CN102495073A - Automatic detecting method of machine vision system for detecting detained needle bushing burrs - Google Patents
Automatic detecting method of machine vision system for detecting detained needle bushing burrs Download PDFInfo
- Publication number
- CN102495073A CN102495073A CN2011103590666A CN201110359066A CN102495073A CN 102495073 A CN102495073 A CN 102495073A CN 2011103590666 A CN2011103590666 A CN 2011103590666A CN 201110359066 A CN201110359066 A CN 201110359066A CN 102495073 A CN102495073 A CN 102495073A
- Authority
- CN
- China
- Prior art keywords
- burr
- remaining needle
- needle lining
- image
- burrs
- 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
- 238000000034 method Methods 0.000 title abstract description 11
- 238000001514 detection method Methods 0.000 claims abstract description 31
- 239000002699 waste material Substances 0.000 claims abstract description 10
- 238000004519 manufacturing process Methods 0.000 claims description 16
- 230000004438 eyesight Effects 0.000 claims description 15
- 238000007689 inspection Methods 0.000 claims description 13
- 238000012360 testing method Methods 0.000 claims description 9
- 230000001360 synchronised effect Effects 0.000 claims description 4
- 238000013459 approach Methods 0.000 claims description 3
- 238000007599 discharging Methods 0.000 claims description 2
- 239000000284 extract Substances 0.000 claims description 2
- 238000012797 qualification Methods 0.000 abstract description 4
- 238000012545 processing Methods 0.000 description 5
- 239000013307 optical fiber Substances 0.000 description 4
- 230000000007 visual effect Effects 0.000 description 4
- 230000003321 amplification Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 239000008280 blood Substances 0.000 description 2
- 210000004369 blood Anatomy 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 238000013461 design Methods 0.000 description 2
- 239000000835 fiber Substances 0.000 description 2
- 230000004313 glare Effects 0.000 description 2
- 238000009499 grossing Methods 0.000 description 2
- 239000002184 metal Substances 0.000 description 2
- 238000003199 nucleic acid amplification method Methods 0.000 description 2
- 208000003464 asthenopia Diseases 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 206010016256 fatigue Diseases 0.000 description 1
- 239000011521 glass Substances 0.000 description 1
- 238000001802 infusion Methods 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
Images
Landscapes
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The invention provides an automatic detecting method of a machine vision system for detecting detained needle bushing burrs, wherein transverse lengths of protruding burrs and sunken burrs at an inclined surface of an end part of the detained needle bushing are set as detecting parameters, detecting precision and qualification range of different detecting parameters are set according to requirements of users, a camera is started by an external trigger and control signal to photograph in real time images of the on-line moving detained needle bushing, the photographed images are transmitted to a computer for detection, the images of the detained needle bushing containing burrs are extracted through image algorithmic process by the computer, sizes of two types of burrs are calculated, products are judged to be qualified or waste according to the calculated sizes of the burrs, and the waste products are taken out from a specified discharge opening through the external trigger and the control signal. The automatic detecting method of the machine vision system for detecting detained needle bushing burrs has high precision and speed in detecting the burrs of the detained needle bushing, and can effectively ensure the qualification rate of the products.
Description
Technical field
The present invention relates to utilize NI Vision Builder for Automated Inspection to carry out the technical field of online detection, relate in particular in remaining needle lining production scene, the method for utilizing NI Vision Builder for Automated Inspection whether to exist burr to detect automatically to the remaining needle lining.
Background technology
Remaining needle be used to infuse, device that blood transfusion and extracorporeal circulation of blood etc. are disposed.Remaining needle remains on the top of remaining needle lining with needle tubing, and the rear end of remaining needle lining connects transfusion with pipe.The medical worker is thrust patient body with said needle tubing with said lining.After this said needle tubing is pulled out, and said lining is kept somewhere in patient body.Said remaining needle lining is connected with infusion set and is used for infusing.
