TIWARI and ESA validate ceramic and metal materials for RAPTOR 3D printer 3D Printing Processes

by Technologies

ESA-supported startup TIWARI Scientific Instruments in Germany has developed low-cost 3D printer, the RAPTOR, which is capable of 3D printing a variety of metals and ceramics using a bound powder filament extrusion process. While the 3D printer is similar to other systems now available in the market – such as those introduced by Nanoe and Triditive among others – TIWARI was able to leverage support from ESA to validate four metal and ceramic materials for space and other applications.

GrabCAD update by Stratasys speeds up design-to-3D print process AM Software

GrabCAD Update by Stratasys Speeds Up Design-to-3D Print Process

by Design

Stratasys is aiming to simplify the design-to-3D print process for designers and engineers by eliminating the CAD-to-STL conversion roadblock in its GrabCAD Print software platform. The 3D printing giant says the software’s new Advanced FDM feature will enable users to streamline design-to-print workflows, while simultaneously improving production times and material usage, in an intuitive way.

The Shop System is here, is the AM 2.0 revolution really about to start? 3D Printer Hardware

The Shop System is here, is the AM 2.0 revolution really about to start? 3D Printer Hardware

by Technologies

With yesterday’s day-long online seminar on what the company’s founders have come to define as Additive Manufacturing 2.0, Dekstop Metal officialized the commercialization of its Shop System with clients in North America, EMEA and APAC. Does this mean the much-awaited revolution of metal binder jetting for production is finally here? Probably. Let’s look at the facts.

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Senvol machine learning to be used for missile application with the U.S. Army AM Software

by Design

Senvol was awarded a United States Army Research Laboratory (ARL) contract to apply its machine learning software, Senvol ML, to rapidly design additive manufactured parts. The software allows the Army to qualify parts across AM processes and platforms, thus reducing the Army’s supply chain lead time. By leveraging ML algorithms, the qualification plan will also be notably more efficient than more traditional qualification plans (i.e. require fewer builds and less time).