Description
Technical field
The present invention relates to the method for detecting bi-metal bandsaw blades welding quality, more particularly, to detects individual particle weldering
The method for connecing formula bands for band welding quality.
Background technology
For individual particle welded band saw blade, the weld strength and stability of particle and base band are assess its quality important
Index and parameter.Multiple sawtooth on bands for band be present, the cutting force that these sawtooth are born in working angles passes by weld seam
Lead to base band.In the case where particle and base band bond strength are inadequate, weld seam can be peeled off by drawing crack, particle from base band.Peel off
The cutting output of the sawtooth to get off can be transferred on next sawtooth, undertaken by next sawtooth, cause the cutting output mistake of this sawtooth
Greatly, cutting force is excessive, it is also possible to drawing crack occurs, peels off.So as to occur to be similar to " domino " phenomenon, multiple sawtooth by drawing crack,
Peel off.On the other hand, weld strength has to uniformly, ensure that error in certain limit, is avoided because of part sawtooth weld strength not
Enough cause whole saw blade premature failure.
At present, the conventional method to the detection of particle welded band saw blade weld strength is to cut the saw blade after welding, is taken
One section of sample, weld strength detection then is carried out using the method for damage type, belong to off-line type detection.
In addition, for particle welded band saw blade, the position that weld seam drawing crack occurs in use is welding
Unusual relevance be present with heat affecting and fusion sites in heat affecting and fusion sites, rather than position while welding, i.e. weld strength.
The major defect of off-line type detection is to need to destroy saw blade, can only be detected end to end particularly with coiled saw blade, no
Then need to cut off from centre, subsequent production taeniae telarum is influenceed.Secondly, sampling observation is belonged to, the reliability of detection is by sampling frequency
The limitation of rate.
The content of the invention
In view of the shortcomings of the prior art, the present invention provides a kind of method of on-line checking bi-metal bandsaw blades welding quality,
To realize the on-line checking of welding stability.
In order to solve the above-mentioned technical problem, technical scheme is as follows：On-line checking bi-metal bandsaw blades welds matter
The method of amount, comprises the following steps：
（1）, in bi-metal bandsaw blades particle welding process, by vision-based detection module obtain base band on particle welding after the completion of
The image information of welding position；
（2）, to step（1）The image information of middle acquisition carries out gray scale adjustment, and analysis is calculated to obtain after the completion of particle welds and welded
The area S in position heat-affected zone1；
（3）, pass through step（1）With（2）, the image information of welding position after the completion of each particle welding is obtained successively, and is analyzed
Calculate the area S for obtaining associated heat-affected zone domaini；
Wherein, i=1,2,3 ... ..., n-1, n；N is the positive integer not less than 2；
（4）, to step（3）The area S of middle acquisition1、S2、……、SnStatistical analysis is carried out, obtains heat-affected zone area
Data are fluctuated, the fluctuation data are compared with desired value or empirical value, judge stability of the welding quality.
In the present invention, it is contemplated that value refers to the value planned in advance according to product quality requirement；Empirical value refers to according to previous weldering
Connect quality measurements and summarize the value obtained.Step（4）In, when the value for fluctuating data exceedes desired value or empirical value, then say
Bright stability of the welding quality is poor, and each welding position weld strength differs greatly in other words；It is expected when the value of the fluctuation data is less than
When value or empirical value, then illustrate that stability of the welding quality is good.
Step（1）In, the vision-based detection module is arranged above the subsequent work stations of welding post, and such particle is once weldering
Completion is connect, the pusher dog being soldered at once in equipment is sent into subsequent work stations, and vision-based detection module can obtain the welding and complete position
Image information.
In the present invention, welding post refers to the job position being welded to particle in base band tooth support, is normally at welding and sets
Standby a certain fixed operation position.
Preferably, the lens axis of vision-based detection module is vertical with plane where base band, so, camera lens face base band upper table
Face is set, and the image obtained under the conditions of being somebody's turn to do can more intuitively reflect the size of heat-affected zone area.
Step（2）In, particle outline line, base band contour line and heat-affected zone are extracted by gray level threshold segmentation method
Boundary line, the area for obtaining the particle outline line, base band contour line and the boundary line Suo Wei regions in heat-affected zone is calculated,
To characterize the size in heat-affected zone.Wherein, particle outline line can be the profile that particle is placed on part in air
Line, can also make the contour line of particle and base band junction section, both of which can, to step（4）Result of determination without materially affect.