Whether the remaining needle lining making-up shop in line production is on-the-spot, need exist burr to carry out online detection to the chamfered portion of remaining needle gasket end.If there is burr in the chamfered portion of remaining needle gasket end, then can stab patient.In the prior art; Online detection to remaining needle gasket end chamfered portion burr relies on the special-purpose magnifier of artificial use to detect; Produce the machine side at the remaining needle lining and establish detection and the processing that about 80 people carry out remaining needle lining burr; According to testing result product is divided into certified products (like protrusion burr lateral length＜0.01um, or notch burr lateral length＜0.01um, and not having other burrs) and unacceptable product (as protruding burr lateral length >=0.01um; Or notch burr lateral length >=0.01um, or other burrs beyond above two kinds are arranged).Unacceptable product is directly as goods rejection.
The shortcoming that manual detection exists mainly contains: the on-the-spot ventilation of workshop is poor, and workman's testing environment is abominable, can't directly use range estimation (needing use special-purpose hand magnifying glass), and labour intensity is big; The normal eye promptly can dim eyesight, eye discomfort such as expand about uninterrupted observation moving object 30min, and testing staff's non-stop run for a long time can't guarantee the product export qualification rate; It is the detection with very high quantity precision that remaining needle lining burr detects, and human eye can't judge accurately that error is big, and the chance of makeing mistakes is a lot, can't guarantee to detect quality; The speed that the professional detects the remaining needle lining is up to 0.5/s, and throughput rate is had very big restriction.
The content of invention
Online detection dependence manual work to remaining needle lining burr detects to prior art, and the workman is easy to generate visual fatigue, and labour intensity is big; Can't guarantee product percent of pass and detect quality; Problems such as monitoring velocity is low, the present invention provides the automatic testing method of a kind of NI Vision Builder for Automated Inspection to remaining needle lining burr, and it reduces workman's detection labour intensity greatly; Accuracy of detection is high, speed is fast, the qualification rate of the product that can effectively guarantee to dispatch from the factory.
Technical scheme of the present invention is following:
A kind of NI Vision Builder for Automated Inspection may further comprise the steps the automatic testing method of remaining needle lining burr:
(1) the remaining needle lining is fixed on the frock bar anchor clamps, makes frock bar anchor clamps on-line operation, taking frock bar anchor clamps one side of camera fixing at on-line operation; According to the size of remaining needle lining to be detected and remaining needle lining towards, select the focal length of camera lens, shooting angle, enlargement factor, shooting distance, aperture size, the time shutter of camera taken in adjustment, so that obtain photographic images clearly;
(2) the burr lateral length with remaining needle gasket end chamfered portion is made as the detection parameter, and according to customer requirements the accuracy of detection and the acceptability limit of said detection parameter is set;
(3) computing machine is obtained camera and synchronous triggering and the control signal of production process, starts the image that said camera is taken on-line operation remaining needle lining burr in real time by external trigger and control signal, and the image of taking is transferred to computing machine confession detection;
(4) computing machine is handled through image algorithm, extracts the image of remaining needle lining burr; Find not have the burr image as if handling, think that then there is not burr in this remaining needle lining, belong to certified products, sort out from certified products letter sorting mouth through image algorithm;
(5) computing machine calculates the size of said remaining needle lining burr; For remaining needle lining protrusion burr; Calculate said protrusion burr and remaining needle lining edge tie point and the outer lateral extent remaining needle lining vertical centre solstics of protrusion burr distance between the two, this distance value also is the lateral length value of said protrusion burr; For remaining needle lining notch burr; Calculate said notch burr and remaining needle lining edge tie point and notch burr inner distance remaining needle lining vertical centre closest approach distance between the two, this distance value also is the lateral length value of said notch burr;
(6) judge that through the burr size that calculates this product belongs to certified products, or waste product, rejects waste product through external trigger and control signal from the discharging opening of appointment.
Its further technical scheme is: to said (6) step, specifically carry out the judgement and the go-on-go of burr by following step:
(7) judge whether burr is protrusion burr or notch burr, if the protrusion burr then turned to for (8) step, if the notch burr then turned to for (9) step; If other unknown flaws then turned to for (11) step;
Whether the lateral length of (8) judging protrusion burr image is at acceptability limit＜0.01um, as then turned to for (10) step at acceptability limit, if then turned to for (11) step more than or equal to acceptability limit >=0.01um;
Whether the lateral length of (9) judging notch burr image is at acceptability limit＜0.01um, as then turned to for (10) step at acceptability limit, if then turned to for (11) step more than or equal to acceptability limit >=0.01um;
(10) sort as certified products;
(11) directly as goods rejection.
