CN103134809A - Welded line defect detection method - Google Patents
Welded line defect detection method Download PDFInfo
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- CN103134809A CN103134809A CN2013100808714A CN201310080871A CN103134809A CN 103134809 A CN103134809 A CN 103134809A CN 2013100808714 A CN2013100808714 A CN 2013100808714A CN 201310080871 A CN201310080871 A CN 201310080871A CN 103134809 A CN103134809 A CN 103134809A
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- 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
- G01N21/88—Investigating the presence of flaws or contamination
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Abstract
The invention relates to a welded line defect detection method which comprises the following steps of: judging a pixel proportion and judging an image shape, wherein the pixel proportion judgment comprises the following steps: determining the position of a welded line, acquiring an image of the welded line, locking a rectangular frame at the position of the welded line on the image, calculating the gray scale value of each pixel point on the image in the rectangular frame, dividing the gray scale values into low gray scale values from 0 to 128 and high gray scale values from 128 to 255, when the proportion of the low gray scale values of the pixel points of the image is more than 85 percent, determining that the welded line has defectes; and when the proportion is less than 85 percent, judging a shape of the image; and the image shape judgment comprises the following steps: performing binary conversion on the acquired image of the welded line, performing edge extraction on the converted image, extracting the shape of the welded line in the image, judging the shape of the welded line through an eight-communicated graph algorithm, and if the shape of the welded line is irregular, determining that the welded line is defected. By the welded line defect detection method, the detection work finished by a worker can be finished by a computer, so that the labor cost is reduced; and the stability and the high efficiency are realized.
Description
Technical field
the present invention relates to a kind of weld defect detection method.
Background technology
the ladle body of metallic packing pail is generally by the welding of metal lamellar body both sides, carries out other operations after forming a columniform tank body again, in welding position, stays weld seam.Because the quality of weld seam directly can have influence on the quality of pail pack, therefore when completing welding, often need butt welded seam to be detected, judge whether weld seam exists defect.Detecting weld seam in industry is generally manual detection, but the detection efficiency of manual detection is lower, can't detect the weld seam slight part, and there is deviation in the mark of judgement, impact judgement stable.
Summary of the invention
the object of the invention is to provide a kind of weld defect detection method.
for achieving the above object, the technical solution used in the present invention is:
a kind of weld defect detection method, comprise pixel ratio judgement, picture shape judgement, wherein:
the pixel ratio judgement comprises: determine position while welding, gather weld image, a rectangle frame that approaches weld shape of position while welding locking at image, calculate the gray-scale value on each pixel of image in the rectangle frame, gray-scale value is divided into to low value gray scale, high value gray scale, and wherein: gray-scale value is the low value gray scale in the 0-128 scope, and gray-scale value is high value gray scale in the 128-255 scope, when the ratio of each pixel low value gray scale of image surpasses 85%, there is defect in weld seam; Lower than 85% the time, carry out the picture shape judgement when the ratio of each pixel low value gray scale of image;
the picture shape judgement comprises: the weld image gathered is carried out to the binaryzation conversion, the image after conversion is carried out to edge extracting, extract the shape of weld seam in image, by eight connected graph algorithms, the judgement weld seam shape, when weld seam be shaped as irregular figure the time, there is defect in weld seam.
preferably, determine position while welding, after the collection weld image, the position while welding of judgement image is positioned at product two ends or centre, when image is positioned in the middle of product, carries out the pixel ratio judgement.
preferably, during the pixel ratio judgement, by two sweep traces, the weld seam both sides of image is scanned, thus the position of definite weld seam.
because technique scheme is used, the present invention compared with prior art has following advantages and effect:
the present invention, by comprehensively judge the defect of weld seam from pixel ratio judgement, two parts of picture shape judgement, carries out image acquisition, defect judgement to product in real time; Testing by originally need to manually completing, be transformed into by computing machine and complete, and reduces cost of labor; Simultaneously, machine detects the manual detection of comparing, and has more stability and high efficiency, and this technological improvement, can effectively improve enterprises production efficiency, improves enterprise competitiveness.
Embodiment
below in conjunction with case study on implementation, the invention will be further described:
enter the weld defect checkout equipment product line is added, checkout equipment comprises at least one industrial camera, capture card, computing machine etc.; By the computer control industrial camera, Real-time Collection weld image data; Weld image is processed Primary Stage Data by capture card, reaches computing machine, and computing machine receives view data it is judged;
judgement comprises: pixel ratio judgement, picture shape judgement,
wherein: at first need to judge whether image takes from two ends or the centre of product, and judge that image is positioned at the welding in-position and still welds away position, record its position mark; When image is positioned in the middle of product, carry out the pixel ratio judgement; When image is positioned at the product two ends, need to carry out special judgement to two ends, at this, be not described;
the pixel ratio judgement comprises: by two sweep traces, the weld seam both sides of image is scanned, thereby determine the position of weld seam, a rectangle frame that approaches weld shape of position while welding locking at image, calculate the gray-scale value on each pixel of image in the rectangle frame, gray-scale value is divided into to low value gray scale, high value gray scale, wherein: gray-scale value is the low value gray scale in the 0-128 scope, gray-scale value is high value gray scale in the 128-255 scope, when the ratio of each pixel low value gray scale of image surpasses 85%, there is defect in weld seam, product is defective, provides alerting signal; Lower than 85% the time, carry out the picture shape judgement when the ratio of each pixel low value gray scale of image.Because the shape of normal weld is close to a rectangle frame, therefore, in the rectangle frame of locking, low value gray scale (being dark colour) ratio is higher, and at the poor weld place, because shape not too is close to a rectangle frame, so in the rectangle frame of locking, must comprise a part of high value gray scale (light colour), so the ratio of low value gray scale just do not have the height of normal weld image.