When individual particle welds, under Thermal Cycle effect, obvious group can be occurred in the base band tooth support of weld seam sidepiece
Knit and performance change, the region that the tissue and performance change is the heat-affected zone of tooth support；Meanwhile each portion in tooth support surface
Position, because heated situation differs, surface oxidation degree is different, so after the completion of welding heat-affected zone surface color and the depth
Obvious difference is had with non-heat-affected zone in tooth support, is had at the interface surfaces in non-heat-affected zone and heat-affected zone bright
Aobvious color and the depth change, and are found in applicant's pilot production, and the change can be told by naked eyes.Therefore, the application
By first obtaining the image information of welding position, gray scale adjustment then is carried out to image information, can be apparent by gray scale adjustment
Ground identifies the boundary line in heat-affected zone, and the profile and the profile of particle and the background of image at base band tooth support position have
Significant difference, base band contour line and particle outline line easily determine that boundary line, base band contour line and particle outline line enclose region
The size of heat-affected zone area can be characterized.
At the different each welding position of weld strength, the corresponding Thermal Cycle operative condition of its in welding process is not
Together, the boundary line in heat-affected zone and non-heat-affected zone（Boundary line）Present position can be different in tooth support, passes through this Shen
The size in the heat-affected zone that method please obtains is also different, that is to say, that the size in heat-affected zone and welding
Certain corresponding relation be present in intensity, thus, by carrying out statistical analysis to the heat-affected zone area of each welding position, obtain
The fluctuation data of heat-affected zone area are obtained, the situation of change of weld strength can be reflected exactly, so as to judge welding quality
Stability.
The image information of welding position is same after the completion of individual particle welding after the completion of each particle welding
Obtained in moment or same period, to reduce variable number as far as possible, be further ensured that the real reliability of result.
Step（3）In, welding position can be welding continuously distributed successively in base band after the completion of each particle welding
Position, or the welding position being spaced apart.
Step（4）In, the fluctuation data include deviation, standard deviation, standard deviation and the side of heat-affected zone area
One or more in difference.
Step（4）In, reference area S1、S2、……、SnAverage value, obtain area SnCorresponding deviation it is absolute
Value, when the absolute value of the deviation exceedes the certain value of average value（Such as 10%）When, it can work as and think welding quality（Or weld strength）
Varied widely, can now feed back signal to welding system adjustment or manual intervention processing.So can be in monovolume base band
In welding process, welding quality situation is monitored in real time, to take measures in time, ensures the stability of welding quality.
, can be to area S in order to detect the stability of entire volume base band welding quality1、S2、……、SnCarry out standard deviation, standard
Deviation or variance analysis, when analysis result shows that undulating value exceedes desired value or empirical value, then it is believed that fluctuation is excessive, weldering
Connect that quality stability is poor, the weld strength deficiency of some of which particle can be assert, can now adjust welding equipment, or weldering accordingly
Parameter is connect, or scraps semi-finished product.
Compared with the detection of off-line type, patent of the present invention has the advantages that：
1）Belong to on-line checking, it is not necessary to destroy saw blade, testing cost is low；
2）Belong to full inspection method, each pad is detected, it is with a high credibility；
3）Based on statistical method, the stability of entire volume saw blade welding quality can interpolate that.
Brief description of the drawings
Fig. 1 is the relative position relation figure of vision-based detection module and base band in the first embodiment of the invention（Face
Figure）.
Fig. 2 is the relative position relation figure of vision-based detection module and base band in the first embodiment of the invention（Overlook
Figure）.
Fig. 3 is image situation map after gray proces in welding position in the first embodiment of the invention.
Fig. 4 is situation maps of the Fig. 3 after gray level threshold segmentation is handled.
Fig. 5 is to extract particle outline line, base band profile by gray level threshold segmentation method in the first embodiment of the invention
The process schematic of the boundary line in line and heat-affected zone,（a）Particle outline line and base band contour line extraction schematic diagram；（b）
Heat-affected zone border line drawing schematic diagram；（c）Heat-affected zone boundary line position view；（d）Characterize heat-affected zone
Area schematic.