And its further technical scheme is: to said (6) step, when detecting product and be waste product, computing machine will carry out picture cues through man-machine interface, and start warning device.
Useful technique effect of the present invention is:
The present invention adopts NI Vision Builder for Automated Inspection that remaining needle lining burr is carried out automatic on-line and detects, and replaces manual detection, and the user can carry out the adjusting of accuracy of detection automatically.Have the record, classification, statistics, storage, the query function that product are detected certified products, this two series products of waste product.And in image, point out the unacceptable product situation through friendly man-machine interface, and give sound, light alarm, reduce workman's detection labour intensity greatly.
Manual detection speed is generally 0.5/s, and the NI Vision Builder for Automated Inspection detection speed can reach 3 ~ 4/s, and the product detection speed of NI Vision Builder for Automated Inspection is artificial 6 ~ 8 times, has greatly improved production efficiency.
Manual detection can't uninterruptedly be carried out product quality in 24 hours and detect owing to environment and physiological reason, adopted NI Vision Builder for Automated Inspection to detect and then made it become possibility.The production time of equipment can prolong to greatest extent, has improved usage ratio of equipment.
The artificial detection because poor, the vision fatiguability that ventilates is difficult to the Continuous Tracking product quality.Quantizing to detect leans on artificial being difficult to guarantee that improper defect rate generally about 8 ~ 10%, has caused the significant wastage of the resources of production and production cost; The detection dimensional accuracy of NI Vision Builder for Automated Inspection is up to 0.001um, and precision can be adjusted, and is set to 0.001,0.002,0.005, several accuracy classes such as 0.01um, thereby improves product percent of pass greatly and detect quality.
Description of drawings
Fig. 1 is normal remaining needle lining image.
Fig. 2 is the remaining needle lining image that the protrusion burr is arranged.
Fig. 3 is the remaining needle lining image that the notch burr is arranged.
Fig. 4 is a process sequence diagram of the present invention.
Embodiment
Further specify below in conjunction with the accompanying drawing specific embodiments of the invention.
Fig. 1, Fig. 2, Fig. 3 take from remaining needle lining side and real image after treatment.
In Fig. 2, photographic images shown in Figure 3, in order better to distinguish figure, burr partly adopts the shape of general burr to represent the burr classification among the figure, and has carried out the exaggeration expression; Blank parts is a remaining needle lining image through the transparent space after the denoising software processes all around, shown in lines be the contour images of remaining needle lining after handling.
Embodiment 1, to the detection of protrusion burr product is arranged:
Protrusion burr image as shown in Figure 2, wherein the outshot of upper right quarter is the visual pattern of protrusion burr.
Basler ACA640-100GM type industrial camera is fixed on the side of remaining needle lining; Camera is about 20mm apart from the distance of remaining needle lining side, uses to execute and bears 50 times of amplification zooms change times camera lenses, and focal length transfers to 16mm; Aperture is transferred to maximal value, and the time shutter is adjusted to 0.41ms.Protrusion burr accuracy of detection is set to 0.001um, and the setting certified products normally protrude the burr lateral length and are 0.01um to the maximum.Adopt special-purpose white fiber optic point source; Shine (backlight) from the heteropleural side of camera; And use and semiclosedly block the influence that the metal framework shields extraneous veiling glare, so that obtain visual pattern more stablely, embody the obvious characteristic of remaining needle lining protrusion burr.The optical fiber source of this project uses the machine vision special light source (also can use the optical fiber source of other companies) of CCS company, so that can photograph distinct image more stablely, and is shown in the screen of computing machine.Adopt the frock bar anchor clamps (faller gill) on the production line to carry out the conveying of remaining needle lining, guarantee that the remaining needle lining by certain direction and speed, stably gets into pick-up unit with belt transmission system.
