How AI is helping re-imagine the role of industrial vision

There is tremendous interest in how AI is changing industrial vision.  Our track on big data and artificial intelligence for industrial vision was fully booked at the High Tech Summit in Copenhagen, Denmark.  AI is leading to a paradigm shift in how quality monitoring and optimization is done today.

Many industrial vision companies still rely on software developers for their computer vision algorithms but they are slowly learning that AI can do the same things faster and more robust.  Many applications within food and pharma have been too hard to solve with traditional computer vision because of large product variations.  Now it is however possible to solve them just by training from examples.  This is also giving rise to new applications – we demonstrated our work together with Teknologisk Institut in slaughterhouse cobots that can adapt to and cut meat based on individual variation in animals (Augmented Cellular Meat Production – ACMP).   Although still slower than a human, the ability to parallelize the process in cell-based production will ultimately allow such systems to increase throughput while at the same time increasing redundancy and thus lower risks of down-times.

Applications within industrial vision are no longer restricted to individual machines and can effectively through AI-based digital twins optimize entire production lines, leading to higher quality products and increased yield.   By combining production level insights with industrial cameras it is possible to train complete systems that learn what to look for in images based on Key Performance Indicators.   We saw massive interest from food and pharma-based applications for optimizing fermentation tanks and other complex bio-related multi-step processes.  Understanding causes and effects in such processes is very complex for humans and traditional process models, but much easier for AI-based methods.  Also, why stop there?  The approach scales easily to supply chains – as long as we have access to the data then you no longer have to worry about modelling individual sensors and processes which previously was a show-stopper for your digitalization strategy.




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