Self-contained system inspects unlabelled cans
Published: 9 February 2012 - Heather Ramsden
Producers of canned foods typically make a large volume of a particular product, such as tomato soup, then store the cans in a warehouse without labels while waiting for orders from customers. The cans are labelled just before shipment, often with the customer’s private brand label. The cans go by at a speed of one every 60 milliseconds so conventional manual inspection is not possible. The only known effort at applying machine vision to this problem used a camera connected to a frame grabber board on a computer. Its weakness is that the specialised hardware is not designed for use in a factory environment. The cameras and frame grabber boards are susceptible to heat and dust. A considerable level of expertise is also required to set up and maintain this type of system, expertise that is typically not found in a canning plant.
Matrix Technologies utilised recent advances in vision system technology to develop a better approach to brightfield automated inspection. “The key to the new approach is the use of the Cognex In-Sight® 5600 vision system to inspect the product codes against the bright can background at a speed of 1,000 products per minute,” says Les Haman, department manager for Matrix Technologies. The Cognex PatMax® pattern matching tool inspects the product code.
This application takes advantage of the ability of the PatMax tool to recognise a pattern regardless of its location. Rather than reading individual characters the application is configured to simply look for an image that matches the three-digit product code. A new product code can be configured simply by putting a can with the new code in position to be viewed by the vision system and positioning a rectangular box around the product code. From that point, the vision system will detect that product code even if it is in a different position or at a different angle as long as it is in the field of view. This approach is much simpler, more robust and more economical than the machine vision technology used on this application in the past.