Computing machine is according to the different control system of institute of different production firm production equipment; Obtain synchronous triggering of camera and production process and control signal; Start said industrial camera and take the image of the remaining needle lining of on-line operation, and the protrusion burr image that will obtain, be stored in the computing machine.
Computing machine carries out Flame Image Process to captured image through edge extracting, smoothing denoising, binary conversion treatment, Fourier Tranform scheduling algorithm, makes image more clear, more meets the truth of remaining needle lining protrusion burr.The algorithm that is adopted in the above-mentioned image processing process is conventional algorithm of the prior art.
Computing machine calculates the lateral length of remaining needle lining protrusion burr.This length is said protrusion burr and remaining needle lining edge tie point and the distance (getting maximal value) of protruding the outer lateral extent remaining needle lining vertical centre solstics of burr, and this distance value also is the lateral length value of said protrusion burr.
Like detected lateral length value is 0.004um, and then this product is certified products; Like detected lateral length value is 0.011um, and then this product is a unacceptable product.Computing machine is pointed out the unacceptable product situation through friendly man-machine interface in image, and gives sound, light alarm, and certified products are write down, classify, add up warehouse-in.
Embodiment 2, to the detection of notch burr product is arranged:
Notch burr as shown in Figure 3 (opening steam bubble flaw) image, the visual pattern that wherein upper right barbed portion forms for the notch burr.
Basler ACA640-100GM type industrial camera is fixed on the side of remaining needle lining; Camera is about 20mm apart from the distance of remaining needle lining side, uses to execute and bears 50 times of amplification zooms change times camera lenses, and focal length transfers to 16mm; Aperture is transferred to maximal value, and the time shutter is adjusted to 0.41ms.Notch burr accuracy of detection is set to 0.001um, sets the normal notch burr of certified products lateral length and is 0.01um to the maximum.Adopt special-purpose white fiber optic point source; Shine (backlight) from the heteropleural side of camera; And use and semiclosedly block the influence that the metal framework shields extraneous veiling glare, so that obtain visual pattern more stablely, embody the obvious characteristic of remaining needle lining notch burr.The optical fiber source of this project uses the machine vision special light source (also can use the optical fiber source of other companies) of CCS company, so that can photograph distinct image more stablely, and is shown in the screen of computing machine.Adopt the frock bar anchor clamps (faller gill) on the production line to carry out the conveying of remaining needle lining, guarantee that the remaining needle lining by certain direction and speed, stably gets into pick-up unit with belt transmission system.
Computing machine is according to the different control system of institute of different production firm production equipment; Obtain synchronous triggering of camera and production process and control signal; Start said industrial camera and take the image of the remaining needle lining of on-line operation, and, be stored in the computing machine the notch burr image that obtains.
Computing machine carries out Flame Image Process to captured image through edge extracting, smoothing denoising, binary conversion treatment, Fourier Tranform scheduling algorithm, makes image more clear, more meets the truth of remaining needle lining notch burr.The algorithm that is adopted in the above-mentioned image processing process is conventional algorithm of the prior art.
Computing machine calculates the lateral length of remaining needle lining notch burr.This length is the distance (getting maximal value) of said notch burr and remaining needle lining edge tie point and notch burr inner distance remaining needle lining vertical centre closest approach, and this distance value also is the lateral length value of said notch burr.
Like detected lateral length value is 0.004um, and then this product is certified products; Like detected lateral length value is 0.012um, and then this product is a unacceptable product.Computing machine is pointed out the unacceptable product situation through friendly man-machine interface in image, and gives sound, light alarm, and certified products are write down, classify, add up warehouse-in.
In above-mentioned two embodiment, also have other unknown flaw images to occur if handle discovery through image algorithm, think that then this remaining needle lining is a unacceptable product, rejects this remaining needle lining as waste product.
More than the control system (hardware and software) of the image capture device (camera, radiation source, power supply, image pick-up card etc.) that uses among all embodiment and storage device (hard disk, CD, floppy disk etc.), image processing equipment (hardware of image processor and software), image display (hardware and software), warning device and each part mentioned above all adopt prior art to design and produce or directly adopt relevant commercially available prod.
Above-described processing step of the present invention is shown in Fig. 4.
