Machine Vision & Deep Learning Integrator
"Enabling clients to produce a quality product & gather data with
Industrial Machine Vision & Deep Learning technology"
Portfolio
To prevent the injection of counterfeit products into the supply chain, the print shop manufacturer had a client request to serialize all the products that get placed onto consumer shelves, along with inner boxes, master cases, and pallets.
SOLUTION
A custom ASP.Net application was developed to augment the manual process of packing products into inner boxes, master cases, and stacking them on pallets. The system generated barcode labels and printed them to a Zebra printer. These labels would get affixed to the product and boxes. The product was scanned with a Cognex Dataman handheld ID Reader prior to being backed into a box. Once the box was filled, the barcode box was scanned, and all the relational data was stored in a database. Similarly, the filled inner boxes would be added to the master case, scanned, and stored relational data. Master boxes were then added to a pallet, and once the pallet was full, a pallet flag containing all the master cases, along with total product and inner box counts.
TAGS
ID Reading, Barcode Reader, Data Collection
A scheduling system dictates the components to be assembled on a diesel engine head. This information is written to an RFID tag that is attached to a carriage. The client needed a way to validate the correct engine head was loaded onto the carriage and get its information (production date, parent, and serial number) married to the RFID tag. (7) different head types will need to be handled by this system.
SOLUTION
Multiple Cognex Dataman cameras are oriented in a work cell. As the carriage enters the work cell and stops, an RFID reader gets the part type and passes that to the HMI software, which triggers 1 or 2 cameras. The cameras extract the date, parent, and serial number for a 2D matrix dot pin barcode, and the HMI software writes the data to the RFID tag and an edge database. If the parent number is not valid for the current part type, the error is flagged for an operator to review. In the event of a poor barcode, the operator can enter the information manually.
TAGS
ID Reading, Automotive, Barcode Reader, Data Collection
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.
SOLUTION
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.
TAGS
Identification, Inspection, Pattern Matching, Pharmaceutical, Data Collection, Smart Camera
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.
SOLUTION
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.
TAGS
Identification, ID Reading, Pharmaceutical, Barcode Reader, Data Collection
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.
SOLUTION
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.
TAGS
Identification, OCR, Pattern Matching, Government, Data Collection, PC-based Vision
Machine Vision & Deep Learning Integrator