Update

New machine interface built for EOS 3D printers

18/08/2020

With the integration into an EOS 3D printing machine at Ariane Group in Taufkirchen, nebumind’s software can now be connected to EOS machines easily and collect and visualize data as „digital product twins“ during manufacture.

With the integration into an EOS 3D printing machine at Ariane Group in Taufkirchen, nebumind’s software can now be connected to EOS machines easily and collect and visualize data as „digital product twins“ during manufacture.

 

In this first step, our software collects machine data via the OPC-UA interface of EOSCONNECT CORE to indicate, if all layers are processed with the same machine parameters. Through our offline import, we can combine this data with OT (optical tomography) export data. This way, operators can analyze correlations between set machine parameters and actual results. Our next step will be to also integrate meltpool data.

 

With the new interface to EOS machines, we are entering the metal 3D printing market, which has grown considerably over the last decade and was valued at USD 772.1 million in 2019 with an expected compound annual growth rate (CAGR) of 27.8% from 2020 to 2027 (Industry Report, June 2020, 3D Printing Metal Market Size, Share). The key factors driving the interest in metal 3D printing are its greater design flexibility, low waste, and cost effectiveness in the overall manufacturing landscape.

 

The nebumind software can create a considerable added value already in the early years of this new manufacturing method. It helps users to identify quality drivers of the manufacturing process, such as speed, temperature and humidity. As all manufacturing data is visualized simultaneously as “digital product twins” of the manufactured component, anomalies can be analysed – fast and intuitively.

 

One interesting application, we are currently exploiting: matching manufacturing data with CT (computer tomography) data recorded during final quality controls. By overlaying the different data sets, anomalies and quality defects within the produced component can be identified even quicker and prevented in the future – creating a steeper learning curve.