It should be noted that above-described at last only is preferred implementation of the present invention, the invention is not restricted to above embodiment.Be appreciated that other improvement and variation that those skilled in the art directly derive or associate under the prerequisite that does not break away from spirit of the present invention and design, all should think to be included within protection scope of the present invention.
Claims (3)
1. a NI Vision Builder for Automated Inspection is characterized in that may further comprise the steps to the automatic testing method of remaining needle lining burr:
(1) the remaining needle lining is fixed on the frock bar anchor clamps, makes frock bar anchor clamps on-line operation, taking frock bar anchor clamps one side of camera fixing at on-line operation; According to the size of remaining needle lining to be detected and remaining needle lining towards, select the focal length of camera lens, shooting angle, enlargement factor, shooting distance, aperture size, the time shutter of camera taken in adjustment, so that obtain photographic images clearly;
(2) the burr lateral length with remaining needle gasket end chamfered portion is made as the detection parameter, and according to customer requirements the accuracy of detection and the acceptability limit of said detection parameter is set;
(3) computing machine is obtained camera and synchronous triggering and the control signal of production process, starts the image that said camera is taken on-line operation remaining needle lining burr in real time by external trigger and control signal, and the image of taking is transferred to computing machine confession detection;
(4) computing machine is handled through image algorithm, extracts the image of remaining needle lining burr; Find not have the burr image as if handling, think that then there is not burr in this remaining needle lining, belong to certified products, sort out from certified products letter sorting mouth through image algorithm;
(5) computing machine calculates the size of said remaining needle lining burr; For remaining needle lining protrusion burr; Calculate said protrusion burr and remaining needle lining edge tie point and the outer lateral extent remaining needle lining vertical centre solstics of protrusion burr distance between the two, this distance value also is the lateral length value of said protrusion burr; For remaining needle lining notch burr; Calculate said notch burr and remaining needle lining edge tie point and notch burr inner distance remaining needle lining vertical centre closest approach distance between the two, this distance value also is the lateral length value of said notch burr;
(6) judge that through the burr size that calculates this product belongs to certified products, or waste product, rejects waste product through external trigger and control signal from the discharging opening of appointment.
2. according to the automatic testing method of the said NI Vision Builder for Automated Inspection of claim 1, it is characterized in that said (6) step is specifically carried out the judgement and the go-on-go of burr by following step to remaining needle lining burr:
(7) judge whether burr is protrusion burr or notch burr, if the protrusion burr then turned to for (8) step, if the notch burr then turned to for (9) step; If other unknown flaws then turned to for (11) step;
Whether the lateral length of (8) judging protrusion burr image is at acceptability limit＜0.01um, as then turned to for (10) step at acceptability limit, if then turned to for (11) step more than or equal to acceptability limit >=0.01um;
Whether the lateral length of (9) judging notch burr image is at acceptability limit＜0.01um, as then turned to for (10) step at acceptability limit, if then turned to for (11) step more than or equal to acceptability limit >=0.01um;
(10) sort as certified products;
(11) directly as goods rejection.
