Ein datengetriebenes Verfahren zur robusten und zuverlässigen Auslegung des Niederdruck-Kokillen-Gießprozesses

Ensuring product quality while maintaining high productivity and energy efficiency are key aspects and therefore levers for optimization measures in companies. In many sectors, including light metal foundries, data-driven methods based on machine learning are already being used in the context of Industry 4.0 to continuously improve process efficiency. Many studies are focused on analyzing process data during serial production. A holistic, data-based strategy for determining a robust and reliable process design that considers any process fluctuations that occur has not yet been developed for low pressure die casting.
This paper therefore presents a novel, automated method for determining a robust and reliable operating point during the development phase of the low pressure die casting process. The focus is on the process parameters for a given casting and mold design but can easily be extended to include design parameters. Within the framework of design of experiments, the procedure includes the needs-based adjustment of the number of experiments as well as compliance with input and output restrictions, the modelling of both static and dynamic system behavior and, within the multi-criteria optimization, the consideration of both uncertainties in the process parameters and uncertainties due to limited metrological accessibility. A selection mechanism, which supports the user in the rapid analysis of robust and reliable Pareto-optimal solutions, rounds off the procedure.
The performance is demonstrated using a real low pressure die casting process at MARTINREA HONSEL GERMANY GMBH. The results show that the approach enables a robust and reliable casting process design with comparatively low energy input, considering the boundary conditions of short cycle times.