[penci_blockquote style=”style-1″ align=”none” author=””]Partnership enables direct machine control and end-to-end workflow automation for additive manufacturing[/penci_blockquote]
Above: Workflow software for Additive Manufacturing/Image Credit: AMFG
EOS, a leading technology supplier in the field of industrial 3D printing of metals and polymers, and AMFG, leading provider of MES software for additive manufacturing, have announced a partnership enabling direct streaming to and connectivity with EOS machines.
Making machine control a reality
The lack of connectivity across the additive manufacturing workflow remains one of the biggest barriers to adoption. Through their partnership, EOS and AMFG aim to remove this barrier and establish a fully connected and automated additive manufacturing workflow.
Customers of EOS machines will be able to manage their entire additive manufacturing operations with AMFG’s Manufacturing Execution System (MES), whilst simultaneously connecting directly with their machines through the software platform. “EOS has always been at the forefront of technological developments in additive manufacturing,” stated Mirco Schöpf, Product Line Manager Connectivity & Automation Software at EOS.
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“We strive to work with partners who share our vision of innovation for additive manufacturing. Our collaboration with AMFG will help our customers continue to unlock the full benefits of our technologies.”
Keyvan Karimi, CEO of AMFG, said: “EOS is a global leader when it comes to 3D printing technologies and we’re thrilled to collaborate with such an esteemed partner. Our partnership not only makes machine control a reality, but it is also a major step towards establishing AM operations that are automated and scalable.”
Creating an end-to-end digital manufacturing workflow
Through AMFG’s MES software, EOS customers can also prepare build jobs and set important parameters like part orientation. Builds can then be sliced and the data sent directly to an EOS 3D printing system. The ability to monitor and receive machine data in real time is also in the works.
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