CN115415649A - GMAW molten droplet bath image identification method and equipment based on long-wave filtering - Google Patents
GMAW molten droplet bath image identification method and equipment based on long-wave filtering Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- 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
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- B—PERFORMING OPERATIONS; TRANSPORTING
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- 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
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Abstract
The invention discloses a GMAW molten drop molten pool image identification method and equipment based on long-wave filtering, and the equipment comprises a camera, an intense light source and a filtering device, wherein the filtering device consists of a splash-proof sheet, a neutral optical filter and a long-wave optical filter, the filtering device is sequentially and coaxially fixed in front of a camera lens through frame threads, the intense light source is a xenon lamp or a laser light source, and the camera adopts a near-infrared high-speed camera with the shooting frame number of 2000. Based on GMAW welded molten drop molten bath molten metal radiation characteristic and electric arc spectral distribution, adopt the mode that long wave light filtering combines the light filling, filter equipment filters arc light and adjusts the camera exposure, and the highlight source carries out the light filling to the process that the camera gathered molten drop molten bath image simultaneously, and accurate discernment GMAW welding process's molten drop molten bath is and is shot the image. The invention can effectively avoid arc light interference, obtain clear and high-quality welding droplet molten pool images and realize effective identification of various metal material GMAW welding droplet molten pools.
Description
Technical Field
The invention belongs to the field of automatic welding, and particularly relates to a GMAW molten drop weld pool image identification method and equipment based on long-wave filtering.
Background
Gas Metal Arc Welding (GMAW) is a welding method which adopts electric arc between a meltable welding wire and a workpiece to be welded which is continuously fed at a constant speed as a heat source to melt the welding wire and a base metal to form a molten pool and a welding seam, and in order to improve welding quality and ensure successful completion of welding, GMAW automatic welding needs real-time detection and control on a welding process. The molten drop transition form and the metal molten pool image based on visual sensing contain rich welding process information, information such as welding penetration, heat flow, welding seam surplus height and the like can be obtained through image acquisition and processing, and the information is fed back to welding process control to effectively improve the welding quality. Therefore, the visual sensing of the welding molten drop and the molten pool has the advantages of high sensitivity, good dynamic response, rich and visual provided information and the like, and is widely applied to the detection and control of molten drop transition and molten pool behavior of GMAW automatic welding.
The welding environment is complicated changeable, electric arc light interference, and the camera is to the metal molten pool that obtains the high definition and have more details, the collection of molten drop image proposes new challenge, for obtaining GMAW welding process high-definition molten drop molten pool image, the scientific research personnel focus on weakening arc light intensity, reinforcing metal molten drop, the research of self radiation intensity of molten pool, in order to avoid the interference of arc light to molten drop, molten pool molten metal radiation light in the molten drop molten pool image acquisition in-process, obtain clear molten drop form and molten pool action image.
The existing method for identifying the welding molten drop molten pool mainly filters the visible light wave band, for example, the paper ' high-speed shooting and electric signal test analysis of arc welding molten drop transition ' (Doi: 1000-565X (2008) 04-0001-05) ', a narrow-band filter is used for filtering the arc light of the welding arc to acquire a molten drop image, the central wavelength of the narrow-band filter is 632.8nm, the half-band width is 10nm, and the peak transmittance is 56%.
In summary, the visual identification of the welding droplet molten pool is an important link for controlling the welding process, and is a necessary premise for realizing the welding automation. The existing method for filtering light in a narrow band of a visible light waveband is difficult to obtain an ideal welding droplet molten pool image, so that a more appropriate filtering waveband needs to be found, and other auxiliary methods such as supplementary lighting and the like are adopted to further enhance the shooting effect.
Disclosure of Invention
In order to solve the problem that a clear droplet molten pool image is difficult to obtain in the GMAW welding process in the prior art, the invention mainly aims to provide GMAW droplet molten pool image identification equipment based on long-wave filtering, and the GMAW droplet molten pool image identification equipment is simple in structural design and convenient to operate.