In figure, 1- vision-based detection modules, 2- base band, 3- particles, 4- boundary lines.
Embodiment
Describe the present invention in detail below with reference to accompanying drawing and in conjunction with the embodiments.It should be noted that in the feelings not conflicted
Under condition, the feature in embodiment and embodiment in the present invention can be mutually combined.For sake of convenience, hereinafter as occurred
" on ", " under ", "left", "right" printed words, only represent that the upper and lower, left and right direction with accompanying drawing in itself is consistent, do not act limiting to structure
It is set for using.
The method of on-line checking bi-metal bandsaw blades welding quality, comprises the following steps：
（1）, base band to be welded is installed on bi-metal bandsaw blades welding equipment, proceed by welding, pass through vision-based detection mould
Block obtains the image information of welding position after the completion of particle welding in base band；
（2）, to step（1）The image information of middle acquisition carries out gray scale adjustment, and analysis is calculated to obtain after the completion of particle welds and welded
The area S in position heat-affected zone1；
（3）, often complete a particle welding, just pass through step（1）With（2）Method, the synchronization after the completion of welding
Or in the period, obtain the image information of welding position after the completion of the welding and analyze the face for calculating and obtaining associated heat-affected zone domain
Product Sn；
Wherein, n is the natural number not less than 2；
（4）, to area S1、S2、……、SnVariance analysis is carried out, the variance of heat-affected zone area is obtained, when the value of the variance
During more than empirical value, then illustrate that stability of the welding quality is poor, each welding position weld strength differs greatly in other words；When the fluctuation
When the value of data is less than desired value or empirical value, then illustrate that stability of the welding quality is good.
Step（1）In, the vision-based detection module is arranged at next station of welding post（After the completion of welding, through dialling
After pawl carries out a feeding, the previous station that is reached of welding position for completing welding）Top.
Step（2）In, particle outline line, base band contour line and heat-affected zone are extracted by gray level threshold segmentation method
Boundary line, calculate the area for obtaining particle outline line, base band contour line and the boundary line Suo Wei regions in heat-affected zone（Such as figure
Shown in 4, the dash area with oblique line）, to characterize the size in heat-affected zone.Specifically, as shown in figure 5, gray scale threshold
It is as follows to be worth dividing method, first, by the difference of particle after welding and tooth Torquay band and welding background can extract particle and
The contour line of base band tooth support, it is specific such as Fig. 5（a）It is shown；Then, heat-affected zone in tooth support after welding is passed through（Close to particle
Gray area）With non-heat-affected zone（Such as Fig. 5（b）Shown in middle black solid portion）Difference, extraction obtain heat-affected zone
Boundary line 4（Such as Fig. 5（c）It is shown）, present embodiment selects base band contour line, particle outer contour and heat-affected zone border
Line encloses area attribute heat-affected zone size（Such as Fig. 5（d）Shown in middle black solid portion）.
According to testing result, can the adjustment of Reasonable adjustment welding condition, such as welding current, voltage, discharge time, adjust
Whole thermal region area, that is, adjust weld strength.
For a certain bands for band, area different welding position in heat affected area can be detected by the method for offline inspection
Weld strength, build heat affected area area and weld strength corresponding relation database, more intuitively testing result can be obtained.
Present invention is particularly suitable for welding parameter in welding process（Such as entire volume saw blade）In the occasion that does not change.
The content that above-described embodiment illustrates should be understood to that these embodiments are only used for being illustrated more clearly that the present invention, without
For limiting the scope of the present invention, after the present invention has been read, the various equivalent form of values of the those skilled in the art to the present invention
Modification each fall within the application appended claims limited range.
Claims (5)
1. the method for on-line checking bi-metal bandsaw blades welding quality, it is characterised in that comprise the following steps：
（1）, in bi-metal bandsaw blades particle welding process, by vision-based detection module obtain base band on particle welding after the completion of
The image information of welding position；
（2）, to step（1）The image information of middle acquisition carries out gray scale adjustment, and analysis is calculated to obtain after the completion of particle welds and welded
The area S in position heat-affected zone1；
（3）, pass through step（1）With（2）, the image information of welding position after the completion of each particle welding is obtained successively, and is analyzed
Calculate the area S for obtaining associated heat-affected zone domaini；
Wherein, i=1,2,3 ... ..., n-1, n；N is the positive integer not less than 2；
（4）, to step（3）The area S of middle acquisition1、S2、……、SnStatistical analysis is carried out, obtains heat-affected zone area
Data are fluctuated, the fluctuation data are compared with desired value or empirical value, judge stability of the welding quality.
2. according to the method for claim 1, it is characterised in that step（1）In, the vision-based detection module is arranged at welding
Above the subsequent work stations of station.
3. according to the method for claim 1, it is characterised in that step（2）In, particle is extracted by gray level threshold segmentation method
The boundary line of contour line, base band contour line and heat-affected zone, calculate obtain the particle outline line, base band contour line and
The area in the boundary line Suo Wei regions in heat-affected zone, to characterize the size in heat-affected zone.
4. according to the method for claim 1, it is characterised in that the image of welding position after the completion of each particle welding
Information individual particle welding after the completion of synchronization or obtained in the same period.
5. according to the method for claim 1, it is characterised in that step（4）In, the fluctuation data include heat-affected zone
One or more in the deviation of area, standard deviation, standard deviation and variance.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810078718.0A CN107796818B (en) | 2018-01-23 | 2018-01-23 | The method of on-line checking bi-metal bandsaw blades welding quality |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810078718.0A CN107796818B (en) | 2018-01-23 | 2018-01-23 | The method of on-line checking bi-metal bandsaw blades welding quality |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107796818A true CN107796818A (en) | 2018-03-13 |
| CN107796818B CN107796818B (en) | 2018-05-11 |
Family
ID=61538543
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810078718.0A Active CN107796818B (en) | 2018-01-23 | 2018-01-23 | The method of on-line checking bi-metal bandsaw blades welding quality |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107796818B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108918309A (en) * | 2018-08-22 | 2018-11-30 | 广州亨龙智能装备股份有限公司 | The solder joint detection method and solder joint detection device of galvanized steel plain sheet |
| CN112059388A (en) * | 2020-09-10 | 2020-12-11 | 湖南泰嘉新材料科技股份有限公司 | Method and device for monitoring welding quality of bimetal band saw blade |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1058170A (en) * | 1996-08-19 | 1998-03-03 | Mazda Motor Corp | Method and device for judging quality of laser beam welding |
| KR20020009131A (en) * | 2000-07-24 | 2002-02-01 | 신현준 | Misalignment detection apparatus for flash butt welder |
| JP2004301611A (en) * | 2003-03-31 | 2004-10-28 | Nisshin Steel Co Ltd | Method and apparatus for detecting crack in thermally affected part of plated steel pipe |
| JP2007155578A (en) * | 2005-12-07 | 2007-06-21 | Omron Corp | Welding quality inspection device, welding quality inspection method, and program |
| JP2008196866A (en) * | 2007-02-08 | 2008-08-28 | Toyota Motor Corp | Weld crack detection method and device |
| CN101324469A (en) * | 2008-08-06 | 2008-12-17 | 中国航空工业第一集团公司北京航空制造工程研究所 | Three-dimensional measurement method for laser welding temperature field |
| CN101788504A (en) * | 2009-01-27 | 2010-07-28 | 欧姆龙株式会社 | The quality management of component-mounted substrate is with information display system and method for information display |
| CN104515778A (en) * | 2015-01-15 | 2015-04-15 | 无锡北斗星通信息科技有限公司 | Yarn quality monitoring and control system based on image identification |
| CN106226307A (en) * | 2016-07-05 | 2016-12-14 | 上海交通大学 | A kind of method measuring 617 nickel-base alloy heat affected area length |
| CN107402217A (en) * | 2017-07-27 | 2017-11-28 | 哈尔滨工业大学（威海） | The inline diagnosis method of the laser gain material manufacturing defect of view-based access control model sensing |
-
2018
- 2018-01-23 CN CN201810078718.0A patent/CN107796818B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH1058170A (en) * | 1996-08-19 | 1998-03-03 | Mazda Motor Corp | Method and device for judging quality of laser beam welding |
| KR20020009131A (en) * | 2000-07-24 | 2002-02-01 | 신현준 | Misalignment detection apparatus for flash butt welder |
| JP2004301611A (en) * | 2003-03-31 | 2004-10-28 | Nisshin Steel Co Ltd | Method and apparatus for detecting crack in thermally affected part of plated steel pipe |
