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Lasers — well-established tools in the manufacture of medical devices—are continuing to break ground by producing smaller, more precise and more functional parts thanks to faster pulse speeds at lower cost, new applications and the marriage of laser processing to Swiss-style machining.

In the last seven to eight years, solid-state lasers have come to dominate laser welding and cutting,” said Tom Bailey, product specialist for Trumpf Inc. (Farmington, CT). While Trumpf still produces CO2 lasers, for most applications solid-state lasers literally outshine them.

With user costs rising and automation still not at the optimum level in China, the best option for pulling small and medium enterprises along on the smart manufacturing journey is cloud technology, Huawei Technologies’ He Fred said here today at a conference titled Manufacturing in the Age of Experience.

Technological gains, young ambassadors, apprenticeships help. But manufacturers’ training investment remains a problem.

The road to manufacturing success today runs through the mountain of data that tools are generating in metalcutting applications, and most importantly communicating and reacting to in real time on the shop floor.

To assist manufacturers on their Industry 4.0 journey, global engineering technologies company Renishaw has partnered with the Connecticut Center for Advanced Technology. Renishaw provided the center with industrial metrology and additive manufacturing equipment to help companies adopt new manufacturing strategies and accelerate implementation of new digital technologies.

Sandvik will acquire DWFritz Automation, a U.S.-based provider of precision metrology, inspection and assembly solutions for advanced manufacturing. DWFritz designs, builds and supports high-speed, non-contact metrology solutions and automation systems.

HoloLens 2, Microsoft’s second-generation, mixed-reality smart glasses is a big part of why augmented reality in assembly has become ready for prime time.

Because its president saw opportunities to improve efficiency and an immediate need to make up for capacity lost due to impending worker retirements, Daiwa Steel Tube is set to save more than $1 million a year.

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.