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How data science intersects with machine engineering for smarter and more valuable packaging operations.
Spend a little time investigating the impact of artificial intelligence (AI) on packaging equipment and processing lines and it quickly becomes apparent the improvements AI holds for higher efficiencies, better quality and improved safety are just emerging.
AI covers a number of engineering-related goals: better predictive maintenance, the concept of zero downtime, clear traceability related to standards compliance and increased worker engagement. Indeed, “smart” devices—such as motors, controllers, sensors and more imparting improved algorithms to robots, conveyors, check weighers and entire packaging lines—promise to engage workers rather than replace them. “Automate tasks, not replace workers” is the mantra.
“We still remain far from general AI that can wholly take over complex tasks, but we have now entered the realm of AI-augmented work and decision science—what we call ‘augmented intelligence,'” Chris Howard, distinguished research VP at research organization Gartner, says. “If you are a CIO and your organization doesn’t use AI, chances are high that your competitors do and this should be a concern.”
So what is AI and what is its impact on packaging? Says Chuck Lewin, president of Performance Motion Devices (Westford, MA): “AI is a set of data-processing techniques that go beyond traditional algorithms connecting cause and effect, observation and action, to a higher level of processing emphasizing pattern recognition, adaptive control and prediction.
“In the context of packaging equipment, AI operates in three steps,” he continues. “It inputs data from sensors, analyzes that data and then alters its own operation based on the results of that analysis. The key is collecting a lot of data, even of variables that may not be directly related to the process being controlled.
“As applied to packaging equipment, AI will improve system performance in several key areas,” he says. “Lines will handle a wider range of incoming materials, they will provide more precise inspection of the materials they handle and they will monitor their own behavior so that required maintenance can be predicted.”
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