the picture shape judgement comprises: the weld image gathered is carried out to the binaryzation conversion, image after conversion is carried out to edge extracting, extract the shape of weld seam in image, by eight connected graph algorithms (in bianry image, the set of the black pixel interconnected becomes (a deceiving) zone, by marking operation is carried out in each zone in image, try to achieve regional number, eight connected graph algorithms from left to right, from top to bottom, detect successively each pixel, if find that certain pixel pixel value is 0, detect successively the upper right of this point, just go up, upper left and left front point be the pixel value of totally four points, judge their connectedness), the shape of judgement weld seam, when weld seam be shaped as regular figure the time, there is not defect in weld seam, product is qualified, when weld seam be shaped as irregular curved figure the time, need further to judge that this image is positioned at the position of product, in the image rule of center section, it is the weld image of specification product, otherwise be substandard product.
after the mechanical arm controller receives alerting signal, control mechanical arm and take out the defectiveness product, prevent that it from entering next processing link.
in addition, for the problem of setting up of checkout equipment, because this cover system adopts the method for machine vision to realize the detection of product defects, so the picture quality of obtaining is had relatively high expectations, and setting up of camera light source is most important, need accurately to guarantee that product, light source, camera are coaxial.
when gathering weld image, need to carry out overlapping taking pictures (average length that lap length is defect) by camera, the image of so obtaining, can guarantee that defect part is positioned at the centre of obtained image all the time.
above-described embodiment is only explanation technical conceive of the present invention and characteristics, and its purpose is to allow the person skilled in the art can understand content of the present invention and implement according to this, can not limit the scope of the invention with this.All equivalences that Spirit Essence is done according to the present invention change or modify, within all should being encompassed in protection scope of the present invention.
Claims (3)
1. a weld defect detection method is characterized in that: comprise pixel ratio judgement, picture shape judgement, wherein:
The pixel ratio judgement comprises: determine position while welding, gather weld image, a rectangle frame that approaches weld shape of position while welding locking at image, calculate the gray-scale value on each pixel of image in the rectangle frame, gray-scale value is divided into to low value gray scale, high value gray scale, and wherein: gray-scale value is the low value gray scale in the 0-128 scope, and gray-scale value is high value gray scale in the 128-255 scope, when the ratio of each pixel low value gray scale of image surpasses 85%, there is defect in weld seam; Lower than 85% the time, carry out the picture shape judgement when the ratio of each pixel low value gray scale of image;
The picture shape judgement comprises: the weld image gathered is carried out to the binaryzation conversion, the image after conversion is carried out to edge extracting, extract the shape of weld seam in image, by eight connected graph algorithms, the judgement weld seam shape, when weld seam be shaped as irregular figure the time, there is defect in weld seam.
2. weld defect detection method according to claim 1, is characterized in that: determine position while welding, judge after the collection weld image that the position while welding of image is positioned at product two ends or centre, when image is positioned in the middle of product, carries out the pixel ratio judgement.
3. weld defect detection method according to claim 1 is characterized in that: during the pixel ratio judgement, by two sweep traces, the weld seam both sides of image is scanned, thereby determined the position of weld seam.