3. according to the automatic testing method of the said NI Vision Builder for Automated Inspection of claim 1, it is characterized in that to said (6) step when detecting product and be waste product, computing machine will carry out picture cues through man-machine interface, and start warning device to remaining needle lining burr.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110359066 CN102495073B (en) | 2011-11-14 | 2011-11-14 | Automatic detecting method of machine vision system for detecting detained needle bushing burrs |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN 201110359066 CN102495073B (en) | 2011-11-14 | 2011-11-14 | Automatic detecting method of machine vision system for detecting detained needle bushing burrs |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102495073A true CN102495073A (en) | 2012-06-13 |
| CN102495073B CN102495073B (en) | 2013-07-31 |
Family
ID=46186914
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN 201110359066 Expired - Fee Related CN102495073B (en) | 2011-11-14 | 2011-11-14 | Automatic detecting method of machine vision system for detecting detained needle bushing burrs |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102495073B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110763692A (en) * | 2019-10-29 | 2020-02-07 | 复旦大学 | Belted steel burr detecting system |
| CN110987959A (en) * | 2019-12-16 | 2020-04-10 | 广州量子激光智能装备有限公司 | Online burr detection method |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009092474A (en) * | 2007-10-05 | 2009-04-30 | Denso Corp | Flash detection method for molded article |
| CN201408031Y (en) * | 2009-05-07 | 2010-02-17 | 深圳市比克电池有限公司 | Battery pole piece burr detecting device |
| CN201731837U (en) * | 2010-06-17 | 2011-02-02 | 江苏通达动力科技股份有限公司 | Burr detection device |
| CN201828525U (en) * | 2010-10-11 | 2011-05-11 | 庄鸿 | Automatic detection system for disposable syringe needles |
-
2011
- 2011-11-14 CN CN 201110359066 patent/CN102495073B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009092474A (en) * | 2007-10-05 | 2009-04-30 | Denso Corp | Flash detection method for molded article |
| CN201408031Y (en) * | 2009-05-07 | 2010-02-17 | 深圳市比克电池有限公司 | Battery pole piece burr detecting device |
| CN201731837U (en) * | 2010-06-17 | 2011-02-02 | 江苏通达动力科技股份有限公司 | Burr detection device |
| CN201828525U (en) * | 2010-10-11 | 2011-05-11 | 庄鸿 | Automatic detection system for disposable syringe needles |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110763692A (en) * | 2019-10-29 | 2020-02-07 | 复旦大学 | Belted steel burr detecting system |
| CN110763692B (en) * | 2019-10-29 | 2022-04-12 | 复旦大学 | Belted steel burr detecting system |
| CN110987959A (en) * | 2019-12-16 | 2020-04-10 | 广州量子激光智能装备有限公司 | Online burr detection method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102495073B (en) | 2013-07-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102303017B (en) | Method for automatically detecting bending of injector steel needles by using machine vision system | |
| CN102303018B (en) | Method for automatically detecting burrs of injector steel needles by using machine vision system | |
| CN101696945B (en) | On-line detection method of machine vision system to photovoltaic glass flaws | |
| CN204479490U (en) | A kind of laser marking On-line Product testing and analysis system | |
| CN102494773A (en) | Automatic detection method of machine vision system on snap-fastener chromatic aberration | |
| CN101509766B (en) | On-line detecting method for spring end plane angle by machine vision system | |
| CN101696943B (en) | On-line detection method of machine vision system to medical surgical knife flaws | |
| CN109406533A (en) | A kind of detection system and method for surface defects of products | |
| CN101696877A (en) | On-line detection method of machine vision system to spring verticality | |
| CN102928431A (en) | Device for automatically grading pearls on line according to size and shape on basis of monocular multi-view machine vision | |
| WO2024055796A1 (en) | Defect detection system, method and apparatus, electronic device, and storage medium | |
| CN101726265A (en) | Online detection method of parallelism of spring by machine vision system | |
| CN105891228A (en) | Optical fiber appearance defect detecting and outer diameter measuring device based on machine vision | |
| WO2022100145A1 (en) | Infusion bag foreign matter identification method and detection method based on artificial intelligence visual technology | |
| CN102305592A (en) | Method for automatically detecting back-stitching of steel needle of injector by using mechanical vision system | |
| CN102305597A (en) | Method for automatically detecting abnormal shape of injector part by using mechanical vision system | |
| TW202219494A (en) | A defect detection method and a defect detection device | |
| CN102608125A (en) | Appearance quality detection system of tows | |
| CN102305603B (en) | Method for automatically detecting inclination of steel needle of injector by machine vision system | |
| CN102305797A (en) | Method for automatically detecting foreign matter of medical product package by using machine vision system | |
| CN102495073B (en) | Automatic detecting method of machine vision system for detecting detained needle bushing burrs | |
| CN102305950A (en) | Method for automatically detecting missed accessory during automatic assembly of injector by using machine visual system | |
| CN103389312B (en) | Copper pipe detection system | |
| CN102495077A (en) | Automatic detecting method of machine vision system for detecting flaws of infusion bottle | |
| CN102507602A (en) | Method for automatically detecting rupture of infusion bottle mouth by using machine vision 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 | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20130731 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |