Nearly a year ago, the world became aware of a new computer virus known as WannaCry. Many institutions were affected by the ransomware. It encrypted and locked a Microsoft Windows computing system and demanded payment.
The next cycle of technology disruption is upon us. Artificial Intelligence (AI) is taking hold in every industry and manufacturing is no exception. AI enables companies—from medical device and electronics manufacturers to pharmaceutical firms—to leverage their Big Data and IoT investments to see new patterns and insights and to perform tasks more efficiently and quickly than ever before.
Companies like ABB, Balluff and Sick would be within their rights to film a commercial with exuberant sensor product managers breaking out in a song of cheer.
CAD/CAM helps auto racers employ CNC machining to maximum advantage.
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.
It’s time to redefine AM and DfAM by what is possible from advanced LPBF systems—and to look ahead with the same determination the semiconductor industry used to better our lives.
The increased use of CT scanning for metal powder bed fusion parts is usually associated with high-value parts and elevated quality requirements. There are increased requests for CT scanning on parts made of engineering-grade polymers like PEEK, PEKK or ULTEM and for fiber-reinforced composites like Nylon 12 CF.
Automakers are turning to Feature-based Product Line Engineering (PLE), which allows organizations to plan, engineer, manufacture, deliver, maintain and evolve product lines much more efficiently.
Colleges and universities are playing a crucial role helping North Carolina address a statewide skilled labor shortage.
The human factor is sometimes just too cumbersome in manufacturing. Take the German chipmaker Infineon: By using an autonomous robot called Scout from MetraLabs for the last several years, the automotive supplier shrank to 10 from 300 the number of minutes it takes to collect the clean-room data needed to measure the presence of rare gases in the air.