CN112326664A - Automatic quality control method for heat-insulating container - Google Patents
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Abstract
The invention relates to the technical field of manufacturing of heat-insulating containers, in particular to an automatic quality control method of a heat-insulating container. According to the automatic quality control method for the heat-insulating container, the traditional manual identification is replaced by the visual detection technology, the manual visual inspection is changed into the visual dynamic capture, the detection result can be quickly obtained, the false detection and the omission of the manual detection are avoided, the accuracy is higher, and the detection qualified rate is higher.
Description
Technical Field
The invention relates to the technical field of manufacturing of heat-insulating containers, in particular to an automatic quality control method for a heat-insulating container.
Background
Machine vision is a branch of the rapid development of artificial intelligence. In brief, machine vision is to use a machine to replace human eyes for measurement and judgment. The machine vision system converts the shot target into an image signal through a machine vision product (namely an image shooting device), transmits the image signal to a special image processing system to obtain the form information of the shot target, and converts the form information into a digital signal according to information such as pixel distribution, brightness, color and the like; the image processing system performs various calculations on these digital signals to extract the features of the target, and then controls the operation of the on-site equipment according to the result of the discrimination.
In foreign countries, the popularity of machine vision is mainly reflected in the semiconductor and electronic industries, and approximately 40% -50% of them are concentrated in the semiconductor industry. Machine vision systems are also widely used in various aspects of quality inspection, and their products are of great importance in application. In china, the application of vision technology began in the 90 s, and the popularization of machine vision product technology was insufficient, resulting in almost blank applications in the above industries.
The flaw detection of the heat preservation container in the current market is basically carried out in a manual identification mode, time and labor are wasted, a large amount of energy is needed, meanwhile, missing detection and error detection are easy to occur in the process of detection, and the accuracy is not high.
Disclosure of Invention
In view of the above, the present invention provides an automatic quality control method for a thermal insulation container, which adopts a visual inspection technology to replace the traditional manual identification, changes the manual visual inspection into visual dynamic capture, can quickly obtain a detection result, avoids the false detection and the omission of the manual inspection, and has the advantages of higher accuracy and higher qualified rate of detection.
The invention solves the technical problems by the following technical means:
an automatic quality control method for a heat preservation container, the quality control method comprises the following steps:
s1: polishing the surface of a workpiece, and continuously acquiring images of the surface of the workpiece by using an industrial camera to obtain sampling images;
s2: returning the collected sampling image to the image processing unit, preprocessing the received image by the image processing unit to highlight the image characteristics, judging whether the surface of the workpiece has flaws or not by the image processing unit according to the extracted image characteristics, and sending a detection result to the PLC;
s3: the PLC controller sends an instruction to the six-axis robot according to the received judgment result if the product is qualified, the workpiece is grabbed and placed in a welding line recognition detection device for welding line recognition, a processing safe area is found, then the PLC controller transmits signals to control the six-axis robot to grab the workpiece and lock the angle and the direction of the workpiece, and the six-axis robot is switched to a punch for typing;
if the product has defects, the PLC sends an instruction to the six-axis robot to grab and place the workpiece into a defective basket
S4: after the six-axis robot sends the workpiece into the die positioning point of the punch, the PLC transmits a signal to the punch for typing, and after the machining is finished, the six-axis robot takes out the workpiece and enters the next procedure.
Further, in the step S1, a sampled image is obtained by combining the light of the line-added structure of the industrial high-speed area-array camera with the line-added light source of the line-scan camera.
The industrial high-speed area array camera is adopted, the millisecond detection frequency can be achieved, the detection time is short, the production efficiency is improved, meanwhile, the defects on the surface of the workpiece can be detected more comprehensively in a mode of combining the linear structure light of the industrial high-speed area array camera and the linear light source of the linear scanning camera, and the detection is more perfect.
Further, the defects in the step S2 include, but are not limited to, pits, striae, scratches, uneven brightness, thick lines, and ripples.
Further, the step S2 specifically includes: returning the collected sampling image to an image processing unit, carrying out self-adaptive binarization and morphological opening and closing operation noise reduction processing on the image, highlighting image characteristics, identifying the outline of the workpiece, extracting the area image of the workpiece, zooming the extracted area image to the size same as that of a non-defective reference image, carrying out pixel point level comparison on the area image and the non-defective reference image, detecting the non-defective workpiece and the defective workpiece, and sending a detection result to a PLC (programmable logic controller).
Further, in the step S3, the weld joint recognition and detection device performs weld joint detection by using a chromatograph.
When the chromatograph is used for detection, the straight seam of the welded workpiece is in a black-gray linear shape, the surface layer of the product which is not welded is silvery-white, the color difference is obvious, and the welding seam can be quickly identified.
Further, in the step S3, the finding of the processing safety area specifically operates as: the welding seam detection is carried out on the 90-degree area of the workpiece which is arranged in the welding seam identification detection device and is over against the chromatograph,
if no welding line exists, the detected 90-degree area is judged to be a processing safe area, a signal is transmitted to the six-axis robot through the PLC, a product is grabbed, the angle and the direction of a workpiece are locked, and the six-axis robot is switched to a punch press for typing;
if the welding line is detected, the welding line is rotated by 90 degrees, the operation of detecting and rotating by 90 degrees is repeated, and if no welding line exists in the detection area, the detected 90-degree area is judged to be a processing safe area, signals are transmitted to the six-axis robot through the PLC, the product is grabbed, the angle and the direction of the product are locked, and the typing processing is carried out by a punch.
Further, when the welding seam recognition device does not detect the welding seam in the current 90-degree area, the welding seam recognition device rotates the welding seam by 60 degrees leftwards and rightwards by taking the center line of the current 90-degree area as a starting point to detect the welding seam, if the welding seam is not detected, the current 90-degree area is determined to be a processing safe area, and if the welding seam is detected, the current 90-degree area rotates 30 degrees towards the direction opposite to the position of the welding seam to be determined to be the processing safe area.
Further, a secondary detection step is included between the steps of S2 and S3, specifically: the PLC controller, according to the received judgment result, if the product is qualified, placing the qualified workpiece in a heating box by using a six-axis robot, heating to 50-60 ℃, taking out, rapidly coating a layer of enhancement film liquid, placing in a vacuum drying box, keeping the temperature and vacuumizing for 10-12min, forming an enhanced film layer on the surface of the workpiece, taking out and using the industrial camera again to continuously acquire images on the surface of the workpiece to acquire a secondary sampling image, the workpiece after image acquisition is grabbed by a six-axis robot and placed in ice water bath at the temperature of 0-5 ℃, and after being cleaned, the workpiece is dried and enters the next procedure, the collected secondary sampling image is returned to the image processing unit, the image processing unit carries out pretreatment on the received image to highlight the image characteristics, and the image processing unit judges whether the surface of the workpiece has flaws or not according to the extracted image characteristics and sends the detection result to the PLC.
Further, the preparation method of the enhanced membrane liquid comprises the following steps: weighing N, N-dimethylacrylamide and N-isopropylacrylamide with equal molar mass, stirring and dissolving in deionized water, adding modified nano carbon powder, ultrasonically dispersing uniformly, adding diphenyl titanium fluoride and ethylene glycol dimethacrylate, magnetically stirring for 30min, adding into a reactor, reacting for 2-5min at the reaction temperature of 50 ℃ under the condition of ultraviolet irradiation under the nitrogen atmosphere, taking out a reaction product after the reaction is finished, dialyzing for 7d with distilled water, replacing the distilled water every 12h, and adding into distilled water with the temperature of 30-45 ℃ after the dialysis is finished, and continuously stirring for 10-12h to obtain the enhanced membrane liquid.
Further, the preparation method of the modified nano carbon powder comprises the following steps: weighing silane coupling agent, adding into distilled water, dropwise adding 4-5 drops of acetic acid, performing ultrasonic oscillation for 10min to obtain hydrolysate, weighing nano carbon powder, adding into distilled water, strongly stirring and dispersing for 10min, adding the hydrolysate, performing constant-temperature reflux for 6h at 80 ℃, performing ultrasonic oscillation for 30min on reaction liquid after reaction, centrifuging and separating, washing the solid with absolute ethyl alcohol for three times, and performing vacuum drying at 40 ℃ to obtain the modified nano carbon powder.
The method is characterized in that concave points or scratches on the surface of the workpiece are very tiny and are not easy to be compared in the process of processing by an image processing unit, so that detection omission is caused, therefore, a secondary detection step is added between the steps S1 and S2, the surface of the workpiece is coated with a reinforcing membrane liquid, the reinforcing membrane liquid adopts N, N-dimethylacrylamide and N-isopropylacrylamide as main materials, has temperature-sensitive characteristics, is in a liquid state or a solid state at the temperature of 10-30 ℃, and is in a solid state above 30 ℃, so that a solid reinforcing membrane layer is formed on the surface of the workpiece before secondary image acquisition through temperature control, and can be eluted in an ice-water bath after the image acquisition is finished without influencing the surface of the workpiece.
After one-time detection, the pits or scratches on the surface of the workpiece are extremely small, in the coating process, the enhancement film liquid cannot enter the pits or scratches, so that air exists between the pits or scratches and the enhancement film layer, and then the air in the pits or scratches breaks through the enhancement film layer to overflow through vacuumizing, so that holes are left on the enhancement film layer, and then modified nano carbon powder in the enhancement film liquid is combined.
The invention has the beneficial effects that:
1. according to the automatic quality control method for the heat-insulating container, disclosed by the invention, various defects are digitalized and quantized by adopting a visual detection technology, manual visual inspection is changed into visual dynamic capture, a detection result can be quickly obtained, the false detection and the omission of manual detection are avoided, and the detection qualified rate is higher.
2. The invention can realize online automatic production by adopting visual detection, reduces inspectors, can automatically classify products according to detection results, adopts visual non-contact measurement, has wider spectral response range, continuous production and lower cost, and is easy to realize information integration by machine vision, high in precision and good in flexibility.
3. According to the automatic quality control method for the heat preservation container, the chromatograph is adopted for automatic identification to avoid weld line word punching, compared with the traditional manual weld line word punching identification, on one hand, the intelligent automatic identification can reduce errors to a certain extent, on the other hand, the intelligent automation replaces manual punching operation, and potential safety hazards caused by manual operation can be avoided.
Detailed Description
The present invention will be described in detail with reference to specific examples below:
the invention discloses an automatic quality control method of a heat preservation container, which comprises the following specific steps:
example one
S1: polishing the surface of a workpiece, continuously acquiring images on the surface of the workpiece by using an industrial camera, and specifically, acquiring a sampling image by combining light of a linear structure of an industrial high-speed area array camera with a linear light source of a linear scanning camera;
s2: returning the collected sampling image to an image processing unit, carrying out self-adaptive binarization and morphological opening and closing operation noise reduction processing on the image, highlighting image characteristics, identifying the outline of the workpiece, extracting a region image where the workpiece is located, zooming the extracted region image to the size same as that of a non-defective reference image, comparing the region image with the non-defective reference image in pixel point level, judging whether defects such as pits, wire drawing, scratches, uneven brightness, thick lines, ripples and the like exist on the surface of the workpiece, detecting the non-defective workpiece and the defective workpiece by setting a threshold value, and sending a detection result to a PLC (programmable logic controller);
s3: the PLC controller sends an instruction to the six-axis robot according to the received judgment result if the product is qualified, the workpiece is grabbed and placed into the welding seam recognition and detection device, the chromatograph is used for welding seam recognition, firstly, the workpiece placed in the welding seam recognition and detection device is directly opposite to a 90-degree area of the chromatograph for welding seam detection,
if no welding line exists, the detected 90-degree area is judged to be a processing safe area, a signal is transmitted to the six-axis robot through the PLC, a product is grabbed, the angle and the direction of a workpiece are locked, and the six-axis robot is switched to a punch press for typing;
if the welding seam is detected, rotating the welding seam by 90 degrees, repeating the operations of detection and 90 degrees until no welding seam exists in the detection area, judging that the detected 90-degree area is a processing safe area, transmitting a signal to a six-axis robot through a PLC (programmable logic controller), grabbing a product, locking the angle and the direction of the product, and switching to a punch press for typing;
if the product has defects, the PLC sends an instruction to the six-axis robot to grab and place the workpiece into a defective basket;
s4: after the six-axis robot sends the workpiece into the die positioning point of the punch, the PLC transmits a signal to the punch for typing, and after the machining is finished, the six-axis robot takes out the workpiece and enters the next procedure.
Example two
The present embodiment is different from the first embodiment in that, in step S3, when the weld joint recognition apparatus does not detect a weld joint in the current 90 ° region, the weld joint detection is performed by further rotating the weld joint recognition apparatus by 60 ° to both left and right from the center line of the current 90 ° region, if no weld joint is detected, the current 90 ° region is determined as a safe processing region, and if a weld joint is detected, the current 90 ° region is rotated by 30 ° in the direction opposite to the position of the weld joint, and is determined as a safe processing region.
EXAMPLE III
Preparing modified nano carbon powder: weighing a silane coupling agent KH-570, adding into distilled water with the volume 10 times that of the silane coupling agent KH-570, dropwise adding acetic acid with the same volume, ultrasonically oscillating for 10min to obtain a hydrolysate, weighing nano carbon powder, adding into distilled water with the mass 20 times that of the nano carbon powder, strongly stirring and dispersing for 10min, adding the hydrolysate, refluxing the nano carbon powder and the silane coupling agent at a constant temperature of 80 ℃ for 6h, ultrasonically oscillating the reaction liquid for 30min after the reaction is finished, centrifuging, separating, washing the solid with absolute ethyl alcohol for three times, and vacuum drying at 40 ℃ to obtain the modified nano carbon powder.
Preparing an enhanced membrane liquid: weighing N, N-dimethylacrylamide and N-isopropylacrylamide with equal molar mass, stirring and dissolving in deionized water, adding modified nano carbon powder, uniformly dispersing by ultrasonic, adding diphenyl titanium fluoride and ethylene glycol dimethacrylate, wherein the mass ratio of the modified nano carbon powder to the diphenyl titanium fluoride to the ethylene glycol dimethacrylate to the N, N-dimethylacrylamide is 1:0.1:0.2:2, magnetically stirring for 30min, adding into a reactor, reacting for 2-5min at 50 deg.C under nitrogen atmosphere and under ultraviolet irradiation, after reaction, taking out the reaction product, dialyzing with distilled water for 7d, replacing distilled water every 12h, adding 10 times of distilled water with the mass of 30-45 ℃ after dialysis is finished, and continuously stirring for 10-12h to obtain the enhanced membrane liquid.
The detection is carried out by using the prepared enhanced membrane liquid, which comprises the following steps:
s1: and polishing the surface of the workpiece, and continuously acquiring images on the surface of the workpiece by using an industrial camera to obtain sampling images.
S2: returning the collected sampling image to the image processing unit, preprocessing the received image by the image processing unit to highlight the image characteristics, judging whether the surface of the workpiece has flaws or not by the image processing unit according to the extracted image characteristics, and sending a detection result to the PLC;
s3: the PLC controller is used for placing a qualified workpiece in a heating box by using a six-axis robot to heat to 50-60 ℃ according to a received judgment result if the product is qualified, taking out the workpiece, quickly coating a layer of enhanced membrane liquid, placing the workpiece in a vacuum drying box, keeping the temperature and vacuumizing at 50 ℃ for keeping the vacuum degree at 45Pa for 10-12min, forming an enhanced membrane layer on the surface of the workpiece, taking out the workpiece, continuously collecting images on the surface of the workpiece by using an industrial camera again, obtaining secondary sampling images, grabbing the workpiece after image collection by using the six-axis robot, placing the workpiece in an ice water bath at 0-5 ℃, cleaning, drying, entering the next procedure, returning the collected secondary sampling images to an image processing unit, preprocessing the received images by the image processing unit, highlighting image characteristics, and judging whether the surface of the workpiece has flaws or not by the image processing unit according to the extracted image characteristics, and sending the detection result to the PLC again;
s4: the PLC controller sends an instruction to the six-axis robot according to the re-received judgment result if the product is qualified, the workpiece is grabbed and placed into the welding seam recognition and detection device, the chromatograph is used for welding seam recognition, firstly, the workpiece placed in the welding seam recognition and detection device is directly opposite to a 90-degree area of the chromatograph for welding seam detection,
if no welding seam exists, then taking the center line of the current 90-degree area as a starting point, rotating the current 90-degree area by 60 degrees leftwards and rightwards again for welding seam detection, if no welding seam is detected, determining the current 90-degree area as a processing safe area, if a welding seam is detected, rotating the current 90-degree area by 30 degrees towards the opposite direction of the position of the welding seam, determining the current 90-degree area as a processing safe area, transmitting a signal to a six-axis robot through a PLC (programmable logic controller), grabbing a product, locking the angular position of the workpiece, and switching to a punch press for typing;
if the welding line is detected, rotating 90 degrees, repeating the operations of detection and 90 degrees until no welding line exists in the detection area and no welding line exists in the range of rotating 30 degrees left and right, judging that the detected 90-degree area is a processing safe area, transmitting a signal to a six-axis robot through a PLC (programmable logic controller), grabbing a product, locking the angle position of the product, and switching to a punch press for typing;
if the product has defects, the PLC sends an instruction to the six-axis robot to grab and place the workpiece into a defective basket;
s3: after the six-axis robot sends the workpiece into the die positioning point of the punch, the PLC transmits a signal to the punch for typing, and after the machining is finished, the six-axis robot takes out the workpiece and enters the next procedure.
