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The rise of cloud-based, Industry 4.0-level computing and networking technology is making multiple documentation and traceability requirements easier to navigate.

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.

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.

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

It is not surprising that the aerospace and defense industry exists at a higher plane of manufacturing. The components and end products being assembled must endure intense forces and pressures, are expected to perform without failure, and even the slightest mistake comes with extreme safety risks.

Three partners contributed their diverse manufacturing and machining experience—and their last initials—to found SPR Machine in 2002.

Aerospace OEMs and their supply chains are evolving plans to manage the economic impact caused by the health-related shutdown last spring.

The U.S. Department of Defense (DoD) wants the industry to secure the country’s leadership in hypersonic weaponry. The request is no small feat.