automated vision system that works, the company tends to stick with it. Another reason has been the effort,
investment and know-how required in labelling images, training the AI system to recognise passable or faulty products, and customising it to a particular company’s needs. For this, Deats is excited by Cognex’s latest product, In-Sight 3800. “In-Sight 3800 has a new thing called edge learning on it, which is basically a subset of deep learning, but it’s really easy to set up and it’s really powerful,” he explains. “So getting pass and fail results on things that used to take days or weeks to program using traditional vision, you can do with five or six images now. And you can categorise 300 different failure modes on the fly in the production environment, without any fancy licensing, as everything’s done straight on the camera.”
Deeper into production With serious improvements expected from AI, deep learning, as well as more advanced 2D and 3D imaging technologies, where else will machine vision penetrate the pharmaceutical industry? “When you have a large manufacturing
line, every time you change products, five or six individuals go around with a notepad and physically have to check every single pinch point [places on a production line representing a potential hazard to humans] and sign it off to the manufacturing process standard,” Deats says. “Tat costs some companies £1 billion plus a year.” Cognex’s solution, in development with partners, is to position 60 to 70 small cameras around the manufacturing line with a view of anticipated pinch points. Tese cameras produce a series of images from which a deep learning library flags anything abnormal. “With this, one person can actually check if it’s normal or not on an iPad and digitally sign that off and send it to the cloud without any human intervention.”
‘AI-enabled machine vision…will be a key technology in driving the next technological revolution in pharmaceutical manufacture’
Zhu also has specific applications in mind. “Crystallisation is a kind of purification process during drug manufacture,” he outlines. “Before, you had to put everything under a microscope in offline analysis because conventional vision technology finds it very hard to separate crystals that are overlapping, but with AI this becomes feasible.” What all three agree on is that AI-
enabled machine vision, coupled with the Internet of Tings (IoT) and robotics, will be a key technology in driving the next technological revolution in pharmaceutical manufacture, Pharma 4.0. “Powerful vision systems will enable customers to deliver smart production where they become even more efficient, accurate and better compliant with the given regulations and requirements,” says Luu. But it won’t happen overnight. “Systems like these are cost-intensive to develop but also for customers to integrate with their existing infrastructure,” Luu concludes. “It will take some time to get to the point these systems are reliable and qualified in a pharmaceutical manufacturing setting.” O
Machine vision can also be used to detect embedded particles, inclusions or contamination on contact lenses that could present a hazard to the wearer
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