When considering a new robotic automation system, one of the biggest concerns can be the weight of the initial costs. While such a large capital expense may be hard to swallow at first, it’s industry-proven that manufacturers see an average ROI of 24 months from robots.
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Alex Berry and his team at Sutrue Ltd. (Colchester, England) exploited the benefits of 3D printing prototypes when developing two new automated suturing devices. They also coined a phrase to describe their prototyping technique.
Automotive manufacturers and their Tier One suppliers spend endless engineering hours developing the PAB (passenger airbag) system, from the airbag and its propellant to the construction of the materials used in the composite instrument panel.
The requirements for FDA 21 CFR Part 11 are in place for a good reason: When companies are making a part that goes inside your body, the engineering and manufacturing process must be meticulously documented, tested and controlled. People’s lives are at stake.
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.
Some in the medical industry are using silicone rubber molds made with a 3D-printed master pattern for low-to-mid production runs of cast polyurethane device housings.
Analytics solutions. The industrial Internet of Things. Robotics. Automation. Manufacturers looking for tech solutions that will help them control costs and gain a competitive edge have many great options. In fact, deciding what type of technology to invest in and why can seem overwhelming.
From Copper to Filaments, engineers are developing new materials for 3D printing, advancing its practical use. In February, Markforged, Watertown, Mass., commercialized a pure copper filament for its printers so they can use this hard-to-machine metal.
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.
Digitization of industry has become an established global trend. Despite all the enthusiasm of visionaries, the machine tool is, was and will remain the core element in production.