Before applying cement, the smooth side of the tread surface must be inspected to ensure the quality adhesion of the tread to a tire carcass. Although a human inspector can easily find defects like blisters and cracks, small defects can be a challenge, and other surface defect anomalies may require shifting positions to find variation in surface sheen.
Our team shadowed human inspectors while collecting images of the product, annotated images, and then an image-based deep learning model was developed to detect surface anomalies. Through further data collection, annotation, and model iterations, the system gained the ability to classify the surface anomalies into several categories. Traditional vision (rule-based) algorithms were added to established pass/fail criteria based on shape, size, and brightness for each classification, thus reducing the need for 100% human inspection.
When customers are finished with a laser ink cartridge, they often send it back using the return box provided with new cartridges. These cartridges are returned to centralized recycling centers across the country by the truck loads. Used cartridges are removed from their packaging and thrown onto a conveyor belt for manual sorting. As volumes steadily increase, additional workforce is required to aid in the sorting. Since the recycling center is credited for each cartridge processed and specific cartridges have a higher number of recyclable components than others, an accurate count of models is highly desired.
PC-based vision system monitors the conveyor for cartridges as they enter into the field of view. At this point, it makes note of the cartridge's encoder location and its orientation. Using a blob tool, the system pre-classifies cartridges based on their shape using a high-end multi-core processor for parallelized pattern matching. Subsequent secondary pattern matching is performed in some instances to identify other key markers when subcategorization is required. A local register retains counts of each cartridge identified. Encoder location, orientation, and cartridge ID are passed to a spider robot to pick and place the cartridges into large bins. Multiple instances and orientations of well over one hundred cartridges were trained into the system.
Animal Health Sciences start-up was seeking a subject matter expert to develop a machine vision solution to determine the orientation of a baby chicken's head for the application of vaccine.
For this SBIR Phase 1 project, a custom lighting and optical solution were developed to acquire images of baby chicks as they passed under the camera. 1000 images were collected and annotated, 2/3rd of the images were then fed into Deep Learning algorithms. Detection performance was 97%, with an execution time of 19-22ms.
After the nail polish bottle has been capped, a consumer label is applied to the top of the cap that indicates the color name and product number. Space constraints prevented an inspection camera from being placed after the label applicator, so the Vision System needs to compensate for 360 orientation of the label.
With some blob morphology techniques, the orientation of the text can be determined. Afterward, an OCR tool was applied to read the text. If the text found did not match the HMI, the product was rejected.
Prior to a tray of vials being loaded in the freeze dryer, the number of vials needs to be counted and documented. Depending on the size of the vials, the number of vials on a tray can range from 100 to 225. Descrepencies between the manual count and actual count occur from time to time.
Due to the need for a continuous laminar airflow on the vials within the cleanroom, the vision system components could not be mounted directly above the vial tray. High-intensity side lights were deployed on the side of the tray. The vials would act as a light pipe allowing for sufficient contrast for the smart camera to identify and count the vials. The HMI of the software provided the Operator with an overlay for each bottle located, and a foot pedal allowed the Operator to acknowledge the count. The images were archived, and a PDF-generated report was saved to network storage at the end of the batch.
A labeling system was added to a mixed nut packaging line. A single line can process 16 different product lines at 120 - 150 jars per minute. Some products use very similar labels, with differences attributed to what country the product is being shipped to. Occasionally the wrong labels are loaded into the labeling system. If this goes unnoticed, a large number of jars need to have their labels removed, and new ones applied.
Cognex InSight cameras were mounted downstream from the labeler to view the front and backside of the jar. Multiple pattern matching zones were established to ensure no two different labels would generate a false positive and stored in a lookup table. Ethernet/IP communications was established between the PLC and the smart camera for effortless product changeovers via a PanelView HMI.
A large national conveyance company installed a conveyor and carton routing system. The Cognex Datamans (image-based barcode reader) ran well, but once the refrigeration system went online, the label reading on the cartons became unreliable.
Moisture and the cold environment caused the cartons' labels to wrinkle, and excessive packing tape by newly trained operators created hotspots on labels. An off-the-shelf cross-polarizing lighting assembly was unavailable at that time, so an existing dual lighting unit was modified with polarizing film and a polarizing filter added to the camera's lens.
A Temp labor team assembled packaging for box displays for a Trading Card game. Occasionally cards or accessories were incorrect or missing.
The product enters a multicamera light tunnel and a QA engineer uses a Wizard to draw ROIs for each card and accessory. Cognex VisionPro's PatMax was used for its robust pattern matching algorithms. The system would stop the conveyor once it exited the light tunnel if there was a mismatch or no match, and turn on a stack light.
Inkjet printer applies a lot and date code on round vitamin bottles. Jams or other printing issues can make the lot or date code unreadable.
Smart Camera was added to the system downstream from the Inkjet printer. System was presented with multiple instances of character from 0 - 9 and trained. PLC pushed the current Lot/Date code to the camera and the Smart Camera verified that it was a match.
Pocked waffle packs are used by pick and place machines in an electronic assembly. End user desired to have a machine that could handle various gold solder pad sizes and have 1 and only 1 gold solder pad placed into a single pocket for a waffle pack.
Gold solder pads were loaded into an Asyril vibratory feeder that would spread the product out for a vision system to identify a singulated solder pad. The robot would pick the solder pad up via suction cup and present it to another camera that would identify orientation and offsets. Using the x,y, and rotation offsets the gold solder pad was placed into the waffle pack.
Seeds are collected and manually placed into a 96-well plate with gaps left open for control samples. These blocks enter into a machine that crushes the sends, which are then transferred over to another machine for genetic testing. The process of manually placing the seed in most, but not all wells of the test plate, is tedious and prone to mistakes. Also, it is important that one and only one seed is deposited in the appropriate well.
Seeds were dispensed into a small backlit bowl, A camera identified a single seed, with sufficient free space around it, so that a pick and place robot with a vacuum tip can move the seed from the bowl into one of the test wells of the 96-well plate.
A secondary camera, verified only one seed made it into the well of the plate. Certain seeds were prone to clog or gum up the surface of the vacuum tip and the secondary check verified compliance.
Product number, manufacturing date, and index are pressed into the surface of the railroad bearing. Since the characters are on a dial, if an individual character doesn't properly roll to the next character a duplicate serial number can be generated of the character is not fully formed.
The product was rolled until a laser sensor located the first character. A Cognex InSight line scan camera imaged the full length of the string and a pattern match was performed on each character.
Due to the variance in surface finish and impression depth (as the character die wore down), multiple images over the whole production were collected and a composite image for each character was generated. The composite image of the character was used to generate the pattern classifier.
Car interiors are often customized for their material, trim, and user features. Variations may be subtle and batch runs are small. An end-of-line inspection system is needed to ensure the product that was assembled matches the build ticket.
A camera with a bright field and dark field configuration was mounted to the end of an ABB robot. The operator loads an assembled car door interior onto an open nest and presses the Inspect bottom. The robot would move to 20 different locations on the backside of the car door interior, triggering the camera/lights, and then requested the operator to rotate the fixture. The robot then moved to 6 different locations, triggering the camera/lights for each.
A mock display is presented to the Operator for pass/fail of all zones. The operator had the ability to select a zone to see the image taken and individual pass/fail results for each Poka-Yoke item.
Prior to loading the tire for shipment, the highpoint is located and the sidewall of the tire is marked with a red dot, white dot, white P, white N or a sticker. Storage and material handling of the tire can cause the highpoint mark to get rubbed off or faded.
Cognex VisionPro was used to identify and verify the presence/absence of the various types of highpoint marks based on the product SKU.
Tires of similar sizes can be produced with different tread patterns. A color band is applied on the tread surface based on the product SKU to ensure the wrong product is not mixed in with the shipment.
Cognex VisionPro was used to blend the color band together (because of the gap between tread lugs) and a color match is performed to verify it matches with the current SKU.
A fuse block assembly for a car or truck has similar frames, but what goes into them can vary. A component can easily be missed, such as a fuse or diode or the wrong relay model installed.
A GUI was developed to allow an Engineer to train a new component (color, pattern matching, OCR) and add it to a component family. Once populated with components, an Engineer can create a product SKU and create a Region of Interests (ROIs) for each component location on the fuse block. The engineer can choose the allowed orientation of the component for the ROI.
With a fully configured product, an Operator can recall the SKU from a part list and batch inspect multiple fuse blocks. Any component that is missing, in the wrong orientation, pattern score is too low, or OCR is mismatched, then the ROI is highlighted in red. Components that pass are highlighted in green.
Key blanks for major retailers are plated, stamped and channels cut. Non-conformances can include poor plating, poor stamping, misaligned stamping, and misaligned channels.
The system was trained with the average composite image of 10 or more keys. Cognex PatInspect and additional image processing were used to determine the difference image, then blob analysis determined pass/fail result.
Opiate Lozenges are sonic welded onto a plastic stick, inspected, and transferred to a packaging tray. An embossed number on the lozenge indicates the dosage of the product. The wrong or unreadable embossed number is considered a non-conformance. A chipped or cracked lozenge is also considered a non-conformance.
Cognex VisionPro was used to pattern match the embossed number; too low scores were rejected. Image processing tools masked out the embossed number, and edge detection algorithms were used to identify cracks/chips.
The product is placed by a robot into a thermoformed package. If the product is not properly placed into the individual pockets it can create a problem with the top foil seal.
Cognex InSight and pattern matching were used to verify the product was seated correctly in the individual pockets. The product line was stopped for non-conformance.
The lot and date code are printed on a label, and the label is applied to the bottom of the stick holding the opiate lozenge. Missing label, missing print, or incorrect lot/date code is considered a non-conformance.
Cognex VisionPro was used for verifying label placement and OCV of lot and date codes.
After the products have been loaded into a vacuum-formed tray, a top foil is applied. If the wrong foil has been loaded into the machine, and if gone unnoticed for a while, a whole batch of products would need to be destroyed. Sealed and unsealed products can not be repackaged.
A Cognex Smart camera was used to pattern match key features of the print on the top foil. Mismatches will signal to the PLC to stop the line.
International mail brought in through airline processing centers are often in large sacks with a 4”x6” card attached to it. Depending on the country of origin, some of these cards are hand written and others are printed. The individual at the processing window needs to manually enter in all of the information on the mail tag into a data entry system before it can be thrown onto a conveyor belt to be processed. This creates a bottle neck in the work flow, since only so many transfer windows can be manned and managed to allow baggage/cargo trucks to be unloaded.
A portable prototype imager with a flat surface allowed the mail handlers to position the mail tag for image acquistion. At the start of a new load the mail handler would enter in the Airline and Flight number, along with the number of pieces. The mail handler would take an image of that tag and then the local Vision PC would attempt to read all of the text on the tag, then present the image along with data to the mail handler. Once approved, the data would be saved to a database. If tag was not readable, the image was sent to a remote location where it was presented to a person for manual entry. This allowed for reconcilation of a load and moving mail bags as quickly as the manual imaging process would allow.
Supplier for the machines that manufacture the Permanent Residency Cards needed to ensure that the text printed on the card matches what is contained in the database. It was also to ensure the person’s face was printed clearly, and verify the presence of several security features.
Standard lighting techniques and Optical Character Recognition routines where used to validate the text and data that was contained in the database. Image correlation was used to validate the person’s face printed on the cards. Multiple different lighting techniques and LED wavelengths were used with different image processing alghrothims to verify the presence of all security features.
Supplier for the machines that manufacture the Driver’s Licenses needed to ensure the text printed on the card matched what is contained in the database. Also needed to ensure the person’s face was printed clearly, and verify presence of the engraved birth date on the card.
Standard lighting techniques and Optical Character Recognition routines where used to validate the text and data that was contained in the database. Image correlation was used to validate the person’s face printed on the cards. Photometric stereo imaging technique with IR LED lights was used to extract the engraving and filter out the print on the driver’s license.
Dashboard displays manufactured by automotive suppliers are hand assembled with different Legend Tags (door open, oil temp, washer fluid low, etc.). Before the product is packaged and shipped for assembly into a vehicle, 100% inspection is required to prevent quality chargebacks.
PC-based vision system reads the 2D barcode on the cluster assembly and queries a database. The database provides information on what Legend tags are to be installed in each of the 10 locations. Using pre-trained patterns, the system verifies all 10 locations on the cluster and prompts the Operator of any mismatches.
An Automotive manufacturer needed to verify the correct roof has been placed on the vehicle prior to robotic welding.
PC-based Vision system identified key features of 6 different possible roof configurations and verified a match based on the VIN barcode.
An Automotive manufacturer needed to verify the correct fender has been placed on the vehicle prior to robotic welding.
PC-based Vision system identified key features of 4 different possible fender configurations and verified a match based on the VIN barcode.
Tier 1 automotive supplier manufactures car frames. Mounts that are bent or incorrectly mounted on the frame need to be detected before additional value is added to the product.
PC-Based vision system located and profiled the mount that was welded to the frame. An alert was raised if the location or deformation was outside of tolerance by more than 1/4 inch.