Multi-Material-Injector-Casting (MMIC)

• Applicants: Professor Dr.-Ing. Stefan Braunreuther; Professor Dr.-Ing. Wolfram Volk
• Subject Area: Primary Shaping and Reshaping Technology, Additive Manufacturing
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 501941276

The aim of the research project is the reproducible production of a component with tailored properties in a multi-material injector casting (MMIC) process. The interaction of the two melts allows free adjustment of the characteristics of the boundary layer between the materials. This makes it possible to provide the right material at the right place in the component to ensure material properties that are optimally adapted to the requirements. An example of this would be a combination of wear resistance in the working area and mechanical strength in the area of the connection points.In particular, the mixing of the melts due to density differences and turbulence poses a challenge in the research project and explicitly requires modeling. For this purpose, extensive simulations are carried out in order to be able to estimate all relevant cause-effect chains. In order to validate the simulation results, a test rig for water model experiments will be set up. Here, a stereo camera system enables the reconstruction of flow conditions by means of tracer particles. These results are incorporated into the design of both the casting process and the control system. The connection of the casting process and the casting system with the control system is based on the principles of cyber-physical systems in order to be able to guarantee a possible development of a real-time capable control system in a follow-up project. The sensor and actuator technology is selected in such a way that the identification of cause-effect chains and of non-measurable process variables is possible by means of a soft sensor based on the real data. The calibration of the thermo-stereo camera system includes different distortion parameters as well as the prediction of the behavior of tracer particles in the flows e.g. by applying a Kalman filter.By matching the predicted reference variable with the gradient that occurred in the component, by correlations and patterns in the simulation data, the behavior of the parameters during the casting process and via identified cause-effect chains of the influencing parameters, the control as well as the valve system is validated. Finally, a possible development of an automated control system, e.g. via a soft sensor, will be investigated.

• DFG Programme: Research Grants