The invention also aims to provide a GMAW molten drop molten pool image identification method based on long-wave filtering, which adopts near-infrared long-wave filtering combined with supplementary lighting to obtain clear and high-quality welding molten drop molten pool images and provides a basis for measurement and control of a welding process.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides GMAW molten droplet molten pool image recognition equipment based on long-wave filtering, which comprises a camera, a laser and a filtering device, wherein the filtering device consists of an anti-splashing sheet, a neutral optical filter and a long-wave optical filter, the anti-splashing sheet, the neutral optical filter and the long-wave optical filter are the same in size with a lens of the camera, and are sequentially and coaxially fixed in front of a lens of the camera through frame threads, and the long-wave optical filter is adjacent to the lens.
Preferably, the splash-proof sheet is made of transparent quartz glass material and is fixed at the forefront end of the optical filter device to protect the camera lens.
Preferably, the neutral filter plate is uniformly subjected to light attenuation, is arranged in parallel with the anti-splashing plate and the long-wave filter plate, and the transmittance is freely adjusted within 1-100%.
Preferably, the filtering wave band of the long wave filter is cut off at a wave band below 850nm, the filtering wave band passes through at least 850nm, the cut-off depth is above OD4, and a narrow band or high pass filter of vacuum coating with a wavelength above 850nm is selected.
Preferably, the camera has a strong light sensing capability in a near infrared band, particularly in a long wavelength band of 850nm or more, and is directed to a droplet, a molten pool, and an arc during photographing.
More preferably, the long-wave filter is a near-infrared narrow-band filter, the center wavelength of the narrow-band filter is 960nm, the half-band width of the narrow-band filter is 10nm, the peak transmittance of the narrow-band filter is 70%, or a high-pass filter with the cutoff wavelength of 850nm is selected.
Preferably, the laser is a xenon lamp of 1000W or more or a laser light source of 50W or more, and has strong radiation in the passing range of the long-wave filter.
Preferably, the camera is a near-infrared high-speed camera with the shooting frame number of 2000, and has strong light sensitivity in a near-infrared band, particularly in a long-wave band above 850 nm.
The invention also provides a GMAW molten droplet bath image identification method based on long-wave filtering, which is realized by the GMAW molten droplet bath image identification device based on long-wave filtering and comprises the following steps: based on GMAW welded molten drop molten pool molten metal radiation characteristic and electric arc spectral distribution, adopt long wave filtering to combine the mode of light filling to filter arc light in, select the highlight to carry out the light filling to camera collection molten drop molten pool image process, the molten drop and the molten pool of clear discernment GMAW welding process.
Preferably, during GMAW welding, the strong light source is arranged beside the camera to irradiate light onto the molten drop bath to supplement light for illumination of the molten drop bath, the long-wave filter arranged in front of the camera lens filters arc light, the neutral filter adjusts the exposure of the camera, and images of the molten drop bath in the GMAW welding process are shot.
Preferably, the arc spectral distribution satisfies: the radiation is strong between 250nm and 850nm, and the near infrared radiation above 850nm is weak; the molten metal radiation of the molten drop molten pool meets the following requirements: the intensity is increased along with the increase of the wavelength within 400nm to 1100nm, and a long-wave filter can be used for filtering short-wave radiation and retaining long-wave radiation, so that an electric arc appears dark and a molten drop molten pool appears bright.
Preferably, the strong light source is a xenon lamp or a laser light source, the strong radiation wave band of the strong light source is in a long wave filtering wave band, the intensity of the light source is greater than 20000 lumens, and the strong light source is aimed at a molten drop, a molten pool and an electric arc for light supplement during shooting, so that the shooting effect is improved.