Priority Applications (3)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310080871.4A CN103134809B (en) | 2013-03-14 | 2013-03-14 | Welded line defect detection method |
| NZ711997A NZ711997A (en) | 2013-03-14 | 2013-09-13 | Weld seam defect detection method |
| PCT/CN2013/083509 WO2014139273A1 (en) | 2013-03-14 | 2013-09-13 | Weld seam defect detection method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201310080871.4A CN103134809B (en) | 2013-03-14 | 2013-03-14 | Welded line defect detection method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN103134809A true CN103134809A (en) | 2013-06-05 |
| CN103134809B CN103134809B (en) | 2015-04-29 |
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Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
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| CN201310080871.4A Active CN103134809B (en) | 2013-03-14 | 2013-03-14 | Welded line defect detection method |
Country Status (3)
| Country | Link |
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| CN (1) | CN103134809B (en) |
| NZ (1) | NZ711997A (en) |
| WO (1) | WO2014139273A1 (en) |
Cited By (9)
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| WO2014139273A1 (en) * | 2013-03-14 | 2014-09-18 | 苏州华源包装股份有限公司 | Weld seam defect detection method |
| CN104215638A (en) * | 2013-06-03 | 2014-12-17 | 英业达科技有限公司 | Pin bending detection method |
| CN108122228A (en) * | 2017-12-21 | 2018-06-05 | 金翰阳科技（大连）股份有限公司 | A kind of polishing putty or lacquer painting detection method |
| CN108665452A (en) * | 2018-05-09 | 2018-10-16 | 广东大鹏液化天然气有限公司 | A kind of pipeline-weld film scanning storage and identification of Weld Defects and its system based on big data |
| CN109001224A (en) * | 2017-06-07 | 2018-12-14 | 宁德时代新能源科技股份有限公司 | Welding seam detection method and detection device |
| CN110751619A (en) * | 2019-08-28 | 2020-02-04 | 中国南方电网有限责任公司超高压输电公司广州局 | Insulator defect detection method |
| CN112102255A (en) * | 2020-08-21 | 2020-12-18 | 杭州培慕科技有限公司 | Intelligent defect rating method based on X-ray imaging image in industrial scene |
| CN114581742A (en) * | 2021-10-26 | 2022-06-03 | 中科慧远视觉技术(北京)有限公司 | Linearity-based connected domain clustering fusion method, device, system and medium |
| CN116664554A (en) * | 2023-07-26 | 2023-08-29 | 微山晟轩机械制造有限公司 | Bolt thread defect detection method based on image processing |
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| CN106290394B (en) * | 2016-09-30 | 2023-04-07 | 华南理工大学 | System and method for detecting aluminum extrusion molding defects of CPU radiator |
| CN109741311B (en) * | 2018-12-28 | 2022-09-02 | 西南交通大学 | Aluminum alloy fusion welding back face fusion width detection method with false edge |
| CN115210035B (en) * | 2020-03-05 | 2024-06-25 | 松下知识产权经营株式会社 | Bead appearance inspection device, bead appearance inspection method, program, and bead appearance inspection system |
| CN112730428A (en) * | 2020-12-23 | 2021-04-30 | 联合汽车电子有限公司 | Extension rod weld joint detection system and method |
| CN113160132A (en) * | 2021-03-10 | 2021-07-23 | 上海应用技术大学 | Detection processing method and system for weld defect image |
| CN113610814B (en) * | 2021-08-10 | 2022-11-18 | 广东利元亨智能装备股份有限公司 | Weld quality detection method and device, electronic equipment and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| WO2014139273A1 (en) * | 2013-03-14 | 2014-09-18 | 苏州华源包装股份有限公司 | Weld seam defect detection method |
| CN104215638A (en) * | 2013-06-03 | 2014-12-17 | 英业达科技有限公司 | Pin bending detection method |
| CN109001224B (en) * | 2017-06-07 | 2022-02-18 | 宁德时代新能源科技股份有限公司 | Welding seam detection method and detection device |
| CN109001224A (en) * | 2017-06-07 | 2018-12-14 | 宁德时代新能源科技股份有限公司 | Welding seam detection method and detection device |
| CN108122228A (en) * | 2017-12-21 | 2018-06-05 | 金翰阳科技（大连）股份有限公司 | A kind of polishing putty or lacquer painting detection method |
| CN108665452A (en) * | 2018-05-09 | 2018-10-16 | 广东大鹏液化天然气有限公司 | A kind of pipeline-weld film scanning storage and identification of Weld Defects and its system based on big data |
| CN108665452B (en) * | 2018-05-09 | 2019-06-07 | 广东大鹏液化天然气有限公司 | Pipeline-weld film scanning storage and identification of Weld Defects and its system |
| CN110751619A (en) * | 2019-08-28 | 2020-02-04 | 中国南方电网有限责任公司超高压输电公司广州局 | Insulator defect detection method |
| CN112102255A (en) * | 2020-08-21 | 2020-12-18 | 杭州培慕科技有限公司 | Intelligent defect rating method based on X-ray imaging image in industrial scene |
| CN112102255B (en) * | 2020-08-21 | 2024-01-23 | 杭州培慕科技有限公司 | Intelligent defect grading method based on X-ray imaging image in industrial scene |
| CN114581742A (en) * | 2021-10-26 | 2022-06-03 | 中科慧远视觉技术(北京)有限公司 | Linearity-based connected domain clustering fusion method, device, system and medium |
| CN114581742B (en) * | 2021-10-26 | 2023-01-24 | 中科慧远视觉技术(北京)有限公司 | Linearity-based connected domain clustering fusion method, device, system and medium |
| CN116664554A (en) * | 2023-07-26 | 2023-08-29 | 微山晟轩机械制造有限公司 | Bolt thread defect detection method based on image processing |
| CN116664554B (en) * | 2023-07-26 | 2023-10-20 | 微山晟轩机械制造有限公司 | Bolt thread defect detection method based on image processing |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2014139273A1 (en) | 2014-09-18 |
| CN103134809B (en) | 2015-04-29 |
| NZ711997A (en) | 2018-10-26 |
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Address after: 215236 Suzhou Province, Wujiang City, Taoyuan Town Peach Road, No. 1948, No. Patentee after: Suzhou Huayuan Cmi Holdings Ltd Address before: 215236 Suzhou Province, Wujiang City, Taoyuan Town Peach Road, No. 1948, No. Patentee before: Wujiang Huayuan Tinplate Printing & Tin Making Co Ltd |