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Hexagon experts embark on a summer mission to make manufacturing smarter for users across the U.S.

An engine manufacturer discovers there is a way to reduce 50 billion data points to 2 billion—a reasonable number from which the foundation for machine learning can be built.

In a high-mix/low-volume environment, it’s not good enough to simply be part of the pack. Today you need to be out front and pulling away, powered by the best smart tech available.

A shared testbed for innovation at NIIMBL represents, but doesn’t mimic, what’s happening in an individual firm’s lab.

Five experts outline ways companies can help existing racially diverse employees interested in management posts, actions those workers can take, ways recruiters can do better, and ways to prepare for the future.

When Rensselaer Polytechnic Institute needed clear plastic to make face shields for the coronavirus pandemic, conventional suppliers were unable to provide it. “We are now one of the largest purchasers of document covers in the country,” Robert Hull, acting VP for research at Rensselaer, said during a  webinar that SME and CESMII – the Smart Manufacturing Institute hosted last week.

This is the first in a series of articles that will cover the accelerating improvement in manufacturing technology.

Designing new aerospace engines is challenging, but Rolls-Royce Germany turned to data analytics and AI to meet these challenges. The results? Reduced costs and shortened lead times.

At a Tier 1 automotive manufacturer in Mexico, it quickly became clear that AI in the factory was a fantastic solution to help human workers achieve greater levels of success; a human plus machine scenario where AI enhances the capabilities of, rather than replaces, human workers.

AI already helps individual factories improve production, safety, efficiency and other metrics while lowering costs. Marrying AI and cloud technology can supercharge those benefits.