imaging and machine vision europe october/november 2010
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applications: QA Quality in quantity
Quality assurance inspections are the bread and butter for machine vision. Greg Blackman looks at some classic QA applications using vision
The choices consumers make in the products they buy are reliant on numerous variables. In a competitive marketplace, businesses will invest a lot of time and money in ways of making their product stand out from the rest of the competition. One of the big variables, however, and one which will hopefully keep the customer coming back for more, is quality. Quality assurance checks are employed in the
vast majority of manufacturing procedures, from food to consumer electronics. Machine vision is built for these types of quality inspections and can be used to ensure part tolerances are met, along with any aesthetic criteria the manufacturer might have with regards to the product. QA forms an important part of labelling, and
for foodstuffs this is especially crucial – not just for presenting attractive packaging that the customer is going to want to buy, but also for displaying the correct information about the product, such as allergen warnings. Tom McLean, general manager at vision system
provider Mettler Toledo CI-Vision, comments: ‘A high percentage of product recalls are actually label-oriented, not content-oriented.’ Mike
Wine labels are subjected to numerous quality checks including damage, placement on the bottle, and whether the label matches the type of wine. Image courtesy of Mettler Toledo CI-Vision
Geren, engineering manager at the company, adds: ‘Some of it is lawsuit based. Mistakes made on labels expose the manufacturer to significant lawsuits, especially when it’s labelling pharmaceuticals or over-the-counter drugs.’ Mettler Toledo CI-Vision installs vision systems
for various industries. One of its customers is the Kendall-Jackson winery in California, which uses machine vision at various stages along its production line, including the 360 Full View inspection system designed for labelling checks. As a bottle passes through the label inspection area, four Basler Scout cameras provide a 360° view of the bottle. Image processing is carried out by the Matrox Imaging Library (MIL). Firstly, every bottle is inspected to ensure
Injection moulded engine manifolds are checked to ensure bolt holes are positioned correctly. Automotive suppliers need to guarantee 100 per cent quality of the parts produced. Image courtesy of Industrial Vision Systems
the barcode or label ID matches the product. Labels are then inspected to make sure they’re not crooked, wrinkled or flagged (i.e. peeling edges); the front and back labels must be equidistant and top and bottom labels must be lined up. Within the MIL platform, the label is ‘unwrapped’ to convert it to 2D and the software builds a composite image of the bottle. From that, the labels’ relative positions can be verified
according to their coordinates. ‘The most important aspect of a vision setup is
lighting. If you can light it then you can inspect it,’ says McLean. However, Geren notes there are some defects that you can’t physically light to pull out the contrast, which is where Matrox’s vision library is used, to bring out the contrast and enhance the defects. A black product ID code on a dark label is one example where the software has to hone the image to find the proper contrast. This kind of processing, if carried out on the fly at 800 products per minute, is resource intensive. ‘MIL provides the tools, but we use our experience to determine which algorithms to use where, so that defective products don’t go by our systems,’ says Geren. ‘There’s a lot of quality control that goes
in,’ comments McLean. ‘There might also be seasonal wines or specials that have to have the correct label. It’s those labelling checks that really guarantee the quality. In general, wineries will inspect every element of the label to the extent that any flaws shouldn’t be noticeable by the human eye when the bottle is on the shelf. ‘If you’re a manufacturer and you’ve fought
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