INTEGRATION
and using a novel optics and lighting setup,’ says Jim Reed, vision product manager at LEPS. Tis increases depth of field, which enables the system to cover the full range of depth required to inspect all seats and moulds. ‘Whether the cavity is four, eight or 10 inches deep, we can keep that whole range in focus,’ he adds. Next, the main inspection cameras verify
Leoni Vision Solutions builds and integrates custom inspection systems g
effective tool for implementing all of those Industry 4.0 standardisation measures. But, above all, it is the enabler of our future business fields.’
Integrators remain vital Industry 4.0 might lead to further demand for automation and vision systems, but integrating vision technology for manufacturing has been around long before Industry 4.0, as David Dechow, principal vision systems architect at vision integrator, Integro Technologies, can attest. ‘During the 40 years in which I have been working [with vision], there have been significant advances in machine vision systems,’ he says. ‘I think the growth and the capability of that technology has never before been as good as it is now. It is a fantastic enabling technology for other technologies within automation and what makes it fun is the diversity of what you work with, even the most challenging applications.’ In terms of whether new technology or concepts, such as Industry 4.0, means there’s a shift in the role of the machine vision integrator, Dechow is not so sure. ‘Te machine vision industry has evolved,’ he says, ‘but end users look to developments like Industry 4.0 and smart manufacturing or the industrial internet of things in the same way they have always looked to machine vision integration: to improve their processes and make them more efficient.’ Te more cutting-edge application areas,
Dechow explained, as with all machine vision integration tasks, will still require a detailed process with steps for planning, design, implementation and deployment, but applications such as deep learning in industrial applications, for example, may require a larger team, or different skill sets than those used in standard machine vision. ‘While Industry 4.0 has a lot of buzz
surrounding it,’ Dechow says, ‘with many articles warning that manufacturers who don’t adopt it early enough will fail, we need to make sure that this doesn’t dilute the end user’s understanding about what machine vision can do. Tey [end users] have always turned to machine vision integrators to improve their processes. It has always been our role to make the system work, even in more complex applications, using the most successful machine vision solution for their particular requirements.’
Seat of power Likewise, Leoni Vision Solutions, part of Leoni Engineering Products and Services (LEPS), emphasises the important role that vision integrators play. Te company has an in-house machine vision development laboratory to run feasibility studies, and ultimately create a custom solution. One of the inspection systems Leoni has
built is for checking seat mould assembly. Tis is a challenging vision task, where the system is used to verify components that have been encapsulated into each foam piece. Te increasingly complex design and large variety of seat moulds requires an inspection system capable of adapting without interrupting the run. Te inspection process begins right after
the operators finish placing components into the cavity. One of the inspection system’s cameras captures images of predetermined points in the cavity where most variation occurs. Te system uses a dedicated camera to measure the intensity of the mould, so that the main inspection cameras can be offset to create maximum contrast between the components and the mould itself. ‘It additionally compensates for different
seat geometries and mould depths by strobing an extraordinary amount of light
34 IMAGING AND MACHINE VISION EUROPE AUGUST/SEPTEMBER 2021
that correct components are placed in the proper locations in the seat cavity. Once the system software confirms the presence of the components, it issues a pass or fail determination. When a mould passes, the system sends a signal to the programmable logic controller. Te mould then travels to the robot pour station, where it’s filled with foam, the lids close and the feed is completed. If the system detects missing components, it alerts the controller to not pour that seat mould. Deep learning can be incorporated for
instances when moulds have handwriting or machining marks, or if they look different from one another, even if the machine is producing the same part. Previously, each mould had to be programmed individually,
‘While deep learning isn’t a magic bullet in industrial inspection, it can offer value’
but by incorporating deep learning into the process, operators can program a single part number rather than every seat cavity in- house, saving a lot of time in the process.
Deep impact While deep learning isn’t a magic bullet in industrial inspection, it can offer value, especially when features to be detected require subjective decisions like those that might be made by a human. ‘Tese systems are incredibly complicated from a vision standpoint, because of the sheer volume and variation between part numbers and models, as well as the programming and changes,’ says Reed. ‘One mould may have 30 inspections being performed on it, so if you take those inspections and multiply it by 200 part styles for 1,500 different models, you can quickly see how this becomes quite complex.’ One of the potential advantages of using
deep learning to accommodate more variation in the part is there’s less time spent programming and maintaining the system. ‘Tese and many other advanced vision techniques,’ says Reed, ‘provide a wide range of integrated solutions for many potentially challenging applications, beyond just the mould insert verification and inspection.’ O
@imveurope |
www.imveurope.com
Leoni Vision Solutions
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