passes the inspection module, six individual photos of the product are taken automatically. That imagery data is sent to a PC for analysis to identify and read the product’s DMC. Not disclosing the contract value, a SV360 vision module was employed at a pharmaceutical company to read markings on bottles. In this case its PC was provided by embedded computer solutions specialist Kontron. As a result the SV360 can cope with a throughput of 400 containers, including bottles, per minute. However, as many cameras as this are not
always necessary. Markings can be read when they are almost perpendicular to the camera, as Jordan explains: ‘They can read the code just shy of the vertical; they can handle that level of perspective distortion.’ He adds: ‘[Better] software has allowed a big leap in readability.’ Reading more than one marking at a time is
also possible with the double-digit Megapixel cameras. They deliver greater image accuracy within the same field of view and an ability to cope with greater depths of field.
Liquid lenses get exposure Depth of field can present a technical challenge for rapid production lines. The solution has been to provide cameras with a liquid lens. This liquid lens consists of water and oil, and with the application of current it can change its focal length. Microscan recently introduced its own product that incorporates a liquid lens, called AutoVision. As well as a liquid lens for auto focus, it also has aperture control to allow changes in exposure for when more light is needed. Higher light levels can be needed when products are travelling along the conveyer faster and a fast exposure is aided by more light or where complex small codes or human readable markings require a greater contrast for the camera to distinguish between their elements. Stemmer Imaging’s group manager Chris Pitt
explains that liquid lenses are necessary when simple factors such as different-sized boxes on a conveyor and the inconsistent location of the barcode on the box can mean a lot of variation in depth of field. ‘While you can get cameras with auto focus, they are slower and will limit the speed of the products [on the conveyor]’, says Pitt. The wider field of view, possible with many- Megapixel cameras, means entire objects can be imaged for code detection. Microscan’s Ludlow explains that with higher resolutions, small codes can be found easily when searching for them over a wide area. For example, a motherboard can be glanced at and the 1 or
2D code can be found. Another example he gives is a tyre, where, when rotated, the entire tyre can be viewed at once. Siemens has readers that can read 1D and
2D codes as well as human readable markings. They achieve this with optical character recognition. Optical character recognition (OCR) describes the capability to identify human-readable characters, most often the Roman alphabet. Siemens explains that OCR is more difficult for electronic systems to read than 1D or DMC markings. Letters such as O or C can be confused where markings are degraded or become obscured. Siemens created its Text Genius algorithm to enable the image interpretation to cope with these instances. According to Stemmer’s Pitt, OCR can be read at high speed, but it requires software development and the machine vision system ‘has to be taught to recognise’ symbols and letters that can be very close together. Whatever the type of object or code, lighting
can be a key factor if the throughput is high. High throughput can be hundreds of metres of conveyer per minute, for example. A conveyer running at 70 metres per minute can see more than 300 products whizz past a reader in 60 seconds – five products per second. As such,
‘Traceability will not work if tracking is impaired by a marking that cannot be read accurately’
high light levels are needed to allow the camera to capture the image for the code data with a very short exposure time. Vision integrators face many challenges
when retrofitting any kind of vision system, and code reading systems are no different. Robert Pounder, Olmec UK technical director, says that many production lines were designed long before machine vision became an option. ‘We have to design the reader station around the production line,’ he says. ‘But even with these challenges, we can still retrofit a code reader station to a conveyor line running at 300 or even 600 metres per minute.’ Regardless of the type of code, traceability
will not work if tracking is impaired by a marking that cannot be read accurately. While data-peening may become a niche where active systems can hold an advantage, the overwhelming majority of printed markings will be tracked using passive vision systems.
3D imaging camera
see the difference. light
multi-spectral imaging
Hall 6 / B18
Chromasens GmbH | Max-Stromeyer-Str. 116 D-78467 Konstanz | Phone: +49 7531 876-0
info@chromasens.de |
www.chromasens.de
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