The automatic quality control method for the heat-insulating container in the first to third embodiments is used for performing flaw detection and typing processing on the produced heat-insulating container by using the punch, meanwhile, the existing manual detection and manual operation punch technology is adopted for comparison, after the heat-insulating container works for 8 hours, the detection number and the qualification rate of the products obtained by detection are counted, and the statistical results are shown in table 1:
TABLE 1
It can be seen from the data in table 1 that, by adopting the automatic quality control method for the heat-insulating container, the processing efficiency is obviously higher than that of the traditional manual detection method, and the qualification rate of the detected and processed product is obviously higher than that of the traditional manual detection and processing technology, while the comparison of the data in the first embodiment, the second embodiment and the third embodiment shows that after the secondary detection step, the omission ratio can be obviously reduced in the image acquisition stage, so that the detection qualification rate can reach 100%, and the comparison of the data in the first embodiment, the second embodiment and the third embodiment shows that the qualification rate of the final word punching processing can be obviously improved by further verifying the step of S3.
Although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted without departing from the spirit and scope of the invention as defined in the appended claims. The techniques, shapes, and configurations not described in detail in the present invention are all known techniques.
Claims (6)
1. An automatic quality control method for a heat preservation container is characterized by comprising the following steps:
s1: polishing the surface of a workpiece, and continuously acquiring images of the surface of the workpiece by using an industrial camera to obtain sampling images;
s2: returning the collected sampling image to the image processing unit, preprocessing the received image by the image processing unit to highlight the image characteristics, judging whether the surface of the workpiece has flaws or not by the image processing unit according to the extracted image characteristics, and sending a detection result to the PLC;
s3: the PLC controller sends an instruction to the six-axis robot according to the received judgment result if the product is qualified, the workpiece is grabbed and placed in a welding line recognition detection device for welding line recognition, a processing safe area is found, then the PLC controller transmits signals to control the six-axis robot to grab the workpiece and lock the angle and the direction of the workpiece, and the six-axis robot is switched to a punch for typing;
if the product has defects, the PLC sends an instruction to the six-axis robot to grab and place the workpiece into a defective basket
S4: after the six-axis robot sends the workpiece into the die positioning point of the punch, the PLC transmits a signal to the punch for typing, and after the machining is finished, the six-axis robot takes out the workpiece and enters the next procedure.
2. The automated quality control method for heat preservation containers of claim 1, wherein in the step S1, the sampled image is obtained by combining light of an industrial high-speed area-array camera with a line-scanning camera and a line light source.
3. The automated quality control method for thermal container as claimed in claim 2, wherein the defects in step S2 include, but are not limited to, pits, striae, scratches, uneven brightness, thick lines, and ripples.
4. The automated quality control method for the heat preservation container according to claim 3, wherein the step S2 is specifically as follows: returning the collected sampling image to an image processing unit, carrying out self-adaptive binarization and morphological opening and closing operation noise reduction processing on the image, highlighting image characteristics, identifying the outline of the workpiece, extracting the area image of the workpiece, zooming the extracted area image to the size same as that of a non-defective reference image, carrying out pixel point level comparison on the area image and the non-defective reference image, detecting the non-defective workpiece and the defective workpiece, and sending a detection result to a PLC (programmable logic controller).
5. The automated quality control method for heat-preserving container as claimed in claim 4, wherein in the step S3, the weld joint identification detection device performs weld joint detection by a chromatograph.
6. The automated quality control method for heat-preservation containers according to claim 5, wherein in the step of S3, the step of searching for the processing safety area is specifically operated as follows: the welding seam detection is carried out on the 90-degree area of the workpiece which is arranged in the welding seam identification detection device and is over against the chromatograph,
if no welding line exists, the detected 90-degree area is judged to be a processing safe area, a signal is transmitted to the six-axis robot through the PLC, a product is grabbed, the angle and the direction of a workpiece are locked, and the six-axis robot is switched to a punch press for typing;
if the welding line is detected, the welding line is rotated by 90 degrees, the operation of detecting and rotating by 90 degrees is repeated, and if no welding line exists in the detection area, the detected 90-degree area is judged to be a processing safe area, signals are transmitted to the six-axis robot through the PLC, the product is grabbed, the angle and the direction of the product are locked, and the typing processing is carried out by a punch.
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