Compared with the prior art, the invention has the following beneficial effects:
1) The mode of combining near-infrared long-wave filtering with light supplement is adopted to avoid arc light interference, clear and high-quality welding molten drop molten pool images are obtained, computer image processing is optimized, welding quality is improved, and a basis is provided for measurement and control of a welding process.
2) The invention adopts the light filtering device to fully filter arc light, retains the light of a liquid molten pool and a molten drop, adopts strong light sources such as laser or xenon lamp to supplement light to a shot object, and the camera shoots clear images of the molten drop and the molten pool under the coordination of the light filtering device and the strong light source, has good light filtering effect, can realize effective identification of various metal material GMAW welding molten drop molten pools, is suitable for the field of GMAW automatic welding, and has wide application prospect.
Drawings
FIG. 1 is a schematic diagram of an image identification device for GMAW droplet molten bath based on long wave filtering in the embodiment.
Fig. 2 is a schematic view of a connection structure of the filter device and the camera of fig. 1.
Fig. 3 is a GMAW arc spectral distribution diagram.
FIG. 4 is a graph of blackbody spectral power as a function of wavelength and temperature.
Fig. 5 is a droplet puddle image taken of GMAW welded Q345 steel.
FIG. 6 is a molten droplet bath image taken of GMAW welded 4043 aluminum alloy sheet.
The reference numbers are as follows: 1, a computer; 2, welding the robot; 3, a protective gas steel cylinder; 4GMAW welder; 5, welding a welding gun; 6 a light filtering device; 7, a lens; 8 cameras; 9 a laser; 10 welding parts; 61 long-wave filter; 62 a neutral color filter; 63 splash-proof sheet.
Detailed Description
The invention will be explained below with reference to the drawings and examples.
Fig. 1 and 2 exemplarily illustrate a GMAW molten droplet bath image recognition device based on long-wave filtering, which includes a camera 8, a laser 9 and a filtering device 6, wherein the filtering device 6 is composed of an anti-spatter 63, a neutral filter 62 and a long-wave filter 61, the anti-spatter 63, the neutral filter 62 and the long-wave filter 61 are all the same size as the lens of the camera 8, and are coaxially fixed in front of the lens 7 of the camera 8 in sequence through frame threads, and the long-wave filter 61 is adjacent to the lens 7.
In some embodiments, the splash-proof sheet is made of a transparent quartz glass material and is fixed at the foremost end of the optical filtering device to protect the lens of the camera.
In some embodiments, the neutral filter plate is uniformly dimmed, is placed in parallel with the anti-splash plate and the long-wave filter plate, and has a transmittance which is freely adjusted within 1% -100%.
In some embodiments, the long-wave filter has a filtering band cut off below 850nm and passing above 850nm, and the cut-off depth is above OD4, and can be a vacuum-coated narrow-band or high-pass filter with a wavelength above 850 nm.
In some embodiments, the camera has strong light sensitivity in the near infrared band, especially in the long wavelength band above 850nm, and the camera is aimed at the molten drop, molten pool and arc during shooting.
In some embodiments, the long-wave filter is a near-infrared narrow-band filter, the center wavelength of the narrow-band filter is 960nm, the half-band width of the narrow-band filter is 10nm, the peak transmittance of the narrow-band filter is 70%, or a high-pass filter with the cutoff wavelength of 850nm is selected.
In some embodiments, the laser uses a xenon lamp of 1000W or more or a laser light source of 50W or more, and has strong radiation in the passing range of the long-wave filter.
In some embodiments, the camera is a near-infrared high-speed camera with 2000 shooting frames, and has strong light sensitivity in a near-infrared band, particularly a long-wave band above 850 nm.
The GMAW molten drop molten pool image identification method based on the GMAW molten drop molten pool image identification device based on the long-wave filtering comprises the following steps:
based on the radiation characteristic of molten metal and the spectral distribution of arc light in a molten drop molten pool for GMAW welding, a strong light source is selected to supplement light for a camera in the process of collecting images of the molten drop molten pool while filtering the arc light in a mode of combining long-wave filtering with light supplement, so that molten drops and the molten pool in the GMAW welding process are clearly identified; namely: during GMAW welding, place the strong light source beside the camera and beat light on the molten drop molten bath for its illumination light filling, place long wave filter filtering arc light and neutral filter before the camera lens in and adjust the exposure of camera, shoot the molten drop molten bath image of GMAW welding process, wherein arc spectral distribution satisfies: the radiation is strong between 250nm and 850nm, and the near infrared radiation above 850nm is weak; the molten metal radiation of the molten drop molten pool meets the following requirements: the intensity is increased along with the increase of the wavelength within 400nm to 1100nm, and a long-wave filter can be used for filtering short-wave radiation and retaining long-wave radiation, so that an electric arc appears dark and a molten drop molten pool appears bright; the strong light source is a xenon lamp or a laser light source, the strong radiation wave band of the strong light source is in the long wave filtering wave band, the intensity of the light source is more than 20000 lumens, and the strong light source is aimed at a molten drop, a molten pool and an electric arc for light supplement during shooting, so that the shooting effect is improved.
Example 1
The embodiment provides GMAW molten drop molten pool image recognition equipment based on long wave filtering combines GMAW welding set, including laser instrument 9, camera 8, camera lens 7, filter equipment 6 and robot 2, as auxiliary light source, laser instrument 9 shoots the molten drop molten pool image to camera 8 and carries out the light filling, during GMAW welding, in filter equipment 6, arc light and molten drop molten pool radiation light get into neutral filter 62 through preventing splashing piece 63, filter the arc light through long wave filter 61 after the light intensity is adjusted to the same degree, the long wave section molten drop, molten pool radiation spectrum gets into camera 8 formation of image, the image transmission of collection is to computer 1, adjust and control welding robot 2 operation to welding parameter after computer image analysis processes.
According to the requirements of a welding process, two Q345 low-carbon steel flat plates are subjected to butt welding by using GMAW welding, the two flat plates are identical in size, 6mm in plate thickness, 500mm in plate length and 100mm in plate width, 1.2mm in welding wire diameter and made of ER50 steel welding wires, welding shielding gas is pure argon gas with the purity of 99.99%, the gas flow is 20L/min, the welding current is 300A, and the welding speed is 280mm/min.
Referring to fig. 2 to 5, the filter device 6 is composed of a splash-proof sheet 63, a neutral filter 62, and a long-wavelength filter 61, and is connected by a frame screw. The anti-splashing sheet 63, the neutral filter 62 and the long-wave filter 61 are the same as the lens of the camera 8 in size and are sequentially and coaxially arranged on the lens 7 of the camera 8 through frame threads. During selection, based on GMAW arc spectral distribution (figure 3) and the variation relation of blackbody spectral radiation force along with wavelength and temperature (figure 4), a near-infrared narrow-band filter is selected for filtering, the central wavelength of the narrow-band filter is 960nm, the half-bandwidth is 10nm, the peak transmittance is 70%, strong light is not used for supplementing light, and a near-infrared high-speed camera with the shooting frame number of 2000 is selected as the camera 8.
During GMAW welding, arc, molten drop and radiation light of a molten pool sequentially pass through a splash-proof sheet 63, a neutral filter 62 and a long-wave filter 61 (narrow-band filter) to enter a camera 8, a collected high-definition molten drop molten pool image is stored on a computer, and the collected molten pool molten drop image is shown in FIG. 5.
Example 2
The present embodiment provides a GMAW droplet puddle image recognition device based on long-wave filtering in combination with a GMAW welding apparatus, including a laser 9, a camera 8, a lens 7, a filtering device 6, and a welding robot 2. As an auxiliary light source, a laser 9 is used for supplementing light to a molten drop molten pool image shot by a camera 8, when GMAW welding is carried out, arc light and molten drop molten pool radiation light in a light filtering device 6 enter a neutral filter 62 through a splash-proof sheet 63, the arc light is filtered through a long-wave filter 61 after the light intensity is adjusted to the same degree, long-wave-band molten drop and molten pool radiation spectrum enter the camera 8 for imaging, collected images are transmitted to a computer 1, welding parameters are adjusted and the operation of a welding robot 2 is controlled after the collected images are analyzed and processed through the computer.
According to the requirements of a welding process, pulse GMAW welding is used for butt welding two 4043 aluminum alloy flat plates, the two flat plates are identical in size, the plate thickness is 4mm, the plate length is 500mm, the plate width is 100mm, the welding current is 150A, the material is 5356 aluminum alloy welding wires, welding shielding gas is high-purity argon gas with the purity of 99.999%, the gas flow is 22L/min, and the welding speed is 480mm/min.
With reference to fig. 2 to 6, the filter device 6 is composed of an anti-splash sheet 63, a neutral filter 62, and a long-wave filter 61, the anti-splash sheet 63, the neutral filter 62, and the long-wave filter 61 are the same as the lens of the camera 8 in size, and are coaxially mounted on the lens 7 of the camera 8 in sequence through a frame screw, a high-pass filter with a cutoff wavelength of 850nm is selected as the long-wave filter, a 50W or 900nm infrared solid laser is selected as a strong light source for light supplement, and the camera 8 selects a near-infrared high-speed camera with a shooting frame number of 2000.
During GMAW welding, an infrared solid laser 9 irradiates laser on a molten drop molten pool beside a camera 8 to illuminate the molten drop molten pool, meanwhile, arc light and light of the molten drop molten pool pass through a splash-proof sheet 63, a neutral filter 62 and a long-wave filter 61 (high-pass filter), and enter a near-infrared high-speed camera to be imaged, the acquired high-definition molten drop molten pool image is stored on a computer, and the acquired molten pool molten drop image is shown in FIG. 6.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. GMAW molten droplet bath image identification equipment based on long-wave filtering is characterized by comprising a camera, an intense light source and a filtering device, wherein:
the light filtering device consists of an anti-splash sheet, a neutral light filter and a long wave light filter, the anti-splash sheet, the neutral light filter and the long wave light filter are the same as the lens of the camera in size, and are sequentially and coaxially fixed in front of the lens of the camera through frame threads, and the long wave light filter is adjacent to the lens;
the strong light source is a xenon lamp or a laser light source, the strong radiation wave band of the strong light source is in the long wave filtering wave band, and the intensity of the light source is more than 20000 lumens;
the camera is a near-infrared high-speed camera with 2000 shooting frames.
2. The GMAW molten droplet bath image recognition device based on long-wave filtering as claimed in claim 1, wherein the strong light source is a xenon lamp with the power of 1000W or more or a laser light source with the power of 50W or more.
3. The GMAW droplet bath image identification device based on long-wave filtering as claimed in claim 1, wherein the splash guard is made of transparent quartz glass material and is fixed at the foremost end of the filtering device.
4. A GMAW droplet puddle image identification device based on long-wave filtering as claimed in claim 1, wherein said neutral filter is uniformly dimmed, placed in parallel with the anti-spatter plate and the long-wave filter.
5. The apparatus of claim 1, wherein the long-wave filter has a cut-off wavelength below 850nm and a cut-off depth above OD 4.
6. The GMAW molten droplet bath image identification device based on long-wave filtering as claimed in claim 5, wherein the long-wave filter is a near-infrared narrow-band filter with a central wavelength of 960nm, a half-bandwidth of 10nm and a peak transmittance of 70%.
7. The GMAW droplet puddle image identification device based on long-wave filtering of claim 5, wherein the long-wave filter is a high-pass filter with a cutoff wavelength of 850 nm.
8. The GMAW molten droplet bath image identification method based on the long wave filtering is realized by combining GMAW molten droplet bath image identification equipment based on the long wave filtering according to any one of claims 1 to 7 with a GMAW welding device, and comprises the following steps: based on GMAW welding's molten metal radiation characteristic of molten drop molten bath and arc spectral distribution, adopt the mode of long wave light filtering combination light filling, promptly: during GMAW welding, the light filtering device is arranged in front of a camera lens to filter arc light and adjust the exposure of the camera, and the strong light source is arranged beside the camera to supplement light for the process of collecting images of a molten drop molten pool by the camera, so that the molten drop molten pool in the GMAW welding process is accurately identified and images are shot.
9. The GMAW droplet puddle image identification method based on long-wave filtering as claimed in claim 8, wherein said arc spectral distribution satisfies: the radiation is strong between 250nm and 850nm, and the near infrared radiation above 850nm is weak; the molten metal radiation of the molten drop molten pool meets the following requirements: increasing with increasing wavelength in the 400nm to 1100nm range.
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Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1448239A (en) * | 2003-04-03 | 2003-10-15 | 上海交通大学 | Arc-welding furnace hearth dynamic characteristic vision sensing method |
| CN101062529A (en) * | 2006-04-25 | 2007-10-31 | 南京理工大学 | Weld seam appearance, fused bath and seam near-infrared vision integral sensing checkout gear |
| US20120229632A1 (en) * | 2009-09-11 | 2012-09-13 | Fronius International Gmbh | Monitoring module for monitoring a process with an electric arc |
| CN102706320A (en) * | 2012-03-20 | 2012-10-03 | 浙江大学 | Method for reducing background light radiation in laser ranging system |
| CN102974918A (en) * | 2012-11-26 | 2013-03-20 | 清华大学 | Multi-spectral spectroscopic photography-based visual monitoring system |
| US20140326705A1 (en) * | 2011-12-15 | 2014-11-06 | Jfe Steel Corporation | Welding position detecting apparatus and welding position detecting method for laser beam welding |
| CN104954739A (en) * | 2015-05-15 | 2015-09-30 | 北京石油化工学院 | GMAW (Gas metal arc welding) globular transfer image shooting system used in underwater dry type high-pressure environment |
| CN107199388A (en) * | 2016-03-17 | 2017-09-26 | 海宁瑞奥金属科技有限公司 | A kind of submerged-arc welding droplet transfer image taking and electrical signal collection system |
| US20180015571A1 (en) * | 2014-12-30 | 2018-01-18 | Jiangsu University Of Science And Technology | Infrared vision sensing detection method and device for narrow-gap weld seam deviation |
| CN107962278A (en) * | 2016-10-19 | 2018-04-27 | 南京理工大学 | A kind of arc welding weld crater image harvester based on saturating infrared fileter |
| CN109444134A (en) * | 2018-07-10 | 2019-03-08 | 南京理工大学 | Molten bath vision optimal imaging band selection method based on spectrum analysis |
| CN109447939A (en) * | 2018-07-10 | 2019-03-08 | 南京理工大学 | Weld width prediction method before molten bath based on mixed spectra vision shapes |
| CN109530955A (en) * | 2018-12-03 | 2019-03-29 | 江苏科技大学 | The welding technological properties evaluating apparatus and method of gas shield welding wire |
| CN110788444A (en) * | 2019-11-28 | 2020-02-14 | 上海工程技术大学 | Dynamic detection device and method for arc additive manufacturing molten pool |
| CN111429423A (en) * | 2020-03-19 | 2020-07-17 | 南通大学 | Multi-spectral welding image identification method based on quaternion multi-degree-of-freedom neurons |
| CN214079689U (en) * | 2020-12-18 | 2021-08-31 | 北京博清科技有限公司 | Observation system of welding pool |
| CN114160974A (en) * | 2021-12-20 | 2022-03-11 | 清华大学 | Image acquisition device and parameter determination method |
| CN114283272A (en) * | 2021-12-15 | 2022-04-05 | 上海电力大学 | Molten pool image acquisition method based on spatial filtering technology |
| CN216852080U (en) * | 2021-12-15 | 2022-06-28 | 上海电力大学 | Molten pool image acquisition system based on spatial filtering technology |
-
2022
- 2022-09-06 CN CN202211086051.1A patent/CN115415649A/en active Pending
Patent Citations (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1448239A (en) * | 2003-04-03 | 2003-10-15 | 上海交通大学 | Arc-welding furnace hearth dynamic characteristic vision sensing method |
| CN101062529A (en) * | 2006-04-25 | 2007-10-31 | 南京理工大学 | Weld seam appearance, fused bath and seam near-infrared vision integral sensing checkout gear |
| US20120229632A1 (en) * | 2009-09-11 | 2012-09-13 | Fronius International Gmbh | Monitoring module for monitoring a process with an electric arc |
| US20140326705A1 (en) * | 2011-12-15 | 2014-11-06 | Jfe Steel Corporation | Welding position detecting apparatus and welding position detecting method for laser beam welding |
| CN102706320A (en) * | 2012-03-20 | 2012-10-03 | 浙江大学 | Method for reducing background light radiation in laser ranging system |
| CN102974918A (en) * | 2012-11-26 | 2013-03-20 | 清华大学 | Multi-spectral spectroscopic photography-based visual monitoring system |
| US20180015571A1 (en) * | 2014-12-30 | 2018-01-18 | Jiangsu University Of Science And Technology | Infrared vision sensing detection method and device for narrow-gap weld seam deviation |
| CN104954739A (en) * | 2015-05-15 | 2015-09-30 | 北京石油化工学院 | GMAW (Gas metal arc welding) globular transfer image shooting system used in underwater dry type high-pressure environment |
| CN107199388A (en) * | 2016-03-17 | 2017-09-26 | 海宁瑞奥金属科技有限公司 | A kind of submerged-arc welding droplet transfer image taking and electrical signal collection system |
| CN107962278A (en) * | 2016-10-19 | 2018-04-27 | 南京理工大学 | A kind of arc welding weld crater image harvester based on saturating infrared fileter |
| CN109444134A (en) * | 2018-07-10 | 2019-03-08 | 南京理工大学 | Molten bath vision optimal imaging band selection method based on spectrum analysis |
| CN109447939A (en) * | 2018-07-10 | 2019-03-08 | 南京理工大学 | Weld width prediction method before molten bath based on mixed spectra vision shapes |
| CN109530955A (en) * | 2018-12-03 | 2019-03-29 | 江苏科技大学 | The welding technological properties evaluating apparatus and method of gas shield welding wire |
| CN110788444A (en) * | 2019-11-28 | 2020-02-14 | 上海工程技术大学 | Dynamic detection device and method for arc additive manufacturing molten pool |
| CN111429423A (en) * | 2020-03-19 | 2020-07-17 | 南通大学 | Multi-spectral welding image identification method based on quaternion multi-degree-of-freedom neurons |
| CN214079689U (en) * | 2020-12-18 | 2021-08-31 | 北京博清科技有限公司 | Observation system of welding pool |
| CN114283272A (en) * | 2021-12-15 | 2022-04-05 | 上海电力大学 | Molten pool image acquisition method based on spatial filtering technology |
| CN216852080U (en) * | 2021-12-15 | 2022-06-28 | 上海电力大学 | Molten pool image acquisition system based on spatial filtering technology |
| CN114160974A (en) * | 2021-12-20 | 2022-03-11 | 清华大学 | Image acquisition device and parameter determination method |
Non-Patent Citations (1)
| Title |
|---|
| 刘立君;于义涛;金海平;张伟杰;吴兆寿;王晓鹏;: "基于ROI区域特征模具激光修复熔池填丝熔入状态识别技术", 焊接学报 * |
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