| JP2007155578A (en) * | 2005-12-07 | 2007-06-21 | Omron Corp | Welding quality inspection device, welding quality inspection method, and program |
| JP2008196866A (en) * | 2007-02-08 | 2008-08-28 | Toyota Motor Corp | Weld crack detection method and device |
| CN101324469A (en) * | 2008-08-06 | 2008-12-17 | 中国航空工业第一集团公司北京航空制造工程研究所 | Three-dimensional measurement method for laser welding temperature field |
| CN101788504A (en) * | 2009-01-27 | 2010-07-28 | 欧姆龙株式会社 | The quality management of component-mounted substrate is with information display system and method for information display |
| CN104515778A (en) * | 2015-01-15 | 2015-04-15 | 无锡北斗星通信息科技有限公司 | Yarn quality monitoring and control system based on image identification |
| CN106226307A (en) * | 2016-07-05 | 2016-12-14 | 上海交通大学 | A kind of method measuring 617 nickel-base alloy heat affected area length |
| CN107402217A (en) * | 2017-07-27 | 2017-11-28 | 哈尔滨工业大学（威海） | The inline diagnosis method of the laser gain material manufacturing defect of view-based access control model sensing |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108918309A (en) * | 2018-08-22 | 2018-11-30 | 广州亨龙智能装备股份有限公司 | The solder joint detection method and solder joint detection device of galvanized steel plain sheet |
| CN108918309B (en) * | 2018-08-22 | 2021-04-02 | 广州亨龙智能装备股份有限公司 | Welding spot detection method and welding spot detection equipment for galvanized steel sheet |
| CN112059388A (en) * | 2020-09-10 | 2020-12-11 | 湖南泰嘉新材料科技股份有限公司 | Method and device for monitoring welding quality of bimetal band saw blade |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107796818B (en) | 2018-05-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108107051B (en) | Lithium battery defect detection system and method based on machine vision | |
| CN107796818B (en) | The method of on-line checking bi-metal bandsaw blades welding quality | |
| CN107081503A (en) | The infrared nondestructive detection device and its Infrared Non-destructive Testing method of a kind of arc-welding quality | |
| CN104020222B (en) | Fully-automatic supersonic aluminum wire press welder weld quality prediction system | |
| CN104535587A (en) | PCBA solder joint inspection method based on machine vision | |
| CN105606620A (en) | PCBA welding spot detection method and system based on vision | |
| CN114544669A (en) | System and method for analyzing concentration of bad welding spots of solder paste detector | |
| CN115805397B (en) | Photovoltaic module battery piece welding detection system | |
| CN113641155A (en) | High-temperature forging detection control system | |
| CN104227216A (en) | On-line nondestructive testing method for resistance welding of solar cell | |
| CN111070694B (en) | Pipeline welding quality monitoring method and system based on big data | |
| CN108878194B (en) | A kind of keyboard automated assembly line | |
| CN118123316A (en) | Method and system for detecting cold joint by ultrasonic welding of battery connecting sheet | |
| CN111185660B (en) | Dynamic detection method for quality of friction stir welding seam based on laser ranging | |
| CN105424714A (en) | Multi-pin-based defect detection device and detection method thereof | |
| CN102950349A (en) | Welding process of plug-in unit of PCB (Printed Circuit Board) | |
| CN206764093U (en) | A kind of infrared nondestructive detection device of arc-welding quality | |
| CN206662523U (en) | A kind of spot welding on-line detecting system | |
| CN112059388A (en) | Method and device for monitoring welding quality of bimetal band saw blade | |
| CN114354491A (en) | DCB ceramic substrate defect detection method based on machine vision | |
| CN116460495B (en) | Welding processing method | |
| CN206677406U (en) | Integrated welding, which is agreed to play, takes the photograph structure and battery core automatic welding detection means | |
| CN104826814A (en) | Full-automatic welding on-line detection experimental equipment | |
| CN108956611B (en) | SLM processing process component surface energy distribution monitoring device and method | |
| CN104568978A (en) | Lamp holder optical head defect detection method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |