With the NextGenAM project, in collaboration with Premium AEROTEC and Daimler, EOS has implemented a pilot process chain tailored to industrial series production. Every process is automated, from conveyance of the metal powder to post-processing after the build process. The highlight is products for different customers can be manufactured in parallel. Unlike classical production lines, which process projects one by one, there is no need for production to unfold sequentially.
The entire production chain is self-controlled without an operator from an autonomous centralized control center. To achieve this, all of the machines are connected on a network. The order data are transferred to the control center, which determines the priority of each build job and assigns it to a 3D printer.
The production system is not closed in any direction – there are automatable interfaces on every side. This means that production can be integrated into the manufacturer’s higher-level systems (MES, ERP, SAP), enabling automated order processing and integration with design data or connections to other manufacturing processes and stations. Additional post-processing steps can be integrated, depending on the parts and the customer requirements.
The EOS control center also provides a foundation for self-optimizing production based on digital twins. The underlying idea is to allow the digitally networked machines to apply intelligent algorithms to create a self-optimizing network, thus digital intelligence is entering the production chain.
What does this mean?
High-performance systems record all data from production until the finished parts and visualize them in a database in real time. Each created part is represented in the database by a digital twin that contains all relevant data about its parameters (location, ambient conditions, material, machine settings, process parameters, costs, and more). For example, after ten weeks of production, you’ll be able to compare various parameters across 100,000 digital twins and experiment with simulations to identify any potential for improvements. The system reproduces the specific impact of specific changes on the results, revealing the best path for self-optimization in the eleventh week.
A final but essential step is the digital consolidation of design, manufacturing and lifecycle know-how. By equipping parts with sensors that record the relevant information during actual usage, it’s possible to create a sort of “lifecycle diary.” Even though parts and products can be tested before delivery, there’s no way to simulate knowledge of real-life conditions.