Additive Manufacturing offers a high level of freedom in design. Higher functionality can be achieved through more complex shapes that are only feasible with 3D printing. To achieve the desired surface quality and dimensional accuracy, it is necessary to post-machine the components. In the case of 3D printed silicon carbide (SiC) ceramic components, machining becomes extremely challenging not only due to its high hardness and wear resistance but also due to its complex shape. The research project ProDenker investigates the resource-efficient value chain of 3D printed silicon carbide (SiC) ceramic components. Each process step is set up as a decentralized physical demonstrator, from the development and manufacturing of thermoplastic feedstocks, to shaping preforms using the extrusion-based 3D printing technology Pellet Additive Manufacturing (PAM) and the subsequent siliconizing, to the final machining step. The SiC-based ceramics under investigation offer great potential for increasing process efficiency and reducing CO2 emissions when introduced into new areas of application in the automotive, energy and chemical sectors by increasing process temperatures while maintaining high specific strength.

However, the demanding material requires new machining strategies and tooling concepts. The mechanical load on the tools during machining leads to high abrasive wear. Polycrystalline diamond tools with geometrically defined cutting edges are used to meet these challenges. A further improvement is expected through the development and use of an environmentally friendly and component-compatible lubricant to increase material removal rates and tool life in the machining process. In addition, a centralized data platform is established to monitor and map the processes. The goal of digitalization is to identify anomalies along the process chain. To this end, production data will be systematically collected at any process step and made available on the data platform for higher-level analysis. Artificial intelligence will be used to detect critical deviations at an early stage, saving resources and avoiding expensive reworking of components.